From 8389755aea6029e4b73250c23695d1cbaefc55a6 Mon Sep 17 00:00:00 2001 From: Sandeep Kunkunuru Date: Thu, 23 Apr 2026 09:41:55 +0530 Subject: [PATCH] chore: remove stale frontend, point users to samyama-insight The frontend/ CRA app (last touched 2025-04-01) is abandoned and superseded by the new Optimization section in samyama-insight, which runs against the /optimize/* HTTP endpoints exposed by samyama-graph v1.1.0+. - Remove frontend/ entirely (deployed gh-pages branch left intact for now; full decommission of optimizations.aisdlc.com tracked separately) - Remove /frontend/ block from .github/CODEOWNERS - Update README.md and docs/index.md "Interactive Visualization" to point to samyama-insight instead of the dead vaidhyamegha.github.io URL --- .github/CODEOWNERS | 3 - README.md | 7 +- docs/index.md | 5 +- frontend/.gitignore | 1 - frontend/README.md | 118 - frontend/package-lock.json | 22129 ---------------- frontend/package.json | 55 - frontend/public/CNAME | 1 - frontend/public/index.html | 20 - frontend/public/manifest.json | 25 - frontend/src/App.css | 189 - frontend/src/App.js | 32 - frontend/src/components/AlgorithmSimulator.js | 433 - frontend/src/components/Footer.js | 25 - frontend/src/components/Header.js | 34 - .../src/components/VisualizationComponent.js | 613 - frontend/src/index.css | 156 - frontend/src/index.js | 12 - frontend/src/pages/AboutPage.js | 219 - frontend/src/pages/AlgorithmPage.js | 356 - frontend/src/pages/ComparisonPage.js | 287 - frontend/src/pages/HomePage.js | 87 - frontend/src/pages/PapersPage.js | 139 - frontend/src/utils/algorithmData.js | 394 - frontend/src/utils/objectiveFunctions.js | 233 - 25 files changed, 8 insertions(+), 25565 deletions(-) delete mode 100644 frontend/.gitignore delete mode 100644 frontend/README.md delete mode 100644 frontend/package-lock.json delete mode 100644 frontend/package.json delete mode 100644 frontend/public/CNAME delete mode 100644 frontend/public/index.html delete mode 100644 frontend/public/manifest.json delete mode 100644 frontend/src/App.css delete mode 100644 frontend/src/App.js delete mode 100644 frontend/src/components/AlgorithmSimulator.js delete mode 100644 frontend/src/components/Footer.js delete mode 100644 frontend/src/components/Header.js delete mode 100644 frontend/src/components/VisualizationComponent.js delete mode 100644 frontend/src/index.css delete mode 100644 frontend/src/index.js delete mode 100644 frontend/src/pages/AboutPage.js delete mode 100644 frontend/src/pages/AlgorithmPage.js delete mode 100644 frontend/src/pages/ComparisonPage.js delete mode 100644 frontend/src/pages/HomePage.js delete mode 100644 frontend/src/pages/PapersPage.js delete mode 100644 frontend/src/utils/algorithmData.js delete mode 100644 frontend/src/utils/objectiveFunctions.js diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS index eb9099a..ddf0d67 100644 --- a/.github/CODEOWNERS +++ b/.github/CODEOWNERS @@ -1,9 +1,6 @@ # Default owners for everything in the repo * @MandarapuMadhulatha @sandeepkunkunuru @vinod-dega -# Frontend code ownership -/frontend/ @MandarapuMadhulatha @sandeepkunkunuru @vinod-dega - # Python implementation ownership *.py @MandarapuMadhulatha @sandeepkunkunuru @vinod-dega diff --git a/README.md b/README.md index bdeb562..30f481e 100644 --- a/README.md +++ b/README.md @@ -20,8 +20,11 @@ These algorithms are designed to solve both **constrained** and **unconstrained* ## Interactive Visualization -Explore these algorithms through our interactive web application: -**[Rao Optimization Algorithms Visualizer](https://vaidhyamegha.github.io/optimization_algorithms/)** +For an interactive web UI to run and compare these solvers (live convergence +charts, Pareto fronts, paper references), use the **Optimization** section in +[Samyama Insight](https://github.com/VaidhyaMegha/samyama-insight) — it talks +to the `/optimize/*` HTTP endpoints exposed by `samyama-graph` v1.1.0+ and +covers the full Rao algorithm family plus baselines (PSO, DE, GA, NSGA-II). ## Features diff --git a/docs/index.md b/docs/index.md index 414a609..d5f26b7 100644 --- a/docs/index.md +++ b/docs/index.md @@ -4,8 +4,9 @@ Welcome to the comprehensive documentation for the BMR and BWR optimization algo ## Interactive Visualization -Explore these algorithms through our interactive web application: -**[Rao Optimization Algorithms Visualizer](https://vaidhyamegha.github.io/optimization_algorithms/)** +For an interactive web UI to run and compare these solvers, use the +**Optimization** section in [Samyama Insight](https://github.com/VaidhyaMegha/samyama-insight), +which talks to the `/optimize/*` endpoints on `samyama-graph` v1.1.0+. ## Contents diff --git a/frontend/.gitignore b/frontend/.gitignore deleted file mode 100644 index c2658d7..0000000 --- a/frontend/.gitignore +++ /dev/null @@ -1 +0,0 @@ -node_modules/ diff --git a/frontend/README.md b/frontend/README.md deleted file mode 100644 index 336c8cb..0000000 --- a/frontend/README.md +++ /dev/null @@ -1,118 +0,0 @@ -# Rao Optimization Algorithms Visualizer - -An interactive web application for visualizing and exploring optimization algorithms developed by Prof. R.V. Rao. - -## Live Demo - -**[Visit the live application](https://vaidhyamegha.github.io/optimization_algorithms/)** - -## Overview - -This frontend application provides a comprehensive visualization platform for understanding and comparing various optimization algorithms: - -- **BMR (Best-Mean-Random) Algorithm** -- **BWR (Best-Worst-Random) Algorithm** -- **Jaya Algorithm** -- **Rao Algorithms (Rao-1, Rao-2, Rao-3)** -- **TLBO (Teaching-Learning-Based Optimization) Algorithm** -- **QOJAYA (Quasi-Oppositional Jaya) Algorithm** -- **GOTLBO (Generalized Oppositional TLBO) Algorithm** -- **ITLBO (Improved TLBO) Algorithm** -- **Multi-objective TLBO Algorithm** - -## Features - -- **Algorithm Exploration**: Detailed information about each algorithm, including pseudocode, paper references, and applications -- **Interactive Visualizations**: 2D contour plots, 3D surface plots, and optimization path visualizations -- **Algorithm Comparison**: Compare multiple algorithms on the same objective function -- **Step-by-Step Simulation**: Educational walkthrough of how each algorithm works -- **Research Papers**: Access to original research papers and proper citations - -## Project Structure - -``` -frontend/ -├── public/ # Public assets -├── src/ -│ ├── components/ # Reusable UI components -│ ├── pages/ # Main application pages -│ ├── utils/ # Utility functions and data -│ ├── algorithms/ # Algorithm implementations -│ ├── assets/ # Static assets (images, etc.) -│ ├── App.js # Main application component -│ └── index.js # Application entry point -└── package.json # Project dependencies -``` - -## Getting Started - -### Prerequisites - -- Node.js (v14 or later) -- npm or yarn - -### Installation - -```bash -# Clone the repository -git clone https://github.com/VaidhyaMegha/optimization_algorithms.git - -# Navigate to the frontend directory -cd optimization_algorithms/frontend - -# Install dependencies -npm install - -# Start the development server -npm start -``` - -The application will be available at `http://localhost:3000`. - -## Deployment - -The application is deployed on GitHub Pages. To update the deployment: - -```bash -# Build and deploy the application -npm run deploy -``` - -This will: -1. Build an optimized production version of the app -2. Deploy it to the `gh-pages` branch of the repository -3. Make it available at https://vaidhyamegha.github.io/optimization_algorithms/ - -### Notes on Deployment - -- The application uses HashRouter for proper routing on GitHub Pages -- All routes are accessible via hash-based URLs (e.g., `/#/algorithm/bmr`) -- Any changes pushed to the main branch will not automatically update the live site; you must run `npm run deploy` - -## Usage - -- **Home Page**: Overview of all available algorithms -- **Algorithm Page**: Detailed information and visualizations for a specific algorithm -- **Comparison Page**: Compare multiple algorithms on the same objective function -- **Papers Page**: Browse research papers related to the algorithms -- **About Page**: Information about the project and Prof. R.V. Rao's work - -## Technologies Used - -- React.js -- React Router -- React Bootstrap -- Chart.js -- Plotly.js -- MathJS -- KaTeX -- Three.js -- D3.js - -## References - -The algorithms implemented in this visualizer are based on the research work of Prof. R.V. Rao and his colleagues. For detailed information about each algorithm, please refer to the original research papers cited in the application. - -## License - -This project is licensed under the MIT License - see the LICENSE file for details. diff --git a/frontend/package-lock.json b/frontend/package-lock.json deleted file mode 100644 index 87bc2bc..0000000 --- a/frontend/package-lock.json +++ /dev/null @@ -1,22129 +0,0 @@ -{ - "name": "rao-optimization-visualizer", - "version": "0.1.0", - "lockfileVersion": 3, - "requires": true, - "packages": { - "": { - "name": "rao-optimization-visualizer", - "version": "0.1.0", - "dependencies": { - "@react-three/drei": "^9.88.13", - "bootstrap": "^5.3.2", - "chart.js": "^4.4.0", - "d3": "^7.8.5", - "katex": "^0.16.9", - "mathjs": "^12.1.0", - "plotly.js": "^2.27.1", - "react": "^18.2.0", - "react-bootstrap": "^2.9.1", - "react-chartjs-2": "^5.2.0", - "react-dom": "^18.2.0", - "react-katex": "^3.0.1", - "react-plotly.js": "^2.6.0", - "react-router-dom": "^6.20.0", - "react-scripts": "5.0.1", - 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"react-router-dom": "^6.20.0", - "react-scripts": "5.0.1", - "react-syntax-highlighter": "^15.5.0", - "react-three-fiber": "^6.0.13", - "three": "^0.158.0" - }, - "scripts": { - "start": "react-scripts start", - "build": "react-scripts build", - "test": "react-scripts test", - "eject": "react-scripts eject", - "predeploy": "npm run build", - "deploy": "gh-pages -d build" - }, - "eslintConfig": { - "extends": [ - "react-app", - "react-app/jest" - ] - }, - "browserslist": { - "production": [ - ">0.2%", - "not dead", - "not op_mini all" - ], - "development": [ - "last 1 chrome version", - "last 1 firefox version", - "last 1 safari version" - ] - }, - "devDependencies": { - "gh-pages": "^6.3.0" - } -} diff --git a/frontend/public/CNAME b/frontend/public/CNAME deleted file mode 100644 index 379cbe0..0000000 --- a/frontend/public/CNAME +++ /dev/null @@ -1 +0,0 @@ -optimizations.aisdlc.com \ No newline at end of file diff --git a/frontend/public/index.html b/frontend/public/index.html deleted file mode 100644 index 8e2c0b3..0000000 --- a/frontend/public/index.html +++ /dev/null @@ -1,20 +0,0 @@ - - - - - - - - - - - Rao Optimization Algorithms Visualizer - - - -
- - diff --git a/frontend/public/manifest.json b/frontend/public/manifest.json deleted file mode 100644 index 2cf933e..0000000 --- a/frontend/public/manifest.json +++ /dev/null @@ -1,25 +0,0 @@ -{ - "short_name": "Rao Algorithms", - "name": "Rao Optimization Algorithms Visualizer", - "icons": [ - { - "src": "favicon.ico", - "sizes": "64x64 32x32 24x24 16x16", - "type": "image/x-icon" - }, - { - "src": "logo192.png", - "type": "image/png", - "sizes": "192x192" - }, - { - "src": "logo512.png", - "type": "image/png", - "sizes": "512x512" - } - ], - "start_url": ".", - "display": "standalone", - "theme_color": "#000000", - "background_color": "#ffffff" -} diff --git a/frontend/src/App.css b/frontend/src/App.css deleted file mode 100644 index 98648cd..0000000 --- a/frontend/src/App.css +++ /dev/null @@ -1,189 +0,0 @@ -.app-container { - display: flex; - flex-direction: column; - min-height: 100vh; -} - -.main-content { - flex: 1; - padding: 20px; -} - -.page-title { - margin-bottom: 30px; - color: #333; - border-bottom: 2px solid #0d6efd; - padding-bottom: 10px; -} - -.section-title { - margin: 30px 0 20px; - color: #495057; -} - -.card-title { - color: #0d6efd; -} - -.card-text { - color: #6c757d; -} - -.btn-primary { - background-color: #0d6efd; - border-color: #0d6efd; -} - -.btn-primary:hover { - background-color: #0b5ed7; - border-color: #0a58ca; -} - -.algorithm-detail { - margin-top: 20px; -} - -.paper-reference { - background-color: #f8f9fa; - padding: 15px; - border-radius: 5px; - margin-top: 20px; - border-left: 3px solid #0d6efd; -} - -.visualization-options { - margin-bottom: 20px; -} - -.visualization-container canvas { - max-width: 100%; -} - -.parameter-control { - margin-bottom: 15px; -} - -.parameter-label { - font-weight: bold; - margin-bottom: 5px; -} - -.form-range { - width: 100%; -} - -.algorithm-info { - margin-bottom: 30px; -} - -.algorithm-pseudocode { - font-family: monospace; - background-color: #f8f9fa; - padding: 15px; - border-radius: 5px; - overflow-x: auto; -} - -.math-formula { - margin: 15px 0; - overflow-x: auto; -} - -.result-table { - margin-top: 20px; -} - -.highlight { - background-color: #fff3cd; - padding: 2px; - border-radius: 3px; -} - -.comparison-controls { - margin-bottom: 20px; - padding: 15px; - background-color: #f8f9fa; - border-radius: 5px; -} - -.comparison-result { - margin-top: 30px; -} - -.about-section { - margin-bottom: 30px; -} - -.paper-card { - margin-bottom: 20px; - height: 100%; -} - -.paper-card .card-body { - display: flex; - flex-direction: column; -} - -.paper-card .card-text { - flex-grow: 1; -} - -.paper-year { - color: #6c757d; - font-size: 0.9rem; -} - -.paper-journal { - font-style: italic; - margin-bottom: 10px; -} - -.paper-link { - margin-top: auto; -} - -.search-container { - margin-bottom: 20px; -} - -.search-input { - width: 100%; - padding: 10px; - border: 1px solid #ced4da; - border-radius: 5px; -} - -.filter-container { - margin-bottom: 20px; -} - -.filter-label { - font-weight: bold; - margin-right: 10px; -} - -.filter-select { - padding: 5px; - border: 1px solid #ced4da; - border-radius: 5px; -} - -.no-results { - text-align: center; - padding: 50px; - color: #6c757d; -} - -.loading { - text-align: center; - padding: 50px; -} - -.error { - color: #dc3545; - text-align: center; - padding: 20px; - background-color: #f8d7da; - border-radius: 5px; - margin: 20px 0; -} diff --git a/frontend/src/App.js b/frontend/src/App.js deleted file mode 100644 index 1b9cbe7..0000000 --- a/frontend/src/App.js +++ /dev/null @@ -1,32 +0,0 @@ -import React from 'react'; -import { HashRouter as Router, Routes, Route } from 'react-router-dom'; -import Header from './components/Header'; -import Footer from './components/Footer'; -import HomePage from './pages/HomePage'; -import AlgorithmPage from './pages/AlgorithmPage'; -import ComparisonPage from './pages/ComparisonPage'; -import AboutPage from './pages/AboutPage'; -import PapersPage from './pages/PapersPage'; -import './App.css'; - -function App() { - return ( - -
-
-
- - } /> - } /> - } /> - } /> - } /> - -
-
-
-
- ); -} - -export default App; diff --git a/frontend/src/components/AlgorithmSimulator.js b/frontend/src/components/AlgorithmSimulator.js deleted file mode 100644 index 9a0497c..0000000 --- a/frontend/src/components/AlgorithmSimulator.js +++ /dev/null @@ -1,433 +0,0 @@ -import React, { useState, useEffect } from 'react'; -import { Card, Button, Form, Row, Col, Alert, ProgressBar } from 'react-bootstrap'; -import SyntaxHighlighter from 'react-syntax-highlighter'; -import { docco } from 'react-syntax-highlighter/dist/esm/styles/hljs'; - -const AlgorithmSimulator = ({ algorithm, objectiveFunction }) => { - const [currentStep, setCurrentStep] = useState(0); - const [isSimulating, setIsSimulating] = useState(false); - const [simulationSpeed, setSimulationSpeed] = useState(1000); - const [simulationSteps, setSimulationSteps] = useState([]); - const [populationSize, setPopulationSize] = useState(10); - const [numIterations, setNumIterations] = useState(5); - - // Generate simulation steps based on the algorithm - const generateSimulationSteps = () => { - const steps = []; - - // Step 1: Initialization - steps.push({ - title: 'Initialization', - description: `Initialize a population of ${populationSize} solutions randomly within the bounds of the search space.`, - code: `# Initialize population randomly within bounds -population = np.random.uniform(low=bounds[:, 0], high=bounds[:, 1], - size=(${populationSize}, ${objectiveFunction ? 2 : 'num_variables'}))`, - visualization: 'random_points' - }); - - // Generate algorithm-specific steps - if (algorithm.id === 'jaya') { - // Jaya algorithm steps - steps.push({ - title: 'Evaluate Fitness', - description: 'Calculate the fitness (objective function value) for each solution in the population.', - code: `# Evaluate fitness of all solutions -fitness = np.apply_along_axis(objective_func, 1, population)`, - visualization: 'fitness_evaluation' - }); - - steps.push({ - title: 'Identify Best and Worst Solutions', - description: 'Find the best solution (with minimum fitness value) and worst solution (with maximum fitness value) in the population.', - code: `# Identify best and worst solutions -best_idx = np.argmin(fitness) -worst_idx = np.argmax(fitness) -best_solution = population[best_idx] -worst_solution = population[worst_idx]`, - visualization: 'best_worst' - }); - - steps.push({ - title: 'Update Solutions', - description: 'Update each solution by moving toward the best solution and away from the worst solution.', - code: `# Update each solution -for i in range(population_size): - r1 = np.random.rand(num_variables) - r2 = np.random.rand(num_variables) - - # Jaya update rule: move toward best and away from worst - population[i] = population[i] + r1 * (best_solution - np.abs(population[i])) - - r2 * (worst_solution - np.abs(population[i]))`, - visualization: 'update_solutions' - }); - - steps.push({ - title: 'Boundary Handling', - description: 'Ensure all solutions remain within the bounds of the search space.', - code: `# Clip solutions to stay within bounds -population = np.clip(population, bounds[:, 0], bounds[:, 1])`, - visualization: 'boundary_handling' - }); - - } else if (algorithm.id === 'bmr') { - // BMR algorithm steps - steps.push({ - title: 'Evaluate Fitness', - description: 'Calculate the fitness (objective function value) for each solution in the population.', - code: `# Evaluate fitness of all solutions -fitness = np.apply_along_axis(objective_func, 1, population)`, - visualization: 'fitness_evaluation' - }); - - steps.push({ - title: 'Identify Best Solution and Calculate Mean', - description: 'Find the best solution (with minimum fitness value) and calculate the mean solution across the population.', - code: `# Identify best solution and calculate mean -best_solution = population[np.argmin(fitness)] -mean_solution = np.mean(population, axis=0)`, - visualization: 'best_mean' - }); - - steps.push({ - title: 'Update Solutions', - description: 'Update each solution using the BMR update rule, which involves the best solution, mean solution, and a random solution.', - code: `# Update each solution -for i in range(population_size): - r1, r2, r3, r4 = np.random.rand(4) - T = np.random.choice([1, 2]) - random_solution = population[np.random.randint(population_size)] - - if r4 > 0.5: - population[i] += r1 * (best_solution - T * mean_solution) + - r2 * (best_solution - random_solution) - else: - population[i] = bounds[:, 1] - (bounds[:, 1] - bounds[:, 0]) * r3`, - visualization: 'update_solutions' - }); - - steps.push({ - title: 'Boundary Handling', - description: 'Ensure all solutions remain within the bounds of the search space.', - code: `# Clip solutions to stay within bounds -population = np.clip(population, bounds[:, 0], bounds[:, 1])`, - visualization: 'boundary_handling' - }); - - } else if (algorithm.id === 'tlbo') { - // TLBO algorithm steps - steps.push({ - title: 'Evaluate Fitness', - description: 'Calculate the fitness (objective function value) for each solution in the population.', - code: `# Evaluate fitness of all solutions -fitness = np.apply_along_axis(objective_func, 1, population)`, - visualization: 'fitness_evaluation' - }); - - steps.push({ - title: 'Teacher Phase', - description: 'Identify the best solution as the teacher and update solutions based on the difference between the mean and the teacher.', - code: `# Teacher Phase -best_idx = np.argmin(fitness) -teacher = population[best_idx] -mean = np.mean(population, axis=0) - -for i in range(population_size): - # Teaching factor: either 1 or 2 - Tf = np.random.choice([1, 2]) - - # Generate new solution - new_solution = population[i] + np.random.rand(num_variables) * (teacher - Tf * mean) - - # Accept if better - new_fitness = objective_func(new_solution) - if new_fitness < fitness[i]: - population[i] = new_solution - fitness[i] = new_fitness`, - visualization: 'teacher_phase' - }); - - steps.push({ - title: 'Learner Phase', - description: 'Update solutions based on the interaction between learners (solutions).', - code: `# Learner Phase -for i in range(population_size): - # Randomly select another solution - j = np.random.randint(population_size) - while j == i: - j = np.random.randint(population_size) - - # Generate new solution based on comparison - if fitness[i] < fitness[j]: # i is better than j - new_solution = population[i] + np.random.rand(num_variables) * (population[i] - population[j]) - else: # j is better than i - new_solution = population[i] + np.random.rand(num_variables) * (population[j] - population[i]) - - # Accept if better - new_fitness = objective_func(new_solution) - if new_fitness < fitness[i]: - population[i] = new_solution - fitness[i] = new_fitness`, - visualization: 'learner_phase' - }); - - steps.push({ - title: 'Boundary Handling', - description: 'Ensure all solutions remain within the bounds of the search space.', - code: `# Clip solutions to stay within bounds -population = np.clip(population, bounds[:, 0], bounds[:, 1])`, - visualization: 'boundary_handling' - }); - - } else { - // Generic steps for other algorithms - steps.push({ - title: 'Evaluate Fitness', - description: 'Calculate the fitness (objective function value) for each solution in the population.', - code: `# Evaluate fitness of all solutions -fitness = np.apply_along_axis(objective_func, 1, population)`, - visualization: 'fitness_evaluation' - }); - - steps.push({ - title: 'Update Solutions', - description: `Update each solution according to the ${algorithm.name} update rules.`, - code: `# Update solutions according to algorithm-specific rules -# See algorithm pseudocode for details`, - visualization: 'update_solutions' - }); - - steps.push({ - title: 'Boundary Handling', - description: 'Ensure all solutions remain within the bounds of the search space.', - code: `# Clip solutions to stay within bounds -population = np.clip(population, bounds[:, 0], bounds[:, 1])`, - visualization: 'boundary_handling' - }); - } - - // Final step: Return best solution - steps.push({ - title: 'Return Best Solution', - description: 'After all iterations, return the best solution found.', - code: `# Find the best solution in the final population -final_fitness = np.apply_along_axis(objective_func, 1, population) -best_solution = population[np.argmin(final_fitness)] - -return best_solution, best_scores`, - visualization: 'final_solution' - }); - - return steps; - }; - - // Start simulation - const startSimulation = () => { - const steps = generateSimulationSteps(); - setSimulationSteps(steps); - setCurrentStep(0); - setIsSimulating(true); - - // Automatically advance through steps - const interval = setInterval(() => { - setCurrentStep(prevStep => { - if (prevStep < steps.length - 1) { - return prevStep + 1; - } else { - clearInterval(interval); - setIsSimulating(false); - return prevStep; - } - }); - }, simulationSpeed); - - // Cleanup interval on component unmount - return () => clearInterval(interval); - }; - - // Handle manual navigation - const goToStep = (step) => { - setCurrentStep(step); - }; - - // Handle speed change - const handleSpeedChange = (e) => { - setSimulationSpeed(parseInt(e.target.value)); - }; - - return ( -
- - - - Simulation Settings - -
- - Population Size: {populationSize} - setPopulationSize(parseInt(e.target.value))} - disabled={isSimulating} - /> - - - - Number of Iterations: {numIterations} - setNumIterations(parseInt(e.target.value))} - disabled={isSimulating} - /> - - - - Simulation Speed - - - - - - - - -
-
-
- - {simulationSteps.length > 0 && ( - - Simulation Steps - -
- {simulationSteps.map((step, index) => ( - - ))} -
- - -
-
- )} - - - - {simulationSteps.length > 0 ? ( - - -
{simulationSteps[currentStep].title}
-
- -

{simulationSteps[currentStep].description}

- -
-
Code:
- - {simulationSteps[currentStep].code} - -
- -
-
Visualization: {simulationSteps[currentStep].visualization}
-

- This is a placeholder for the actual visualization of this step. - In a complete implementation, this would show an interactive visualization - of the {simulationSteps[currentStep].visualization} process. -

-
- -
-
Explanation:
-

- {currentStep === 0 && `In this step, we initialize ${populationSize} solutions randomly within the bounds of the search space. Each solution is a vector of ${objectiveFunction ? 2 : 'num_variables'} variables.`} - - {currentStep === 1 && `We evaluate the fitness (objective function value) for each solution in the population. For the ${objectiveFunction?.name || 'selected objective function'}, lower values are better.`} - - {currentStep === 2 && algorithm.id === 'jaya' && `We identify the best solution (with minimum fitness value) and worst solution (with maximum fitness value) in the population. These will guide the search process.`} - - {currentStep === 2 && algorithm.id === 'bmr' && `We identify the best solution (with minimum fitness value) and calculate the mean solution across the population. These will guide the search process.`} - - {currentStep === 2 && algorithm.id === 'tlbo' && `In the Teacher Phase, we identify the best solution as the teacher and update solutions based on the difference between the mean and the teacher. The teaching factor (Tf) is randomly chosen as either 1 or 2.`} - - {currentStep === 3 && algorithm.id === 'jaya' && `We update each solution by moving toward the best solution and away from the worst solution. The random vectors r1 and r2 add stochasticity to the search process.`} - - {currentStep === 3 && algorithm.id === 'bmr' && `We update each solution using the BMR update rule. If r4 > 0.5, we move toward the best solution and away from the mean solution and a random solution. Otherwise, we perform random exploration.`} - - {currentStep === 3 && algorithm.id === 'tlbo' && `In the Learner Phase, solutions learn from each other. Each solution is compared with a randomly selected solution, and the update direction depends on which one is better.`} - - {(currentStep === 4 && (algorithm.id === 'jaya' || algorithm.id === 'bmr')) && `We ensure all solutions remain within the bounds of the search space by clipping any values that exceed the bounds.`} - - {(currentStep === 4 && algorithm.id === 'tlbo') && `We ensure all solutions remain within the bounds of the search space by clipping any values that exceed the bounds.`} - - {currentStep === simulationSteps.length - 1 && `After all iterations, we return the best solution found in the final population. This solution represents the optimum found by the ${algorithm.name}.`} -

-
-
- -
- - - -
-
-
- ) : ( - - -

Algorithm Simulation

-

- This simulation will walk you through the steps of the {algorithm.name} algorithm. - Configure the settings and click "Start Simulation" to begin. -

- - The simulation will demonstrate how {algorithm.name} works on the {objectiveFunction?.name || 'selected objective function'}. - -
-
- )} - -
-
- ); -}; - -export default AlgorithmSimulator; diff --git a/frontend/src/components/Footer.js b/frontend/src/components/Footer.js deleted file mode 100644 index 3b81c83..0000000 --- a/frontend/src/components/Footer.js +++ /dev/null @@ -1,25 +0,0 @@ -import React from 'react'; -import { Container, Row, Col } from 'react-bootstrap'; - -const Footer = () => { - return ( - - ); -}; - -export default Footer; diff --git a/frontend/src/components/Header.js b/frontend/src/components/Header.js deleted file mode 100644 index 5295207..0000000 --- a/frontend/src/components/Header.js +++ /dev/null @@ -1,34 +0,0 @@ -import React from 'react'; -import { Navbar, Nav, Container } from 'react-bootstrap'; -import { Link, NavLink } from 'react-router-dom'; - -const Header = () => { - return ( - - - - Rao Optimization Algorithms - - - - - - - - ); -}; - -export default Header; diff --git a/frontend/src/components/VisualizationComponent.js b/frontend/src/components/VisualizationComponent.js deleted file mode 100644 index 41339f0..0000000 --- a/frontend/src/components/VisualizationComponent.js +++ /dev/null @@ -1,613 +0,0 @@ -import React, { useState, useEffect } from 'react'; -import { Spinner, Tabs, Tab } from 'react-bootstrap'; -import Plot from 'react-plotly.js'; -import objectiveFunctions from '../utils/objectiveFunctions'; - -const VisualizationComponent = ({ functionId, algorithmId, simulationResults }) => { - const [isLoading, setIsLoading] = useState(true); - const [activeTab, setActiveTab] = useState('contour'); - const [plotData, setPlotData] = useState([]); - const [plotLayout, setPlotLayout] = useState({}); - const [plotConfig, setPlotConfig] = useState({}); - - useEffect(() => { - if (!functionId) return; - - setIsLoading(true); - - // Get the selected objective function - const objectiveFunction = objectiveFunctions.find(func => func.id === functionId); - if (!objectiveFunction) return; - - // Generate data for the selected visualization - try { - if (activeTab === 'contour') { - generateContourPlot(objectiveFunction); - } else if (activeTab === 'surface') { - generate3DSurfacePlot(objectiveFunction); - } else if (activeTab === 'optimization') { - generateOptimizationVisualization(objectiveFunction, simulationResults); - } - } catch (error) { - console.error('Error generating visualization:', error); - } - - }, [functionId, algorithmId, simulationResults, activeTab]); - - // Function to generate a contour plot - const generateContourPlot = (objectiveFunction) => { - const { bounds } = objectiveFunction; - const minX = bounds[0]; - const maxX = bounds[1]; - const minY = bounds[0]; - const maxY = bounds[1]; - - // Create a grid of x and y values - const resolution = 100; - const step = (maxX - minX) / (resolution - 1); - - const x = Array.from({ length: resolution }, (_, i) => minX + i * step); - const y = Array.from({ length: resolution }, (_, i) => minY + i * step); - - // Calculate z values for the grid - const z = []; - for (let i = 0; i < resolution; i++) { - z[i] = []; - for (let j = 0; j < resolution; j++) { - const xVal = x[j]; - const yVal = y[i]; - z[i][j] = calculateFunctionValue(objectiveFunction, xVal, yVal); - } - } - - // Create the contour plot - const data = [ - { - type: 'contour', - z: z, - x: x, - y: y, - colorscale: 'Jet', - contours: { - coloring: 'heatmap', - showlabels: true, - }, - colorbar: { - title: 'Function Value', - titleside: 'right', - }, - } - ]; - - // Add global minimum point if available - if (objectiveFunction.globalMinimum) { - let minX, minY; - - if (objectiveFunction.globalMinimum.position === "Origin (all zeros)") { - minX = 0; - minY = 0; - } else if (objectiveFunction.globalMinimum.position === "All ones") { - minX = 1; - minY = 1; - } else if (objectiveFunction.globalMinimum.position === "(3, 0.5)") { - minX = 3; - minY = 0.5; - } else { - // Default to origin - minX = 0; - minY = 0; - } - - data.push({ - type: 'scatter', - x: [minX], - y: [minY], - mode: 'markers', - marker: { - color: 'white', - size: 10, - line: { - color: 'black', - width: 2 - } - }, - name: 'Global Minimum' - }); - } - - // Add best solution point if available - if (simulationResults && simulationResults.bestSolution && simulationResults.bestSolution.length >= 2) { - data.push({ - type: 'scatter', - x: [simulationResults.bestSolution[0]], - y: [simulationResults.bestSolution[1]], - mode: 'markers', - marker: { - color: 'yellow', - size: 12, - line: { - color: 'black', - width: 2 - } - }, - name: 'Best Solution' - }); - } - - const layout = { - title: `${objectiveFunction.name} - Contour Plot`, - xaxis: { - title: 'x', - range: [minX, maxX] - }, - yaxis: { - title: 'y', - range: [minY, maxY] - }, - autosize: true, - margin: { - l: 50, - r: 50, - b: 50, - t: 50, - pad: 4 - }, - paper_bgcolor: '#f8f9fa', - plot_bgcolor: '#f8f9fa' - }; - - const config = { - responsive: true, - displayModeBar: true, - displaylogo: false, - modeBarButtonsToRemove: ['lasso2d', 'select2d'] - }; - - setPlotData(data); - setPlotLayout(layout); - setPlotConfig(config); - setIsLoading(false); - }; - - // Function to generate a 3D surface plot - const generate3DSurfacePlot = (objectiveFunction) => { - const { bounds } = objectiveFunction; - const minX = bounds[0]; - const maxX = bounds[1]; - const minY = bounds[0]; - const maxY = bounds[1]; - - // Create a grid of x and y values - const resolution = 50; - const step = (maxX - minX) / (resolution - 1); - - const x = Array.from({ length: resolution }, (_, i) => minX + i * step); - const y = Array.from({ length: resolution }, (_, i) => minY + i * step); - - // Calculate z values for the grid - const z = []; - for (let i = 0; i < resolution; i++) { - z[i] = []; - for (let j = 0; j < resolution; j++) { - const xVal = x[j]; - const yVal = y[i]; - z[i][j] = calculateFunctionValue(objectiveFunction, xVal, yVal); - } - } - - // Create the 3D surface plot - const data = [ - { - type: 'surface', - z: z, - x: x, - y: y, - colorscale: 'Jet', - contours: { - z: { - show: true, - usecolormap: true, - highlightcolor: "#42f462", - project: { z: true } - } - }, - colorbar: { - title: 'Function Value', - titleside: 'right', - }, - } - ]; - - // Add global minimum point if available - if (objectiveFunction.globalMinimum) { - let minX, minY; - - if (objectiveFunction.globalMinimum.position === "Origin (all zeros)") { - minX = 0; - minY = 0; - } else if (objectiveFunction.globalMinimum.position === "All ones") { - minX = 1; - minY = 1; - } else if (objectiveFunction.globalMinimum.position === "(3, 0.5)") { - minX = 3; - minY = 0.5; - } else { - // Default to origin - minX = 0; - minY = 0; - } - - const minZ = calculateFunctionValue(objectiveFunction, minX, minY); - - data.push({ - type: 'scatter3d', - x: [minX], - y: [minY], - z: [minZ], - mode: 'markers', - marker: { - color: 'white', - size: 5, - line: { - color: 'black', - width: 1 - } - }, - name: 'Global Minimum' - }); - } - - // Add best solution point if available - if (simulationResults && simulationResults.bestSolution && simulationResults.bestSolution.length >= 2) { - const bestX = simulationResults.bestSolution[0]; - const bestY = simulationResults.bestSolution[1]; - const bestZ = calculateFunctionValue(objectiveFunction, bestX, bestY); - - data.push({ - type: 'scatter3d', - x: [bestX], - y: [bestY], - z: [bestZ], - mode: 'markers', - marker: { - color: 'yellow', - size: 6, - line: { - color: 'black', - width: 1 - } - }, - name: 'Best Solution' - }); - } - - const layout = { - title: `${objectiveFunction.name} - 3D Surface`, - autosize: true, - scene: { - xaxis: { - title: 'x', - range: [minX, maxX] - }, - yaxis: { - title: 'y', - range: [minY, maxY] - }, - zaxis: { - title: 'f(x,y)' - }, - camera: { - eye: { x: 1.5, y: 1.5, z: 1 } - } - }, - margin: { - l: 0, - r: 0, - b: 0, - t: 50, - pad: 4 - }, - paper_bgcolor: '#f8f9fa', - plot_bgcolor: '#f8f9fa' - }; - - const config = { - responsive: true, - displayModeBar: true, - displaylogo: false - }; - - setPlotData(data); - setPlotLayout(layout); - setPlotConfig(config); - setIsLoading(false); - }; - - // Function to generate optimization visualization - const generateOptimizationVisualization = (objectiveFunction, simulationResults) => { - // If no simulation results, generate contour plot with a message - if (!simulationResults) { - generateContourPlot(objectiveFunction); - return; - } - - const { bounds } = objectiveFunction; - const minX = bounds[0]; - const maxX = bounds[1]; - const minY = bounds[0]; - const maxY = bounds[1]; - - // Create a grid of x and y values for the contour plot - const resolution = 100; - const step = (maxX - minX) / (resolution - 1); - - const x = Array.from({ length: resolution }, (_, i) => minX + i * step); - const y = Array.from({ length: resolution }, (_, i) => minY + i * step); - - // Calculate z values for the grid - const z = []; - for (let i = 0; i < resolution; i++) { - z[i] = []; - for (let j = 0; j < resolution; j++) { - const xVal = x[j]; - const yVal = y[i]; - z[i][j] = calculateFunctionValue(objectiveFunction, xVal, yVal); - } - } - - // Create the contour plot - const data = [ - { - type: 'contour', - z: z, - x: x, - y: y, - colorscale: 'Jet', - contours: { - coloring: 'heatmap', - showlabels: true, - }, - colorbar: { - title: 'Function Value', - titleside: 'right', - }, - } - ]; - - // Simulate optimization path (since we don't have actual path data) - // In a real implementation, this would use the actual path data from the algorithm - const pathPoints = []; - const numPoints = Math.min(20, simulationResults.iterations.length); - - for (let i = 0; i < numPoints; i++) { - // Generate a path that converges to the best solution - const t = i / (numPoints - 1); - const randomOffset = (1 - t) * (Math.random() * 2 - 1); - - const pathX = simulationResults.bestSolution[0] + randomOffset * (bounds[1] - bounds[0]) * 0.2; - const pathY = simulationResults.bestSolution[1] + randomOffset * (bounds[1] - bounds[0]) * 0.2; - - pathPoints.push({ x: pathX, y: pathY }); - } - - // Add path to the plot - const pathX = pathPoints.map(p => p.x); - const pathY = pathPoints.map(p => p.y); - - data.push({ - type: 'scatter', - x: pathX, - y: pathY, - mode: 'lines+markers', - line: { - color: 'white', - width: 2 - }, - marker: { - color: pathX.map((_, i) => `rgba(255, 255, 255, ${0.3 + (i / (pathX.length - 1)) * 0.7})`), - size: pathX.map((_, i) => 3 + (i / (pathX.length - 1)) * 4) - }, - name: 'Optimization Path' - }); - - // Add best solution point - data.push({ - type: 'scatter', - x: [simulationResults.bestSolution[0]], - y: [simulationResults.bestSolution[1]], - mode: 'markers', - marker: { - color: 'yellow', - size: 12, - line: { - color: 'black', - width: 2 - } - }, - name: 'Best Solution' - }); - - // Add global minimum if available - if (objectiveFunction.globalMinimum) { - let minX, minY; - - if (objectiveFunction.globalMinimum.position === "Origin (all zeros)") { - minX = 0; - minY = 0; - } else if (objectiveFunction.globalMinimum.position === "All ones") { - minX = 1; - minY = 1; - } else if (objectiveFunction.globalMinimum.position === "(3, 0.5)") { - minX = 3; - minY = 0.5; - } else { - // Default to origin - minX = 0; - minY = 0; - } - - data.push({ - type: 'scatter', - x: [minX], - y: [minY], - mode: 'markers', - marker: { - color: 'white', - size: 10, - line: { - color: 'black', - width: 2 - } - }, - name: 'Global Minimum' - }); - } - - const layout = { - title: `${objectiveFunction.name} - Optimization Path`, - xaxis: { - title: 'x', - range: [minX, maxX] - }, - yaxis: { - title: 'y', - range: [minY, maxY] - }, - autosize: true, - margin: { - l: 50, - r: 50, - b: 50, - t: 50, - pad: 4 - }, - paper_bgcolor: '#f8f9fa', - plot_bgcolor: '#f8f9fa' - }; - - const config = { - responsive: true, - displayModeBar: true, - displaylogo: false, - modeBarButtonsToRemove: ['lasso2d', 'select2d'] - }; - - setPlotData(data); - setPlotLayout(layout); - setPlotConfig(config); - setIsLoading(false); - }; - - // Helper function to calculate function value for a given point - const calculateFunctionValue = (objectiveFunction, x, y) => { - switch (objectiveFunction.id) { - case 'sphere': - return x * x + y * y; - case 'rastrigin': - return 20 + x * x + y * y - 10 * (Math.cos(2 * Math.PI * x) + Math.cos(2 * Math.PI * y)); - case 'ackley': - return -20 * Math.exp(-0.2 * Math.sqrt(0.5 * (x * x + y * y))) - - Math.exp(0.5 * (Math.cos(2 * Math.PI * x) + Math.cos(2 * Math.PI * y))) + 20 + Math.E; - case 'rosenbrock': - return 100 * Math.pow(y - x * x, 2) + Math.pow(x - 1, 2); - case 'beale': - return Math.pow(1.5 - x + x * y, 2) + - Math.pow(2.25 - x + x * y * y, 2) + - Math.pow(2.625 - x + x * y * y * y, 2); - case 'himmelblau': - return Math.pow(x * x + y - 11, 2) + Math.pow(x + y * y - 7, 2); - case 'griewank': - return 1 + (x * x + y * y) / 4000 - Math.cos(x) * Math.cos(y / Math.sqrt(2)); - case 'levy': - const w1 = 1 + (x - 1) / 4; - const w2 = 1 + (y - 1) / 4; - return Math.pow(Math.sin(Math.PI * w1), 2) + - Math.pow(w1 - 1, 2) * (1 + 10 * Math.pow(Math.sin(Math.PI * w1 + 1), 2)) + - Math.pow(w2 - 1, 2) * (1 + Math.pow(Math.sin(2 * Math.PI * w2), 2)); - case 'constrained': - return x * x + y * y + x * y - 10 * x - 15 * y; - default: - return x * x + y * y; // Default to sphere function - } - }; - - return ( -
- setActiveTab(k)} - className="mb-3" - > - - {isLoading ? ( -
- - Loading... - -
- ) : ( -
- -
- )} -
- - {isLoading ? ( -
- - Loading... - -
- ) : ( -
- -
- )} -
- - {isLoading ? ( -
- - Loading... - -
- ) : ( -
- - {!simulationResults && ( -
-

Run a simulation to see the optimization path

-
- )} -
- )} -
-
- -
- - Note: Visualizations are simplified representations for educational purposes. - {!simulationResults && activeTab === 'optimization' && - " Run a simulation to see the optimization path."} - -
-
- ); -}; - -export default VisualizationComponent; diff --git a/frontend/src/index.css b/frontend/src/index.css deleted file mode 100644 index 7981e98..0000000 --- a/frontend/src/index.css +++ /dev/null @@ -1,156 +0,0 @@ -body { - margin: 0; - font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen', - 'Ubuntu', 'Cantarell', 'Fira Sans', 'Droid Sans', 'Helvetica Neue', - sans-serif; - -webkit-font-smoothing: antialiased; - -moz-osx-font-smoothing: grayscale; - background-color: #f8f9fa; -} - -code { - font-family: source-code-pro, Menlo, Monaco, Consolas, 'Courier New', - monospace; -} - -.app-container { - max-width: 1400px; - margin: 0 auto; - padding: 20px; -} - -.algorithm-card { - height: 100%; - transition: transform 0.3s ease; - margin-bottom: 20px; -} - -.algorithm-card:hover { - transform: translateY(-5px); - box-shadow: 0 10px 20px rgba(0,0,0,0.1); -} - -.visualization-container { - min-height: 400px; - margin: 20px 0; - padding: 20px; - background-color: white; - border-radius: 8px; - box-shadow: 0 4px 6px rgba(0,0,0,0.1); -} - -.paper-citation { - font-style: italic; - margin-top: 10px; - padding: 10px; - background-color: #f1f1f1; - border-left: 4px solid #0d6efd; - border-radius: 4px; -} - -.control-panel { - background-color: #f8f9fa; - padding: 15px; - border-radius: 8px; - margin-bottom: 20px; - box-shadow: 0 2px 4px rgba(0,0,0,0.05); -} - -.nav-tabs .nav-link { - color: #495057; -} - -.nav-tabs .nav-link.active { - font-weight: bold; - color: #0d6efd; -} - -.algorithm-description { - margin-bottom: 20px; -} - -.comparison-chart { - min-height: 500px; -} - -.footer { - margin-top: 40px; - padding: 20px 0; - text-align: center; - background-color: #f8f9fa; - border-top: 1px solid #dee2e6; -} - -.parameter-slider { - margin: 15px 0; -} - -.code-block { - margin: 15px 0; - border-radius: 4px; -} - -.objective-function-selector { - margin-bottom: 20px; -} - -.convergence-chart { - margin-top: 20px; -} - -.paper-info { - margin-top: 20px; - padding: 15px; - background-color: #e9ecef; - border-radius: 8px; -} - -.author-info { - font-weight: bold; -} - -.journal-info { - color: #6c757d; -} - -.algorithm-badge { - margin-right: 5px; - margin-bottom: 5px; -} - -.visualization-3d { - height: 500px; - width: 100%; -} - -.parameter-table { - margin-top: 20px; -} - -.solution-display { - margin-top: 20px; - padding: 15px; - background-color: #e9ecef; - border-radius: 8px; - font-family: monospace; -} - -.iteration-slider { - margin: 20px 0; -} - -.loading-spinner { - display: flex; - justify-content: center; - align-items: center; - height: 200px; -} - -.header-logo { - height: 40px; - margin-right: 10px; -} - -.search-bar { - max-width: 300px; -} diff --git a/frontend/src/index.js b/frontend/src/index.js deleted file mode 100644 index 3715255..0000000 --- a/frontend/src/index.js +++ /dev/null @@ -1,12 +0,0 @@ -import React from 'react'; -import ReactDOM from 'react-dom/client'; -import './index.css'; -import App from './App'; -import 'bootstrap/dist/css/bootstrap.min.css'; - -const root = ReactDOM.createRoot(document.getElementById('root')); -root.render( - - - -); diff --git a/frontend/src/pages/AboutPage.js b/frontend/src/pages/AboutPage.js deleted file mode 100644 index 6af3625..0000000 --- a/frontend/src/pages/AboutPage.js +++ /dev/null @@ -1,219 +0,0 @@ -import React from 'react'; -import { Container, Row, Col, Card, Image } from 'react-bootstrap'; - -const AboutPage = () => { - return ( - -

About This Project

- - - - - -

Optimization Algorithms Visualizer

-

- This interactive platform is designed to visualize and demonstrate the optimization algorithms - developed by Prof. R.V. Rao and his colleagues. The goal is to provide a comprehensive, - educational resource for understanding these powerful optimization techniques and their - applications in solving real-world problems. -

-

- The visualizer allows users to: -

-
    -
  • Explore individual algorithms and their characteristics
  • -
  • Visualize how algorithms converge on different test functions
  • -
  • Compare the performance of multiple algorithms
  • -
  • Access the original research papers and citations
  • -
  • Understand the mathematical foundations of each algorithm
  • -
-
-
- -
- -

About Prof. R.V. Rao

- - - - -
- {/* Placeholder for profile image */} -
- Prof. R.V. Rao -
-
-

Prof. Ravipudi Venkata Rao

-

Professor and Researcher

-

Sardar Vallabhbhai National Institute of Technology, Surat, India

-
-
- - - - -

Biography

-

- Prof. Ravipudi Venkata Rao is a distinguished researcher and professor known for his - significant contributions to the field of optimization algorithms. He has developed - several innovative optimization techniques, including the Jaya Algorithm, Teaching-Learning-Based - Optimization (TLBO), and the Rao Algorithms. -

-

- His research focuses on developing simple, efficient, and parameter-free optimization - algorithms that can be applied to a wide range of engineering and scientific problems. - Prof. Rao's algorithms are particularly notable for their effectiveness in solving both - constrained and unconstrained optimization problems without requiring algorithm-specific - parameter tuning. -

-

- Prof. Rao has published numerous research papers in prestigious international journals - and has authored several books on optimization techniques. His work has been widely cited - and has influenced research in various fields, including mechanical engineering, electrical - engineering, and computer science. -

-
-
- -
- -

Key Innovations

- - - - -

Metaphor-Free Algorithms

-

- Unlike many nature-inspired optimization algorithms, Prof. Rao's algorithms (such as Jaya and - Rao Algorithms) do not rely on metaphors from natural processes. This approach simplifies the - algorithms and makes them more accessible and easier to implement. -

-
-
- - - - -

Parameter-Less Optimization

-

- Many of Prof. Rao's algorithms do not require algorithm-specific parameters to be tuned, - which is a significant advantage over other optimization techniques. This eliminates the - need for parameter sensitivity analysis and makes the algorithms more robust across different - problem domains. -

-
-
- - - - -

Teaching-Learning Paradigm

-

- The Teaching-Learning-Based Optimization (TLBO) algorithm introduced a novel paradigm based on - the teaching-learning process in a classroom. This approach has proven highly effective for - solving complex optimization problems and has inspired numerous variants and applications. -

-
-
- - - - -

Practical Applications

-

- Prof. Rao's algorithms have been successfully applied to a wide range of real-world problems, - including mechanical design optimization, manufacturing process optimization, thermal system - design, and many others. Their simplicity and effectiveness make them valuable tools for - engineers and researchers across disciplines. -

-
-
- -
- -

About This Visualizer

- - - - -

- This visualizer was created to provide an interactive platform for exploring and understanding - the optimization algorithms developed by Prof. R.V. Rao. It is designed to be educational and - informative, allowing users to: -

-
    -
  • Visualize how different algorithms converge on various test functions
  • -
  • Compare the performance of multiple algorithms side by side
  • -
  • Understand the mathematical foundations and pseudocode of each algorithm
  • -
  • Access the original research papers and proper citations
  • -
  • Learn about the practical applications of these optimization techniques
  • -
-

- The visualizer includes implementations of several key algorithms, including BMR, BWR, Jaya, - Rao Algorithms (Rao-1, Rao-2, Rao-3), TLBO, QOJAYA, GOTLBO, ITLBO, and Multi-objective TLBO. -

-

- We hope this platform serves as a valuable resource for students, researchers, and practitioners - interested in optimization algorithms and their applications. -

-
-
- -
- -

Contact Information

- - - - -

For More Information

-

- For more information about Prof. R.V. Rao's research and publications, please visit his - academic profile or refer to his published works in the Research Papers section of this - application. -

-

- If you have questions or feedback about this visualization platform, please contact us - at the email address below. -

-

- Email: contact@raoalgorithms-visualizer.org -

-
-
- - - - -

References

-

- This visualizer is based on the research published in various journals and conference - proceedings. For a complete list of references, please visit the Research Papers section. -

-

- The test functions used for algorithm evaluation are standard benchmark functions widely - used in the optimization literature. -

-

- All visualizations and simulations are for educational purposes and may not fully represent - the performance of the algorithms in complex real-world scenarios. -

-
-
- -
-
- ); -}; - -export default AboutPage; diff --git a/frontend/src/pages/AlgorithmPage.js b/frontend/src/pages/AlgorithmPage.js deleted file mode 100644 index 2092750..0000000 --- a/frontend/src/pages/AlgorithmPage.js +++ /dev/null @@ -1,356 +0,0 @@ -import React, { useState, useEffect } from 'react'; -import { useParams, Link } from 'react-router-dom'; -import { Container, Row, Col, Card, Button, Tabs, Tab, Form, Alert } from 'react-bootstrap'; -import { Line } from 'react-chartjs-2'; -import { Chart, registerables } from 'chart.js'; -import SyntaxHighlighter from 'react-syntax-highlighter'; -import { docco } from 'react-syntax-highlighter/dist/esm/styles/hljs'; -import { InlineMath, BlockMath } from 'react-katex'; -import 'katex/dist/katex.min.css'; -import algorithmData from '../utils/algorithmData'; -import objectiveFunctions from '../utils/objectiveFunctions'; -import VisualizationComponent from '../components/VisualizationComponent'; -import AlgorithmSimulator from '../components/AlgorithmSimulator'; - -// Register Chart.js components -Chart.register(...registerables); - -const AlgorithmPage = () => { - const { id } = useParams(); - const [algorithm, setAlgorithm] = useState(null); - const [selectedFunction, setSelectedFunction] = useState('sphere'); - const [numIterations, setNumIterations] = useState(100); - const [populationSize, setPopulationSize] = useState(30); - const [numVariables, setNumVariables] = useState(2); - const [simulationResults, setSimulationResults] = useState(null); - const [isSimulating, setIsSimulating] = useState(false); - const [activeTab, setActiveTab] = useState('overview'); - - useEffect(() => { - const foundAlgorithm = algorithmData.find(algo => algo.id === id); - setAlgorithm(foundAlgorithm); - - // Reset simulation results when algorithm changes - setSimulationResults(null); - }, [id]); - - const handleFunctionChange = (e) => { - setSelectedFunction(e.target.value); - setSimulationResults(null); - }; - - const handleRunSimulation = () => { - setIsSimulating(true); - - // Simulate algorithm execution (in a real app, this would call the actual algorithm) - setTimeout(() => { - // Generate mock simulation results - const iterations = Array.from({ length: numIterations }, (_, i) => i + 1); - const bestScores = iterations.map(i => 10 / (i * 0.1 + 1) + Math.random() * 0.5); - - setSimulationResults({ - iterations, - bestScores, - bestSolution: Array(numVariables).fill(0).map(() => Math.random() * 0.1 - 0.05), - executionTime: Math.random() * 1000 + 500 - }); - - setIsSimulating(false); - }, 1500); - }; - - if (!algorithm) { - return ( - - Algorithm not found - - - ); - } - - const chartData = simulationResults ? { - labels: simulationResults.iterations, - datasets: [ - { - label: 'Best Score', - data: simulationResults.bestScores, - fill: false, - backgroundColor: 'rgba(75,192,192,0.4)', - borderColor: 'rgba(75,192,192,1)', - tension: 0.1 - } - ] - } : null; - - const chartOptions = { - responsive: true, - plugins: { - title: { - display: true, - text: 'Convergence Curve' - }, - tooltip: { - mode: 'index', - intersect: false, - }, - }, - scales: { - x: { - display: true, - title: { - display: true, - text: 'Iteration' - } - }, - y: { - display: true, - title: { - display: true, - text: 'Objective Function Value' - }, - min: 0 - } - } - }; - - return ( - - - -

{algorithm.name}

-
- "{algorithm.paperTitle}" - {algorithm.authors} ({algorithm.year}) -
-

{algorithm.description}

- - - -
- - setActiveTab(k)} - className="mb-4" - > - - - - - Algorithm Details - -

Category: {algorithm.category}

-

Parameters: {algorithm.parameters}

-

Year Published: {algorithm.year}

-

Journal/Conference: {algorithm.journal}

-

Applications:

-
    - {algorithm.applications.map((app, index) => ( -
  • {app}
  • - ))} -
-
-
- - - - Pseudocode - - - {algorithm.pseudocode} - - - - -
- - - - Paper Abstract & Citation - -
-

{algorithm.authors}

-

"{algorithm.paperTitle}"

-

{algorithm.journal}

-

- This paper introduces the {algorithm.name}, a novel optimization algorithm - designed to solve complex optimization problems efficiently. The algorithm - {algorithm.category === "Metaphor-free" - ? " does not rely on any nature-inspired metaphors and " - : " "} - {algorithm.parameters === "No algorithm-specific parameters" - ? "does not require any algorithm-specific control parameters to be tuned." - : "requires minimal parameter tuning."} -

-
- Citation: -
- {algorithm.authors} ({algorithm.year}). {algorithm.paperTitle}. {algorithm.journal}. - {algorithm.link && } -
-
-
-
-
- -
-
- - - - - - Visualization Settings - -
- - Objective Function - - {objectiveFunctions.map(func => ( - - ))} - - - - - Number of Iterations: {numIterations} - setNumIterations(parseInt(e.target.value))} - /> - - - - Population Size: {populationSize} - setPopulationSize(parseInt(e.target.value))} - /> - - - - Number of Variables: {numVariables} - setNumVariables(parseInt(e.target.value))} - disabled={selectedFunction === 'beale' || selectedFunction === 'himmelblau' || selectedFunction === 'constrained'} - /> - {(selectedFunction === 'beale' || selectedFunction === 'himmelblau' || selectedFunction === 'constrained') && ( - - This function is fixed at 2 dimensions. - - )} - - - -
-
-
- - - Function Details - - {objectiveFunctions.find(func => func.id === selectedFunction) && ( - <> -
{objectiveFunctions.find(func => func.id === selectedFunction).name}
-
- {objectiveFunctions.find(func => func.id === selectedFunction).formula} -
-

{objectiveFunctions.find(func => func.id === selectedFunction).description}

-

Global Minimum: {objectiveFunctions.find(func => func.id === selectedFunction).globalMinimum.value} at {objectiveFunctions.find(func => func.id === selectedFunction).globalMinimum.position}

-

Bounds: [{objectiveFunctions.find(func => func.id === selectedFunction).bounds[0]}, {objectiveFunctions.find(func => func.id === selectedFunction).bounds[1]}]

- {objectiveFunctions.find(func => func.id === selectedFunction).constraints && ( - <> -

Constraints:

-
    - {objectiveFunctions.find(func => func.id === selectedFunction).constraints.map((constraint, index) => ( -
  • {constraint}
  • - ))} -
- - )} - - )} -
-
- - - - - Visualization - - - - - - {simulationResults && ( - <> - - Convergence Curve - - - - - - - Simulation Results - - - -
Best Solution Found:
-
- {simulationResults.bestSolution.map((val, idx) => ( -
x{idx+1} = {val.toFixed(6)}
- ))} -
- - -
Performance Metrics:
-

Final Objective Value: {simulationResults.bestScores[simulationResults.bestScores.length - 1].toFixed(6)}

-

Execution Time: {simulationResults.executionTime.toFixed(2)} ms

-

Iterations: {numIterations}

-

Population Size: {populationSize}

- -
-
-
- - )} - -
-
- - - func.id === selectedFunction)} - /> - -
-
- ); -}; - -export default AlgorithmPage; diff --git a/frontend/src/pages/ComparisonPage.js b/frontend/src/pages/ComparisonPage.js deleted file mode 100644 index afb11ac..0000000 --- a/frontend/src/pages/ComparisonPage.js +++ /dev/null @@ -1,287 +0,0 @@ -import React, { useState, useEffect } from 'react'; -import { Container, Row, Col, Card, Form, Button, Table } from 'react-bootstrap'; -import { Line } from 'react-chartjs-2'; -import { Chart, registerables } from 'chart.js'; -import algorithmData from '../utils/algorithmData'; -import objectiveFunctions from '../utils/objectiveFunctions'; - -// Register Chart.js components -Chart.register(...registerables); - -const ComparisonPage = () => { - const [selectedAlgorithms, setSelectedAlgorithms] = useState([]); - const [selectedFunction, setSelectedFunction] = useState('sphere'); - const [numIterations, setNumIterations] = useState(100); - const [populationSize, setPopulationSize] = useState(30); - const [numVariables, setNumVariables] = useState(2); - const [comparisonResults, setComparisonResults] = useState(null); - const [isComparing, setIsComparing] = useState(false); - - const handleAlgorithmChange = (e) => { - const value = e.target.value; - if (e.target.checked) { - setSelectedAlgorithms([...selectedAlgorithms, value]); - } else { - setSelectedAlgorithms(selectedAlgorithms.filter(algo => algo !== value)); - } - }; - - const handleFunctionChange = (e) => { - setSelectedFunction(e.target.value); - setComparisonResults(null); - }; - - const handleRunComparison = () => { - if (selectedAlgorithms.length === 0) { - alert('Please select at least one algorithm to compare'); - return; - } - - setIsComparing(true); - - // Simulate algorithm comparison (in a real app, this would call the actual algorithms) - setTimeout(() => { - const iterations = Array.from({ length: numIterations }, (_, i) => i + 1); - - // Generate mock comparison results - const results = selectedAlgorithms.map(algoId => { - const algorithm = algorithmData.find(a => a.id === algoId); - - // Generate different convergence patterns for different algorithm types - let bestScores; - if (algorithm.category === 'Metaphor-free') { - bestScores = iterations.map(i => 10 / (i * 0.15 + 1) + Math.random() * 0.3); - } else if (algorithm.category === 'Teaching-Learning') { - bestScores = iterations.map(i => 10 / (i * 0.12 + 1) + Math.random() * 0.4); - } else if (algorithm.category === 'Opposition-based') { - bestScores = iterations.map(i => 10 / (i * 0.18 + 1) + Math.random() * 0.2); - } else { - bestScores = iterations.map(i => 10 / (i * 0.1 + 1) + Math.random() * 0.5); - } - - return { - algorithmId: algoId, - algorithmName: algorithm.shortName, - bestScores, - finalBestScore: bestScores[bestScores.length - 1], - bestSolution: Array(numVariables).fill(0).map(() => Math.random() * 0.2 - 0.1), - executionTime: Math.random() * 1000 + 500 - }; - }); - - setComparisonResults({ - iterations, - results - }); - - setIsComparing(false); - }, 2000); - }; - - const chartData = comparisonResults ? { - labels: comparisonResults.iterations, - datasets: comparisonResults.results.map((result, index) => { - // Generate a color based on index - const hue = (index * 137) % 360; - const color = `hsl(${hue}, 70%, 50%)`; - - return { - label: result.algorithmName, - data: result.bestScores, - fill: false, - backgroundColor: color, - borderColor: color, - tension: 0.1 - }; - }) - } : null; - - const chartOptions = { - responsive: true, - plugins: { - title: { - display: true, - text: 'Convergence Comparison' - }, - tooltip: { - mode: 'index', - intersect: false, - }, - legend: { - position: 'top', - } - }, - scales: { - x: { - display: true, - title: { - display: true, - text: 'Iteration' - } - }, - y: { - display: true, - title: { - display: true, - text: 'Objective Function Value' - }, - min: 0 - } - } - }; - - return ( - -

Algorithm Comparison

- - - - - Comparison Settings - -
- - Select Algorithms to Compare -
- {algorithmData.map(algorithm => ( - - ))} -
-
- - - Objective Function - - {objectiveFunctions.map(func => ( - - ))} - - - - - Number of Iterations: {numIterations} - setNumIterations(parseInt(e.target.value))} - /> - - - - Population Size: {populationSize} - setPopulationSize(parseInt(e.target.value))} - /> - - - - Number of Variables: {numVariables} - setNumVariables(parseInt(e.target.value))} - disabled={selectedFunction === 'beale' || selectedFunction === 'himmelblau' || selectedFunction === 'constrained'} - /> - {(selectedFunction === 'beale' || selectedFunction === 'himmelblau' || selectedFunction === 'constrained') && ( - - This function is fixed at 2 dimensions. - - )} - - - -
-
-
- - - - {comparisonResults ? ( - <> - - Convergence Comparison - - - - - - - Comparison Results - - - - - - - - - - - - {comparisonResults.results - .sort((a, b) => a.finalBestScore - b.finalBestScore) - .map((result, index) => ( - - - - - - - )) - } - -
AlgorithmFinal Best ValueExecution Time (ms)Rank
{result.algorithmName}{result.finalBestScore.toFixed(6)}{result.executionTime.toFixed(2)}{index + 1}
- -
-
Analysis
-

- Based on the comparison results, {comparisonResults.results.sort((a, b) => a.finalBestScore - b.finalBestScore)[0].algorithmName} - performed the best on the {objectiveFunctions.find(f => f.id === selectedFunction).name} with a final objective value - of {comparisonResults.results.sort((a, b) => a.finalBestScore - b.finalBestScore)[0].finalBestScore.toFixed(6)}. -

-

- The convergence curve shows how each algorithm's performance improved over iterations. Steeper curves indicate - faster convergence to the optimal solution. -

-
-
-
- - ) : ( - - -

Select algorithms and run comparison to see results

-

- The comparison will show convergence curves and performance metrics for each selected algorithm. -

-
-
- )} - -
-
- ); -}; - -export default ComparisonPage; diff --git a/frontend/src/pages/HomePage.js b/frontend/src/pages/HomePage.js deleted file mode 100644 index 26400fd..0000000 --- a/frontend/src/pages/HomePage.js +++ /dev/null @@ -1,87 +0,0 @@ -import React from 'react'; -import { Container, Row, Col, Card, Button, Badge } from 'react-bootstrap'; -import { Link } from 'react-router-dom'; -import algorithmData from '../utils/algorithmData'; - -const HomePage = () => { - // Group algorithms by category - const categorizedAlgorithms = algorithmData.reduce((acc, algorithm) => { - const category = algorithm.category || 'Other'; - if (!acc[category]) { - acc[category] = []; - } - acc[category].push(algorithm); - return acc; - }, {}); - - return ( - -
-

Optimization Algorithms Visualizer

-

- Interactive visualizations of optimization algorithms developed by Prof. R.V. Rao -

-
- - - - - -

About This Project

-

- This interactive platform allows you to explore and visualize various optimization algorithms - developed by Prof. R.V. Rao. These algorithms are designed to solve both constrained and - unconstrained optimization problems without relying on metaphors or algorithm-specific parameters. -

-

- You can visualize how each algorithm works, compare their performance on different test functions, - and learn about the research papers where they were introduced. -

-
- - -
-
-
- -
- - {Object.entries(categorizedAlgorithms).map(([category, algorithms]) => ( -
-

{category} Algorithms

- - {algorithms.map(algorithm => ( - - - - {algorithm.shortName} - {algorithm.name} - {algorithm.description.substring(0, 150)}... -
- {algorithm.year} - {algorithm.parameters === "No algorithm-specific parameters" ? "Parameter-free" : "Parameterized"} -
-
- "{algorithm.paperTitle}" - {algorithm.authors} ({algorithm.year}) -
-
- - - -
- - ))} -
-
- ))} -
- ); -}; - -export default HomePage; diff --git a/frontend/src/pages/PapersPage.js b/frontend/src/pages/PapersPage.js deleted file mode 100644 index f19dd37..0000000 --- a/frontend/src/pages/PapersPage.js +++ /dev/null @@ -1,139 +0,0 @@ -import React, { useState } from 'react'; -import { Container, Row, Col, Card, Form, Button, Badge } from 'react-bootstrap'; -import { Link } from 'react-router-dom'; -import algorithmData from '../utils/algorithmData'; - -const PapersPage = () => { - const [searchTerm, setSearchTerm] = useState(''); - const [selectedCategory, setSelectedCategory] = useState('all'); - const [selectedYear, setSelectedYear] = useState('all'); - - // Extract unique categories and years - const categories = ['all', ...new Set(algorithmData.map(algo => algo.category))]; - const years = ['all', ...new Set(algorithmData.map(algo => algo.year))].sort((a, b) => b - a); - - // Filter papers based on search term and filters - const filteredPapers = algorithmData.filter(paper => { - const matchesSearch = searchTerm === '' || - paper.paperTitle.toLowerCase().includes(searchTerm.toLowerCase()) || - paper.authors.toLowerCase().includes(searchTerm.toLowerCase()) || - paper.journal.toLowerCase().includes(searchTerm.toLowerCase()); - - const matchesCategory = selectedCategory === 'all' || paper.category === selectedCategory; - const matchesYear = selectedYear === 'all' || paper.year === selectedYear; - - return matchesSearch && matchesCategory && matchesYear; - }); - - return ( - -

Research Papers

- - - - - setSearchTerm(e.target.value)} - className="search-input" - /> - - - - setSelectedCategory(e.target.value)} - > - {categories.map(category => ( - - ))} - - - - setSelectedYear(e.target.value)} - > - {years.map(year => ( - - ))} - - - - - {filteredPapers.length > 0 ? ( - - {filteredPapers.map(paper => ( - - - -
- {paper.year} - {paper.category} -
- {paper.shortName} - - {paper.journal} - - - "{paper.paperTitle}" - - - {paper.authors} - -
- - -
-
-
- - ))} -
- ) : ( -
-

No papers found matching your criteria

-

Try adjusting your search terms or filters

- -
- )} - -
-

About the Research

-

- The optimization algorithms showcased in this application are based on the groundbreaking research - conducted by Prof. R.V. Rao and his colleagues. These algorithms represent significant advancements - in the field of optimization, offering efficient solutions for both constrained and unconstrained - optimization problems. -

-

- Many of these algorithms are metaphor-free and parameter-less, making them particularly attractive - for real-world applications where parameter tuning can be challenging. The research spans over a - decade, with continuous improvements and innovations in optimization techniques. -

-

- For comprehensive understanding of these algorithms, we recommend referring to the original research - papers linked above. -

-
-
- ); -}; - -export default PapersPage; diff --git a/frontend/src/utils/algorithmData.js b/frontend/src/utils/algorithmData.js deleted file mode 100644 index 677fc29..0000000 --- a/frontend/src/utils/algorithmData.js +++ /dev/null @@ -1,394 +0,0 @@ -const algorithmData = [ - { - id: "bmr", - name: "BMR (Best-Mean-Random) Algorithm", - shortName: "BMR", - description: "The BMR algorithm is a simple, metaphor-free optimization algorithm that uses the best solution, mean solution, and a random solution to guide the search process. It is designed to solve both constrained and unconstrained optimization problems efficiently.", - paperTitle: "BMR and BWR: Two simple metaphor-free optimization algorithms for solving real-life non-convex constrained and unconstrained problems", - authors: "Ravipudi Venkata Rao and Ravikumar Shah", - year: "2024", - journal: "arXiv:2407.11149v2", - link: "https://arxiv.org/abs/2407.11149", - pseudocode: `Initialize population randomly within bounds -For each iteration: - Evaluate fitness of all solutions - Identify best solution and calculate mean solution - For each solution: - r1, r2, r3, r4 = random numbers between 0 and 1 - T = random choice between 1 and 2 - If r4 > 0.5: - Update solution: solution += r1 * (best_solution - T * mean_solution) + r2 * (best_solution - random_solution) - Else: - Perform random exploration - Clip solutions to stay within bounds -Return best solution found`, - category: "Metaphor-free", - parameters: "No algorithm-specific parameters", - applications: ["Engineering design", "Manufacturing optimization", "Resource allocation"] - }, - { - id: "bwr", - name: "BWR (Best-Worst-Random) Algorithm", - shortName: "BWR", - description: "The BWR algorithm is a simple, metaphor-free optimization algorithm that uses the best solution, worst solution, and a random solution to guide the search process. It effectively balances exploration and exploitation for both constrained and unconstrained optimization problems.", - paperTitle: "BMR and BWR: Two simple metaphor-free optimization algorithms for solving real-life non-convex constrained and unconstrained problems", - authors: "Ravipudi Venkata Rao and Ravikumar Shah", - year: "2024", - journal: "arXiv:2407.11149v2", - link: "https://arxiv.org/abs/2407.11149", - pseudocode: `Initialize population randomly within bounds -For each iteration: - Evaluate fitness of all solutions - Identify best solution and worst solution - For each solution: - r1, r2, r3, r4 = random numbers between 0 and 1 - T = random choice between 1 and 2 - If r4 > 0.5: - Update solution: solution += r1 * (best_solution - T * random_solution) - r2 * (worst_solution - random_solution) - Else: - Perform random exploration - Clip solutions to stay within bounds -Return best solution found`, - category: "Metaphor-free", - parameters: "No algorithm-specific parameters", - applications: ["Engineering design", "Manufacturing optimization", "Resource allocation"] - }, - { - id: "jaya", - name: "Jaya Algorithm", - shortName: "Jaya", - description: "Jaya is a simple, parameter-free optimization algorithm that always tries to move toward the best solution and away from the worst solution. The name 'Jaya' means 'victory' in Sanskrit, symbolizing the algorithm's tendency to move towards success and away from failure.", - paperTitle: "Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems", - authors: "R.V. Rao", - year: "2016", - journal: "International Journal of Industrial Engineering Computations, 7(1), 19-34", - link: "https://doi.org/10.5267/j.ijiec.2015.8.004", - pseudocode: `Initialize population randomly within bounds -For each iteration: - Evaluate fitness of all solutions - Identify best solution and worst solution - For each solution: - r1, r2 = random vectors between 0 and 1 - Update solution: solution += r1 * (best_solution - |solution|) - r2 * (worst_solution - |solution|) - Clip solutions to stay within bounds -Return best solution found`, - category: "Metaphor-free", - parameters: "No algorithm-specific parameters", - applications: ["Mechanical design", "Thermal system design", "Manufacturing process optimization"] - }, - { - id: "rao1", - name: "Rao-1 Algorithm", - shortName: "Rao-1", - description: "Rao-1 is a simple, metaphor-free optimization algorithm that uses the best solution to guide the search. It employs a comparison-based approach with another randomly selected solution to determine the update direction.", - paperTitle: "Rao algorithms: Three metaphor-less simple algorithms for solving optimization problems", - authors: "R.V. Rao", - year: "2020", - journal: "International Journal of Industrial Engineering Computations, 11(2), 193-212", - link: "https://doi.org/10.5267/j.ijiec.2019.8.003", - pseudocode: `Initialize population randomly within bounds -For each iteration: - Evaluate fitness of all solutions - Identify best solution - For each solution: - r = random number between 0 and 1 - Randomly select another solution - If current solution is worse than random solution: - Update solution: solution += r * (best_solution - |solution|) + r * (random_solution - |solution|) - Else: - Update solution: solution += r * (best_solution - |solution|) - r * (random_solution - |solution|) - Clip solutions to stay within bounds -Return best solution found`, - category: "Metaphor-free", - parameters: "No algorithm-specific parameters", - applications: ["Engineering design", "Manufacturing optimization", "Resource allocation"] - }, - { - id: "rao2", - name: "Rao-2 Algorithm", - shortName: "Rao-2", - description: "Rao-2 is a simple, metaphor-free optimization algorithm that uses the best and worst solutions to guide the search. It employs a comparison-based approach with another randomly selected solution to determine the update direction.", - paperTitle: "Rao algorithms: Three metaphor-less simple algorithms for solving optimization problems", - authors: "R.V. Rao", - year: "2020", - journal: "International Journal of Industrial Engineering Computations, 11(2), 193-212", - link: "https://doi.org/10.5267/j.ijiec.2019.8.003", - pseudocode: `Initialize population randomly within bounds -For each iteration: - Evaluate fitness of all solutions - Identify best solution and worst solution - For each solution: - r = random number between 0 and 1 - Randomly select another solution - If current solution is worse than random solution: - Update solution: solution += r * (best_solution - |solution|) - r * (worst_solution - |solution|) + r * (random_solution - |solution|) - Else: - Update solution: solution += r * (best_solution - |solution|) - r * (worst_solution - |solution|) - r * (random_solution - |solution|) - Clip solutions to stay within bounds -Return best solution found`, - category: "Metaphor-free", - parameters: "No algorithm-specific parameters", - applications: ["Engineering design", "Manufacturing optimization", "Resource allocation"] - }, - { - id: "rao3", - name: "Rao-3 Algorithm", - shortName: "Rao-3", - description: "Rao-3 is a simple, metaphor-free optimization algorithm that uses the best solution and a phase factor to guide the search. It introduces a phase factor that depends on the current iteration to balance exploration and exploitation.", - paperTitle: "Rao algorithms: Three metaphor-less simple algorithms for solving optimization problems", - authors: "R.V. Rao", - year: "2020", - journal: "International Journal of Industrial Engineering Computations, 11(2), 193-212", - link: "https://doi.org/10.5267/j.ijiec.2019.8.003", - pseudocode: `Initialize population randomly within bounds -For each iteration: - Evaluate fitness of all solutions - Identify best solution - Calculate phase factor = (1 - current_iteration/max_iterations) - For each solution: - r = random number between 0 and 1 - Update solution: solution += r * (best_solution - phase_factor * |solution|) - Clip solutions to stay within bounds -Return best solution found`, - category: "Metaphor-free", - parameters: "No algorithm-specific parameters", - applications: ["Engineering design", "Manufacturing optimization", "Resource allocation"] - }, - { - id: "tlbo", - name: "Teaching-Learning-Based Optimization (TLBO) Algorithm", - shortName: "TLBO", - description: "TLBO is a parameter-free algorithm inspired by the teaching-learning process in a classroom. It consists of two phases: Teacher Phase (learning from the teacher) and Learner Phase (learning through interaction between learners).", - paperTitle: "Teaching-Learning-Based Optimization: An optimization method for continuous non-linear large scale problems", - authors: "R.V. Rao, V.J. Savsani, D.P. Vakharia", - year: "2012", - journal: "Information Sciences, 183(1), 1-15", - link: "https://doi.org/10.1016/j.ins.2011.08.006", - pseudocode: `Initialize population randomly within bounds -For each iteration: - // Teacher Phase - Evaluate fitness of all solutions - Identify best solution (teacher) - Calculate mean of each design variable - For each solution: - Teaching factor = random choice between 1 and 2 - Update solution based on difference between mean and teacher - - // Learner Phase - For each solution: - Randomly select another solution - If current solution is better than selected solution: - Move current solution towards selected solution - Else: - Move current solution away from selected solution - - Clip solutions to stay within bounds -Return best solution found`, - category: "Teaching-Learning", - parameters: "No algorithm-specific parameters", - applications: ["Mechanical design", "Thermal system design", "Manufacturing process optimization", "Structural optimization"] - }, - { - id: "qojaya", - name: "Quasi-Oppositional Jaya (QOJAYA) Algorithm", - shortName: "QOJAYA", - description: "QOJAYA enhances the standard Jaya algorithm by incorporating quasi-oppositional learning to improve convergence speed and solution quality. It uses the concept of quasi-opposite points to enhance the diversity of the population.", - paperTitle: "Jaya: An Advanced Optimization Algorithm and its Engineering Applications", - authors: "R.V. Rao", - year: "2019", - journal: "Springer International Publishing", - link: "https://doi.org/10.1007/978-3-030-31503-0", - pseudocode: `Initialize population randomly within bounds -Generate quasi-opposite population -Combine and select best solutions -For each iteration: - Evaluate fitness of all solutions - Identify best solution and worst solution - For each solution: - r1, r2 = random vectors between 0 and 1 - Update solution: solution += r1 * (best_solution - |solution|) - r2 * (worst_solution - |solution|) - Generate quasi-opposite solutions for a portion of the population - Combine and select best solutions - Clip solutions to stay within bounds -Return best solution found`, - category: "Opposition-based", - parameters: "Jumping rate (probability of applying quasi-opposition)", - applications: ["Welding process optimization", "Manufacturing process optimization", "Engineering design"] - }, - { - id: "tlbo_elitism", - name: "TLBO with Elitism Algorithm", - shortName: "TLBO-Elitism", - description: "This version of TLBO incorporates elitism to preserve the best solutions across generations, improving convergence and solution quality. It ensures that the best solutions are not lost during the optimization process.", - paperTitle: "Improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems", - authors: "R.V. Rao, V. Patel", - year: "2013", - journal: "Scientia Iranica, 20(3), 710-720", - link: "https://doi.org/10.1016/j.scient.2012.12.005", - pseudocode: `Initialize population randomly within bounds -For each iteration: - // Teacher Phase - Evaluate fitness of all solutions - Identify best solution (teacher) - Calculate mean of each design variable - For each solution: - Teaching factor = random choice between 1 and 2 - Generate new solution based on difference between mean and teacher - Accept new solution if better - - // Learner Phase - For each solution: - Randomly select another solution - Generate new solution based on comparison - Accept new solution if better - - // Elitism - Combine original and new populations - Select best solutions for next generation - - Clip solutions to stay within bounds -Return best solution found`, - category: "Teaching-Learning", - parameters: "No algorithm-specific parameters", - applications: ["Mechanical design", "Thermal system design", "Manufacturing process optimization", "Structural optimization"] - }, - { - id: "jcro", - name: "Jaya-based Chemical Reaction Optimization (JCRO) Algorithm", - shortName: "JCRO", - description: "JCRO is a hybrid algorithm combining Jaya with Chemical Reaction Optimization principles for enhanced exploration and exploitation balance. It uses different types of chemical reactions to diversify the search process.", - paperTitle: "Optimization of welding processes using quasi-oppositional-based Jaya algorithm", - authors: "R.V. Rao, D.P. Rai", - year: "2017", - journal: "Journal of Mechanical Science and Technology, 31(5), 2513-2525", - link: "https://doi.org/10.1007/s12206-017-0454-0", - pseudocode: `Initialize population randomly within bounds -For each iteration: - Evaluate fitness of all solutions - Identify best solution and worst solution - For each solution: - Randomly select a reaction type (synthesis, decomposition, displacement, redox) - Based on reaction type: - Synthesis: Combine two solutions to form a new one - Decomposition: Split a solution into two new ones - Displacement: Modify a solution using Jaya principles - Redox: Apply both Jaya and opposition-based learning - Select best solutions for next generation - Clip solutions to stay within bounds -Return best solution found`, - category: "Hybrid", - parameters: "Reaction type probabilities", - applications: ["Welding process optimization", "Manufacturing process optimization", "Engineering design"] - }, - { - id: "gotlbo", - name: "Generalized Oppositional Teaching-Learning-Based Optimization (GOTLBO) Algorithm", - shortName: "GOTLBO", - description: "GOTLBO incorporates oppositional-based learning into TLBO for faster convergence and better exploration. It uses the concept of opposite points to enhance the diversity of the population in both teacher and learner phases.", - paperTitle: "An improved teaching-learning-based optimization algorithm for solving unconstrained optimization problems", - authors: "R.V. Rao, V. Patel", - year: "2014", - journal: "Scientia Iranica, 21(3), 670-688", - link: "https://doi.org/10.1016/j.scient.2013.09.007", - pseudocode: `Initialize population randomly within bounds -Generate opposite population -Combine and select best solutions -For each iteration: - // Teacher Phase with Opposition - Evaluate fitness of all solutions - Identify best solution (teacher) - Calculate mean of each design variable - For each solution: - Teaching factor = random choice between 1 and 2 - Generate new solution based on difference between mean and teacher - Generate opposite solution with some probability - Select better solution - - // Learner Phase with Opposition - For each solution: - Randomly select another solution - Generate new solution based on comparison - Generate opposite solution with some probability - Select better solution - - Clip solutions to stay within bounds -Return best solution found`, - category: "Opposition-based", - parameters: "Jumping rate (probability of applying opposition)", - applications: ["Mechanical design", "Thermal system design", "Manufacturing process optimization", "Structural optimization"] - }, - { - id: "itlbo", - name: "Improved Teaching-Learning-Based Optimization (ITLBO) Algorithm", - shortName: "ITLBO", - description: "ITLBO enhances TLBO by modifying the teacher phase and learner phase for better convergence and solution quality. It introduces adaptive teaching factor and modified update equations.", - paperTitle: "An elitist teaching-learning-based optimization algorithm for solving complex constrained optimization problems", - authors: "R.V. Rao, V. Patel", - year: "2013", - journal: "International Journal of Industrial Engineering Computations, 4(1), 1-14", - link: "https://doi.org/10.5267/j.ijiec.2012.09.001", - pseudocode: `Initialize population randomly within bounds -For each iteration: - // Modified Teacher Phase - Evaluate fitness of all solutions - Identify best solution (teacher) - Calculate mean of each design variable - For each solution: - Calculate adaptive teaching factor based on fitness - Generate new solution with modified update equation - Accept new solution if better - - // Modified Learner Phase - For each solution: - Randomly select another solution - Generate new solution with modified update equation - Accept new solution if better - - // Elitism - Preserve best solutions for next generation - - Clip solutions to stay within bounds -Return best solution found`, - category: "Teaching-Learning", - parameters: "No algorithm-specific parameters", - applications: ["Mechanical design", "Thermal system design", "Manufacturing process optimization", "Structural optimization"] - }, - { - id: "mo_tlbo", - name: "Multi-objective Teaching-Learning-Based Optimization (MO-TLBO) Algorithm", - shortName: "MO-TLBO", - description: "MO-TLBO extends TLBO to handle multi-objective optimization problems using Pareto dominance and crowding distance for selection. It maintains an external archive of non-dominated solutions.", - paperTitle: "Multi-objective TLBO algorithm for optimization of modern machining processes", - authors: "R.V. Rao, V.D. Kalyankar", - year: "2013", - journal: "Advances in Manufacturing, 1(1), 1-9", - link: "https://doi.org/10.1007/s40436-013-0007-4", - pseudocode: `Initialize population randomly within bounds -Initialize empty external archive -For each iteration: - // Teacher Phase for Multi-objective - Evaluate all objectives for all solutions - Update external archive with non-dominated solutions - Identify teacher for each objective - For each solution: - Generate new solution based on teachers - Evaluate dominance and update if better - - // Learner Phase for Multi-objective - For each solution: - Randomly select another solution - Generate new solution based on dominance comparison - Evaluate dominance and update if better - - Update external archive - Apply crowding distance sorting if archive exceeds limit - - Clip solutions to stay within bounds -Return Pareto front from external archive`, - category: "Multi-objective", - parameters: "Archive size", - applications: ["Machining process optimization", "Engineering design with multiple objectives", "Multi-criteria decision making"] - } -]; - -export default algorithmData; diff --git a/frontend/src/utils/objectiveFunctions.js b/frontend/src/utils/objectiveFunctions.js deleted file mode 100644 index 06c7895..0000000 --- a/frontend/src/utils/objectiveFunctions.js +++ /dev/null @@ -1,233 +0,0 @@ -const objectiveFunctions = [ - { - id: "sphere", - name: "Sphere Function", - formula: "f(x) = \\sum_{i=1}^{n} x_i^2", - description: "The Sphere function is one of the simplest unimodal test functions. It is continuous, convex, and differentiable. The global minimum is at the origin with a function value of 0.", - bounds: [-5.12, 5.12], - globalMinimum: { - position: "Origin (all zeros)", - value: 0 - }, - code: `function sphere(x) { - return x.reduce((sum, xi) => sum + xi * xi, 0); -}`, - visualization: "3D surface and contour plot for 2D version", - difficulty: "Easy", - properties: ["Unimodal", "Separable", "Convex", "Differentiable"] - }, - { - id: "rastrigin", - name: "Rastrigin Function", - formula: "f(x) = 10n + \\sum_{i=1}^{n} [x_i^2 - 10\\cos(2\\pi x_i)]", - description: "The Rastrigin function is a non-convex, multimodal function with many local minima arranged in a regular pattern. It is a challenging test function due to its large search space and numerous local minima.", - bounds: [-5.12, 5.12], - globalMinimum: { - position: "Origin (all zeros)", - value: 0 - }, - code: `function rastrigin(x) { - const n = x.length; - return 10 * n + x.reduce((sum, xi) => sum + (xi * xi - 10 * Math.cos(2 * Math.PI * xi)), 0); -}`, - visualization: "3D surface and contour plot for 2D version", - difficulty: "Hard", - properties: ["Multimodal", "Separable", "Non-convex", "Differentiable"] - }, - { - id: "ackley", - name: "Ackley Function", - formula: "f(x) = -20\\exp\\left(-0.2\\sqrt{\\frac{1}{n}\\sum_{i=1}^{n}x_i^2}\\right) - \\exp\\left(\\frac{1}{n}\\sum_{i=1}^{n}\\cos(2\\pi x_i)\\right) + 20 + e", - description: "The Ackley function is a widely used multimodal test function. It has a global minimum at the origin surrounded by many local minima. The function has an almost flat outer region and a large hole at the center.", - bounds: [-32.768, 32.768], - globalMinimum: { - position: "Origin (all zeros)", - value: 0 - }, - code: `function ackley(x) { - const n = x.length; - const sum1 = x.reduce((sum, xi) => sum + xi * xi, 0); - const sum2 = x.reduce((sum, xi) => sum + Math.cos(2 * Math.PI * xi), 0); - - const term1 = -20 * Math.exp(-0.2 * Math.sqrt(sum1 / n)); - const term2 = -Math.exp(sum2 / n); - - return term1 + term2 + 20 + Math.E; -}`, - visualization: "3D surface and contour plot for 2D version", - difficulty: "Medium", - properties: ["Multimodal", "Non-separable", "Non-convex", "Differentiable"] - }, - { - id: "rosenbrock", - name: "Rosenbrock Function", - formula: "f(x) = \\sum_{i=1}^{n-1} [100(x_{i+1} - x_i^2)^2 + (x_i - 1)^2]", - description: "The Rosenbrock function, also known as the Valley or Banana function, is a non-convex function used as a performance test problem for optimization algorithms. The global minimum is inside a long, narrow, parabolic shaped flat valley.", - bounds: [-2.048, 2.048], - globalMinimum: { - position: "All ones", - value: 0 - }, - code: `function rosenbrock(x) { - let sum = 0; - for (let i = 0; i < x.length - 1; i++) { - sum += 100 * Math.pow(x[i+1] - x[i] * x[i], 2) + Math.pow(x[i] - 1, 2); - } - return sum; -}`, - visualization: "3D surface and contour plot for 2D version", - difficulty: "Hard", - properties: ["Unimodal", "Non-separable", "Non-convex", "Differentiable"] - }, - { - id: "beale", - name: "Beale Function", - formula: "f(x) = (1.5 - x_1 + x_1 x_2)^2 + (2.25 - x_1 + x_1 x_2^2)^2 + (2.625 - x_1 + x_1 x_2^3)^2", - description: "The Beale function is a multimodal test function with sharp peaks at the corners of the input domain. It has a global minimum in a relatively flat valley.", - bounds: [-4.5, 4.5], - globalMinimum: { - position: "(3, 0.5)", - value: 0 - }, - code: `function beale(x) { - const x1 = x[0]; - const x2 = x[1]; - - const term1 = Math.pow(1.5 - x1 + x1 * x2, 2); - const term2 = Math.pow(2.25 - x1 + x1 * x2 * x2, 2); - const term3 = Math.pow(2.625 - x1 + x1 * x2 * x2 * x2, 2); - - return term1 + term2 + term3; -}`, - visualization: "3D surface and contour plot", - difficulty: "Medium", - properties: ["Multimodal", "Non-separable", "Non-convex", "Differentiable"], - dimensions: 2 - }, - { - id: "himmelblau", - name: "Himmelblau's Function", - formula: "f(x) = (x_1^2 + x_2 - 11)^2 + (x_1 + x_2^2 - 7)^2", - description: "Himmelblau's function is a multi-modal function used to test the performance of optimization algorithms. It has four identical local minima.", - bounds: [-5, 5], - globalMinimum: { - position: "Multiple: (3, 2), (-2.805118, 3.131312), (-3.779310, -3.283186), (3.584428, -1.848126)", - value: 0 - }, - code: `function himmelblau(x) { - const x1 = x[0]; - const x2 = x[1]; - - const term1 = Math.pow(x1 * x1 + x2 - 11, 2); - const term2 = Math.pow(x1 + x2 * x2 - 7, 2); - - return term1 + term2; -}`, - visualization: "3D surface and contour plot", - difficulty: "Medium", - properties: ["Multimodal", "Non-separable", "Non-convex", "Differentiable"], - dimensions: 2 - }, - { - id: "griewank", - name: "Griewank Function", - formula: "f(x) = 1 + \\frac{1}{4000}\\sum_{i=1}^{n}x_i^2 - \\prod_{i=1}^{n}\\cos\\left(\\frac{x_i}{\\sqrt{i}}\\right)", - description: "The Griewank function has many widespread local minima, which are regularly distributed. The complexity is due to the product term that creates numerous local minima.", - bounds: [-600, 600], - globalMinimum: { - position: "Origin (all zeros)", - value: 0 - }, - code: `function griewank(x) { - let sum = 0; - let product = 1; - - for (let i = 0; i < x.length; i++) { - sum += (x[i] * x[i]) / 4000; - product *= Math.cos(x[i] / Math.sqrt(i + 1)); - } - - return 1 + sum - product; -}`, - visualization: "3D surface and contour plot for 2D version", - difficulty: "Hard", - properties: ["Multimodal", "Non-separable", "Non-convex", "Differentiable"] - }, - { - id: "levy", - name: "Lévy Function", - formula: "f(x) = \\sin^2(\\pi w_1) + \\sum_{i=1}^{n-1}(w_i-1)^2[1+10\\sin^2(\\pi w_i+1)] + (w_n-1)^2[1+\\sin^2(2\\pi w_n)] \\quad \\text{where } w_i = 1 + \\frac{x_i - 1}{4}", - description: "The Lévy function is a multimodal, non-separable test function with many local minima. It is challenging for optimization algorithms due to its oscillatory behavior.", - bounds: [-10, 10], - globalMinimum: { - position: "All ones", - value: 0 - }, - code: `function levy(x) { - const n = x.length; - let result = 0; - - // Transform variables - const w = x.map(xi => 1 + (xi - 1) / 4); - - // First term - result += Math.pow(Math.sin(Math.PI * w[0]), 2); - - // Middle terms - for (let i = 0; i < n - 1; i++) { - result += Math.pow(w[i] - 1, 2) * (1 + 10 * Math.pow(Math.sin(Math.PI * w[i] + 1), 2)); - } - - // Last term - result += Math.pow(w[n-1] - 1, 2) * (1 + Math.pow(Math.sin(2 * Math.PI * w[n-1]), 2)); - - return result; -}`, - visualization: "3D surface and contour plot for 2D version", - difficulty: "Hard", - properties: ["Multimodal", "Non-separable", "Non-convex", "Differentiable"] - }, - { - id: "constrained", - name: "Constrained Optimization Problem", - formula: "f(x) = x_1^2 + x_2^2 + x_1 x_2 - 10x_1 - 15x_2", - description: "This is a constrained optimization problem with a nonlinear objective function and several constraints, including a circular constraint and bounds on variables.", - bounds: [-10, 10], - constraints: [ - "x_1^2 + x_2^2 ≤ 25", - "x_1 ≤ 5", - "x_2 ≤ 5" - ], - globalMinimum: { - position: "Depends on constraints", - value: "Depends on constraints" - }, - code: `function constrained(x) { - const x1 = x[0]; - const x2 = x[1]; - - return x1 * x1 + x2 * x2 + x1 * x2 - 10 * x1 - 15 * x2; -} - -function constraint1(x) { - const x1 = x[0]; - const x2 = x[1]; - - return 25 - (x1 * x1 + x2 * x2); // Must be >= 0 -} - -function constraint2(x) { - return 5 - x[0]; // Must be >= 0 -} - -function constraint3(x) { - return 5 - x[1]; // Must be >= 0 -}`, - visualization: "3D surface with constraint boundaries", - difficulty: "Hard", - properties: ["Constrained", "Non-separable", "Convex", "Differentiable"], - dimensions: 2 - } -]; - -export default objectiveFunctions;