Optimization system based on genetic algorithms and ant colony optimization (ACO).
This project implements an AI-based system for solving logistics and route optimization problems using evolutionary algorithms and swarm intelligence techniques.
The system focuses on finding optimal or near-optimal solutions for routing, clustering, and distance-based optimization tasks.
- Route optimization using Ant Colony Optimization (ACO)
- Genetic algorithms for solution refinement
- Clustering based on geographic coordinates
- Distance matrix computation and optimization
- Experimental evaluation of different optimization strategies
- Path optimization based on pheromone simulation
- Adaptive exploration vs exploitation balance
- Population-based optimization
- Selection, crossover, and mutation strategies
- Grouping based on latitude and longitude
- Preprocessing for optimization tasks
- Distance matrix generation
- Route optimization engine
- Clustering module
- Visualization of routes and clusters
The project includes multiple experimental approaches:
- Route optimization using ACO
- Hybrid approaches with clustering + optimization
- Evaluation of different optimization parameters
- Python
- NumPy
- Optimization algorithms (ACO, Genetic Algorithms)
- Data processing and visualization tools
This project explores:
- combinatorial optimization
- swarm intelligence
- evolutionary algorithms
- route optimization problems
- hybrid optimization strategies
- Logistics and delivery optimization
- Transportation systems
- Supply chain optimization
- Route planning systems
- Hybrid models combining ML + optimization
- Real-time route optimization
- Integration with map APIs
- Scaling to large datasets
Svetlana Rumyantseva
AI Systems Engineer