Genetic Algorithm Solution for Vehicle Routing Problem (VRP)

This implementation provides a complete MATLAB solution for solving logistics distribution problems with 8 nodes using genetic algorithm methodology. The codebase includes robust validation mechanisms to ensure solution feasibility and allows customization of distance matrices and other parameters to adapt to specific problem requirements. Key algorithmic components implemented: - Chromosome representation using permutation encoding for route sequences - Fitness function calculating total route distance with penalty mechanisms for constraint violations - Tournament selection with elitism preservation - Partially matched crossover (PMX) and swap mutation operators - Convergence monitoring with generational progress tracking The solution incorporates visualization functionality that generates route plots using MATLAB's plotting capabilities, displaying node connections and path sequences for clear interpretation of distribution routes. Users can modify the distance matrix, vehicle capacity constraints, and algorithm parameters (population size, mutation rate, iteration count) through well-documented configuration sections. The implementation follows standard genetic algorithm workflow: population initialization → fitness evaluation → selection → crossover → mutation → termination check. Route optimization considers both distance minimization and constraint satisfaction, making it suitable for various VRP variants including capacitated VRP scenarios.