Solving TSP with Genetic Algorithm in MATLAB

The Traveling Salesman Problem (TSP) represents a classic combinatorial optimization challenge where a salesman must find the shortest possible route visiting all given cities exactly once and returning to the origin city. Various methodologies exist for addressing TSP, with genetic algorithms standing out as one of the most effective approaches. Genetic algorithms simulate natural evolution processes through computational optimization techniques, employing genetic operations like selection, crossover, and mutation to progressively evolve solutions toward optimality. In MATLAB implementation, this typically involves defining key components: chromosome representation (city permutation encoding), fitness function calculation (total route distance minimization), and genetic operator customization. Key MATLAB implementation aspects include: - Population initialization using randperm() for random route generation - Fitness evaluation through cumulative distance calculation between consecutive cities - Tournament selection or roulette wheel selection for parent chromosome selection - Ordered crossover or PMX crossover for offspring generation while preserving city visits - Swap mutation or inversion mutation for maintaining population diversity The MATLAB Global Optimization Toolbox provides robust genetic algorithm functions (ga) that can be customized for TSP-specific constraints. Implementation typically requires creating a custom fitness function that computes total tour distance and configuring algorithm parameters like population size, crossover rate, and stopping criteria. This approach demonstrates exceptional performance in finding near-optimal solutions for complex TSP instances, making it remarkably effective for practical applications!