Solving TSP Shortest Path Optimization Problem Using Genetic Algorithm

Solving shortest path optimization problems using Genetic Algorithm (with accompanying MATLAB source code.txt).

Genetic Algorithm is a heuristic optimization method that mimics biological evolution processes to solve shortest path problems. The algorithm employs selection, crossover, and mutation operations to evolve population solutions over generations. Key MATLAB functions include population initialization using randperm(), fitness evaluation through path distance calculation, tournament selection via randperm() indexing, ordered crossover (OX) for route recombination, and swap mutation for local improvements.

The core objective is to find the minimum total distance path connecting all given nodes exactly once. The algorithm iteratively improves route solutions by maintaining a population of candidate paths, evaluating their fitness based on total distance, and applying genetic operators to generate improved offspring populations. Convergence is achieved when the optimal path meets stopping criteria like maximum generations or fitness threshold.

This methodology finds applications in logistics planning, travel route optimization, and network design. Route optimization reduces operational costs, minimizes resource consumption, and enhances system efficiency. The MATLAB implementation demonstrates practical configuration of genetic operators parameters: population size (typically 50-100), crossover rate (0.7-0.9), and mutation rate (0.01-0.05).

The provided MATLAB source code facilitates understanding of algorithm implementation details, including distance matrix computation, elitism preservation, and convergence visualization. Users can modify the code for custom node configurations and operational constraints.