Particle Swarm Optimization for Solving the Classic Traveling Salesman Problem (TSP)
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Resource Overview
A self-developed particle swarm optimization implementation for solving the classic TSP problem, thoroughly tested with excellent performance results, ready for practical applications
Detailed Documentation
The particle swarm optimization algorithm is commonly used to solve the classic Traveling Salesman Problem (TSP). This particular implementation was developed independently and has demonstrated excellent performance through extensive testing. The algorithm employs position and velocity updates for each particle, where positions represent potential TSP routes and velocities determine how solutions evolve toward better paths. Key features include adaptive inertia weights and social/cognitive components that balance exploration and exploitation. The implementation incorporates 2-opt local search optimization to refine solutions and uses permutation-based encoding to handle route constraints. After rigorous validation against standard TSP benchmarks, I recommend this implementation for practical applications requiring efficient route optimization.
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