Discrete Particle Swarm Optimization for TSP based on Neighborhood
نویسنده
چکیده
Particle swarm optimization (PSO) is a kind of evolutionary algorithm to find optimal solutions for continuous optimization problems. Updating kinetic equations for particle swarm optimization algorithm are improved to solve traveling salesman problem (TSP) based on problem characteristics and discrete variable. Those strategies which are named heuristic factor, reversion mutant and adaptive noise factor, are designed and combined into a new hybrid discrete particle swam optimization algorithms. Experiments on low and high-dimensional data in TSPLIB show that, comparing with other hybrid discrete particle swarm (DPSO), the proposed algorithm can improve the search performance significantly no matter in convergent speed or precision.
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