Particle Swarm Optimization Survey for Traveling Salesman Problem

• Main Authors: Yen-Lin Chen, 陳彥霖
• Other Authors: 田方治
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/mvbhy9

碩士 === 國立臺北科技大學 === 工業工程與管理研究所 === 95 === Particle Swarm Optimization (PSO), a new developed heuristic algorithm, is a searching algorithm with intelligence of colony advanced by Eberhart and Kennedy in 1995. The algorithm has been proved the ability of searching that is useful in lots of field, but a few of combinatorial optimization. This study surveys PSO for solving “traveling salesman problem (TSP),” further, discusses the advantage and disadvantage in the field and tries to research the most capability of PSO for solving TSP. Applies “Composition Function,” which is advanced by Gallad and Hawary in Canada, be the main algorithm, then uses three additional methods: “heuristic initial solution,” “mutation,” and “local search” to improve its searching ability. Furthermore, test 9 different data of TSPLIB and verify the proposed additional methods that are efficient or not. In the end, named the algorithm “IPSO” and compare the result with Genetic Algorithm (GA), which is powerful for solving TSP in a lot of reference.