Hybrid Particle Swarm Optimization Algorithm Merging Simulated Annealing and Mountain-Climb Searching

Abstract:

To solve the problem of standard particle swarm optimization (PSO) easy turn to premature convergence and poor ability in local search, this paper present a hybrid particle swarm optimization algorithm merging simulated annealing (SA) and mountain-climb. During the running time, the algorithm use the pso to find the global optimal position quickly, take advantage of the Gaussian mutation and mountain-climb strategy to enhance local search ability, and combine with SA to strengthen the population diversity to enable particles to escape from local minima. Test results on several typical test functions show that this new algorithm has a significant improve in searching ability and effectively overcome the premature convergence problem.

• Periodical: Applied Mechanics and Materials (Volumes 651-653)
• Pages: 2159-2163
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.651-653.2159
• Online since: September 2014
• Authors: Jia Xing You*, Ji Li Chen, Ming Gang Dong
• Keywords: Gaussian Mutation, Mountain-Climbing, Particle Swarm Optimization (PSO), Simulated Annealing (SA)

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