| Summary: | 碩士 === 中山醫學大學 === 醫學資訊學系碩士班 === 105 === In the field of searching the optimal solutions of objective functions, particle swarm optimization (PSO) can be said to be a simple but effective algorithm. Its advantages include simplicity and ease to understand and implement, but it easily leads to getting stuck in local optima. In order to maintain the original benefit and promote its performance, we propose a novel idea in this paper, which selects the best setting model of PSO based on the previous performance through a switch of PSO with multiple setting models. Experimental results show that the PSO through the scheme is better than any with its individual setting alone. In the future, PSO algorithms with a switch of multiple models will be a promising research field. In addition, the idea can be easily extended to a scheme of selecting the best from multiple optimization methods.
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