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Published in:
2014 | OriginalPaper | Chapter
A New Approach of Diversity Enhanced Particle Swarm Optimization with Neighborhood Search and Adaptive Mutation
Authors : Dang Cong Tran, Zhijian Wu, Hui Wang
Published in: Neural Information Processing
Publisher: Springer International Publishing
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Like other stochastic algorithms, particle swarm optimization algorithm (PSO) has shown a good performance over global numerical optimization. However, PSO also has a few drawbacks such as premature convergence and low convergence speed, especially on complex problem. In this paper, we present a new approach called AMPSONS in which neighborhood search, diversity mechanism and adaptive mutation were utilized. Experimental results obtained from a test on several benchmark functions showed that the performance of proposed AMPSONS algorithm is superior to five other PSO variants, namely CLPSO, AMPSO, GOPSO, DNLPSO, and DNSPSO, in terms of convergence speed and accuracy.