期刊
APPLIED SOFT COMPUTING
卷 13, 期 10, 页码 4047-4062出版社
ELSEVIER
DOI: 10.1016/j.asoc.2013.05.014
关键词
Evolutionary computation; Grey evolutionary analysis; Grey relational analysis; Parameter automation strategy; Particle swarm optimization
资金
- National Science Council, Taiwan, Republic of China [NSC 100-2221-E-262-002, NSC 101-2221-E-262-011]
Based on grey relational analysis, this study attempts to propose a grey evolutionary analysis (GEA) to analyze the population distribution of particle swarm optimization (PSO) during the evolutionary process. Then two GEA-based parameter automation approaches are developed. One is for the inertia weight and the other is for the acceleration coefficients. With the help of the GEA technique, the proposed parameter automation approaches would enable the inertia weight and acceleration coefficients to adapt to the evolutionary state. Such parameter automation behaviour also makes an attempt on the GEA-based PSO to perform a global search over the search space with faster convergence speed. In addition, the proposed PSO is applied to solve the optimization problems of twelve unimodal and multimodal benchmark functions for illustration. Simulation results show that the proposed GEA-based PSO could outperform the adaptive PSO, the grey PSO, and two well-known PSO variants on most of the test functions. (C) 2013 Elsevier B. V. All rights reserved.
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