期刊
SOFT COMPUTING
卷 23, 期 12, 页码 4421-4437出版社
SPRINGER
DOI: 10.1007/s00500-018-3098-9
关键词
Large-scale optimization; Particle swarm optimization; Convergence speed controller; Numerical optimization
资金
- National Natural Science Foundation of China [61370102]
- Guangdong Natural Science Funds for Distinguished Young Scholar [2014A 030306050]
- Ministry of Education-China Mobile Research Funds [MCM20160206]
- Guangdong High-level personnel of special support program [2014 TQ01X664]
Particle swarm optimization (PSO) has high convergence speed yet with its major drawback of premature convergence when solving large-scale optimization problems. We argue that it can be empowered by adaptively adjusting its convergence speed for the problems. In this paper, a convergence speed controller is proposed to improve the performance of PSO for large-scale optimization. As an additional operator of PSO, the controller is applied periodically and independently. It has two conditions and rules for adjusting the convergence speed of PSO, one for premature convergence and the other for slow convergence. The effectiveness of the PSO with convergence speed controller is evaluated by calculating the benchmark functions of CEC'2010. The numerical results indicate that the proposed controller helps PSO to keep a balance between convergence speed and swarm diversity during the optimization process. The results also support our argument that PSO can on average outperform other PSOs and cooperative coevolution methods for large-scale optimization when working with the convergence speed controller.
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