期刊
INFORMATION SCIENCES
卷 223, 期 -, 页码 119-135出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2012.10.012
关键词
Particle Swarm Optimization (PSO); Diversity; Neighborhood search; Global optimization
资金
- Science and Technology Plan Projects of Jiangxi Provincial Education Department [GJJ12641, GJJ12633, GJJ12307]
- Youth Foundation of Nanchang Institute of Technology [2012KJ021]
- National Natural Science Foundation of China [61070008, 61165004, 61261039]
Particle swarm optimization (PSO) has shown an effective performance for solving variant benchmark and real-world optimization problems. However, it suffers from premature convergence because of quick losing of diversity. In order to enhance its performance, this paper proposes a hybrid PSO algorithm, called DNSPSO, which employs a diversity enhancing mechanism and neighborhood search strategies to achieve a trade-off between exploration and exploitation abilities. A comprehensive experimental study is conducted on a set of benchmark functions, including rotated multimodal and shifted high-dimensional problems. Comparison results show that DNSPSO obtains a promising performance on the majority of the test problems. (C) 2012 Elsevier Inc. All rights reserved.
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