期刊
KNOWLEDGE-BASED SYSTEMS
卷 139, 期 -, 页码 23-40出版社
ELSEVIER
DOI: 10.1016/j.knosys.2017.10.011
关键词
Particle swarm optimization; Chaotic map; Dynamic weight; Optimization
资金
- National Natural Science Foundation of China [61375084, 61773242]
- Key Program of Natural Science Foundation of Shandong Province [ZR2015QZ08]
- Key Program of Scientific and Technological Innovation of Shandong Province [2017CXGC0926]
- Key Research and Development Program of Shandong Province [2017GGX30133]
Particle swarm optimization (PSO), which is inspired by social behaviors of individuals in bird swarms, is a nature-inspired and global optimization algorithm. The PSO method is easy to implement and has shown good performance for many real-world optimization tasks. However, PSO has problems with premature convergence and easy trapping into local optimum solutions. In order to overcome these deficiencies, a chaotic dynamic weight particle swarm optimization (CDW-PSO) is proposed. In the CDW-PSO algorithm, a chaotic map and dynamic weight are introduced to modify the search process. The dynamic weight is defined as a function of the fitness. The search accuracy and performance of the CDW-PSO algorithm are verified on seventeen well-known classical benchmark functions. The experimental results show that, for almost all functions, the CDW-PSO technique has superior performance compared with other nature-inspired optimizations and well-known PSO variants. Namely, the proposed algorithm of CDW-PSO has better search performance. (C) 2017 Elsevier B.V. All rights reserved.
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