期刊
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
卷 23, 期 4, 页码 718-731出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2018.2885075
关键词
Adaptive strategy; evolutionary algorithm; local search (LS); multimodal function; particle swarm optimization (PSO)
资金
- National Natural Science Foundation of China [61571336, 71372202, 71672137]
A comprehensive learning particle swarm optimizer (CLPSO) embedded with local search (LS) is proposed to pursue higher optimization performance by taking the advantages of CLPSO's strong global search capability and LS's fast convergence ability. This paper proposes an adaptive LS starting strategy by utilizing our proposed quasi-entropy index to address its key issue, i.e., when to start LS. The changes of the index as the optimization proceeds are analyzed in theory and via numerical tests. The proposed algorithm is tested on multimodal benchmark functions. Parameter sensitivity analysis is performed to demonstrate its robustness. The comparison results reveal overall higher convergence rate and accuracy than those of CLPSO, state-of-the-art particle swarm optimization variants.
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