期刊
OPTIMIZATION
卷 64, 期 4, 页码 1057-1080出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/02331934.2013.793329
关键词
differential evolution; artificial fish swarm; glowworm swarm optimization; multimodal function optimization; hybrid optimization algorithm; 68W20; 68T20
资金
- National Science Foundation of China [61165015]
- Guangxi High School Science Foundation [20121ZD008]
- key lab of intelligent perception and image understanding of ministry of Education of China [IPIU01201100]
In this paper, a novel hybrid glowworm swarm optimization (HGSO) algorithm is proposed. The HGSO algorithm embeds predatory behaviour of artificial fish swarm algorithm (AFSA) into glowworm swarm optimization (GSO) algorithm and combines the GSO with differential evolution on the basis of a two-population co-evolution mechanism. In addition, to overcome the premature convergence, the local search strategy based on simulated annealing is applied to make the search of GSO approach the true optimum solution gradually. Finally, several benchmark functions show that HGSO has faster convergence efficiency and higher computational precision, and is more effective for solving constrained multi-modal function optimization problems.
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