期刊
MATHEMATICAL PROBLEMS IN ENGINEERING
卷 2014, 期 -, 页码 -出版社
HINDAWI LTD
DOI: 10.1155/2014/936374
关键词
-
资金
- National Science Foundation of China [61165015]
- Guangxi Science Foundation [2012GXNSFDA053028]
- Guangxi High School Science Foundation [20121ZD008]
- Open Research Fund Program of Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China [IPIU01201100]
To simulate the freedom and uncertain individual behavior of krill herd, this paper introduces the opposition based learning (OBL) strategy and free search operator into krill herd optimization algorithm (KH) and proposes a novel opposition-based free search krill herd optimization algorithm (FSKH). In FSKH, each krill individual can search according to its own perception and scope of activities. The free search strategy highly encourages the individuals to escape from being trapped in local optimal solution. So the diversity and exploration ability of krill population are improved. And FSKH can achieve a better balance between local search and global search. The experiment results of fourteen benchmark functions indicate that the proposed algorithm can be effective and feasible in both low-dimensional and high-dimensional cases. And the convergence speed and precision of FSKH are higher. Compared to PSO, DE, KH, HS, FS, and BA algorithms, the proposed algorithm shows a better optimization performance and robustness.
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