Journal
IEEE ACCESS
Volume 7, Issue -, Pages 133982-133995Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2941247
Keywords
Artificial bee colony (ABC); multiple search strategies; strategy pool; dimensional selection; opposition-based learning
Categories
Funding
- National Natural Science Foundation of China [61663028]
- Distinguished Young Talents Plan of Jiangxi Province [20171BCB23075]
- Natural Science Foundation of Jiangxi Province [20171BAB202035]
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Artificial bee colony (ABC) algorithm is a popular optimization technique with strong search ability. Although ABC has the ability to handle complex optimization problems, it suffers from weak exploitation and slow convergence. In order to tackle this issue, a new ABC variant based on multiple search strategies and dimension selection (ABC-MSDS) is proposed in this paper. Firstly, multiple search strategies based on dual strategy pool are designed. Compared to other existing ABC with multiple search strategies, our approach constructs two strategy pools for employed and onlooker bees, respectively. Secondly, a new dimension selection method is used to replace the random dimension selection in the standard ABC. In the search process, each dimension is chosen one by one in terms of the quality of offspring. Finally, a modified scout bee phase is employed to accelerate the search. Experimental study is conducted on classical benchmark problems and CEC 2013 shifted and rotated problems. The performance of ABC-MSDS is compared with several recently published ABC variants. Computational results demonstrate the effectiveness of our approach.
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