Abstract
As an effective swarm intelligence based optimization technique, artificial bee colony (ABC) algorithm has become popular in recent years. However, its performance is still not satisfied in solving some complex optimization problems. The main reason is that both of the employed bee phase and onlooker bee phase use the same solution search equation to generate new candidate solutions, and the solution search equation is good at exploration but poor at exploitation. To solve this problem, in this paper, we propose a multi-strategy artificial bee colony algorithm with neighborhood search (MSABC-NS). In MSABC-NS, a multi-strategy mechanism is designed to use two different solution search equations, and a neighborhood search mechanism is introduced to make full use of good solutions. Experiments are conducted on 22 widely used benchmark functions, and three different ABC variants are included in the comparison. The results show that our approach can achieve better performance on most of the benchmark functions.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (Nos. 61603163 and 61876074) and the Science and Technology Foundation of Jiangxi Province (No. 20151BAB217007).
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Sun, C., Zhou, X., Wang, M. (2019). A Multi-strategy Artificial Bee Colony Algorithm with Neighborhood Search. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2019. Lecture Notes in Computer Science(), vol 11655. Springer, Cham. https://doi.org/10.1007/978-3-030-26369-0_29
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DOI: https://doi.org/10.1007/978-3-030-26369-0_29
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