### Novel artificial bee colony algorithm based on objective space decomposition for solving multi-objective optimization problems

WAN Peng-fei, GAO Xing-bao*

1. School of Mathematics and Information Science, Shannxi Normal University, Xian 710119, Shaanxi, China
• Published:2018-11-14
• About author:国家自然科学基金资助项目(61273311);中央高校基本科研业务费专项资金资助项目(GK201603002,2017TS002)
• Supported by:
国家自然科学基金资助项目(61273311);中央高校基本科研业务费专项资金资助项目(GK201603002,2017TS002)

Abstract: When solving multi-objective optimization problems, how to keep balance between convergence and distribution of solutions is a task which extremely important, but it is not easy. In this paper, we develop a novel artificial bee colony algorithm based on objective space decomposition for solving these issues. First, we divide the objective space into a series of sub-regions by a set of direction vectors, and one solution is at least chosen at each sub-region to maintain the diversity of the obtained solutions. To improve the convergence performance, we propose a search strategy based on information exchanging and two selection strategies based on decomposition respectively to enhance the search capacity of artificial bee colony algorithm. Moreover, a search strategy based on Gaussian distribution is employed to improve the effectiveness. The proposed algorithm is empirically compared with eight state-of-the-art multi-objective evolutionary algorithms on 10 benchmark problems. The comparative results demonstrate that the convergence and distribution performance of the proposed algorithm are superior to the compared algorithms.

CLC Number:

• TP301
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