Affiliations: [a] Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China | [b] Institute of Bio-Inspired Intelligence and Mining Knowledge, School of Computer Science and Technology, Anhui University, Hefei 230039, Anhui, China | [c] School of Electric and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, Henan, China
Correspondence:
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Corresponding author: Xingyi Zhang, Institute of Bio-Inspired Intelligence and Mining Knowledge, School of Computer Science and Technology, Anhui University, No. 111, Jiulong Road, Hefei 230039, Anhui, China. Tel.: +86 13971063372; E-mail: xyzhanghust @gmail.com.
Abstract: Evolutionary algorithms have been used to solve a variety of many-objective optimization problems, where these problems contain more than three conflicting objectives. Most existing evolutionary algorithms have shown to perform well on many-objective optimization problems with regular Pareto optimal fronts, their performance, however, will often considerably deteriorate on those whose Pareto optimal fronts are irregular, e.g., discontinuous, degenerated and convex. To address this issue, in this paper, we propose a region division based many-objective optimization evolutionary algorithm, termed RdEA, where a region division approach is suggested to maintain diversity of population for many-objective optimization (especially for problems with irregular Pareto fronts). In the proposed region division based diversity maintaining approach, the geometric information of Pareto optimal fronts is taken into account by using the non-dominated solutions found at each generation, which helps to solve the problems with irregular Pareto optimal fronts better. The proposed RdEA is compared with five state-of-the-art many-objective evolutionary algorithms on 16 test problems from two test suites DTLZ and WFG and a real-world optimization problem. Experimental results on these problems demonstrate that the competitiveness of the proposed algorithm in solving many-objective optimization problems, especially for those with irregular Pareto optimal fronts.
Keywords: Many-objective optimization, region division, diversity maintaining, irregular Pareto front
