An Angle-based Many-Objective evolutionary algorithm with Shift-based density estimation and sum of objectives. (15th December 2022)

• Record Type: Journal Article
• Authors: Zhang, Jianlin; Cao, Jie; Zhao, Fuqing; Chen, Zuohan
• Is Part Of: Expert systems with applications. Volume 209(2022)
• Publication Date: 2022-12-15
• Subjects: Many-objective optimization -- Angle-based selection -- Shift-based density estimation -- Evolutionary optimization; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.118333
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: The proposed algorithm is effective for solving many-objective problems. Shift-based estimation and sum of objectives can reserve the excellent solutions. An angle-based selection strategy can improve the diversity of solutions. Experimental results have been presented by using Wilcoxon's rank-sum test. Abstract: Due to the curse of dimensionality, the existing evolutionary algorithms have difficulties in balancing convergence and diversity in many-objective problems. To address this shortcoming, this paper proposes an efficient many-objective optimizer named MaOEA-ASS. In the MaOEA-ASS, the angle-based selection strategy is used to obtain solutions with good diversity from the population. In addition, the combination of the shift-based density estimation and the sum of objectives, which uses the iteration information and emphasis the distribution of solutions, is employed to obtain the high-quality solutions approximating the optimal Pareto solutions. The proposed MaOEA-ASS is compared with eight state-of-the-art many-objective optimization algorithms (MaOEAs) on the DTLZ and WFG test suites, and its performance is verified on a practical many-objective problem. The experimental results demonstrate that the proposed MaOEA-ASS has a superior performance over the peer competitors on all considered many-objective problems.