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Article type: Research Article
Authors: Hu, Ziyua; c | Ma, Xuemina; c | Sun, Haoa; c; * | Yang, Jingminga; c | Zhao, Zhiweib
Affiliations: [a] Department of Electrical and Engineering, Yanshan University, Qinhuangdao, PR China | [b] Department of Computer Science and Technology, Tangshan College, Tangshan, PR China | [c] Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, China
Correspondence: [*] Corresponding author. Hao Sun, E-mail: sunhao@ysu.edu.cn.
Abstract: When dealing with multi-objective optimization, the proportion of non-dominated solutions increase rapidly with the increase of optimization objective. Pareto-dominance-based algorithms suffer the low selection pressure towards the true Pareto front. Decomposition-based algorithms may fail to solve the problems with highly irregular Pareto front. Based on the analysis of the two selection mechanism, a dynamic reference-vector-based many-objective evolutionary algorithm(RMaEA) is proposed. Adaptive-adjusted reference vector is used to improve the distribution of the algorithm in global area, and the improved non-dominated relationship is used to improve the convergence in a certain local area. Compared with four state-of-art algorithms on DTLZ benchmark with 5-, 10- and 15-objective, the proposed algorithm obtains 13 minimum mean IGD values and 8 minimum standard deviations among 15 test problem.
Keywords: Many-objective optimization, evolutionary algorithm, Gaussian mixture model, selection mechanism
DOI: 10.3233/JIFS-192124
Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 449-461, 2021
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