A coevolutionary algorithm using multi-operator ensemble for many-objective optimisation problems

被引:0
作者
Zhu, Di [1 ]
Xiao, Renbin [1 ]
Li, Gui [1 ]
Ma, Yingnan [1 ]
Yi, Mengting [2 ]
机构
[1] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Hubei, Peoples R China
[2] Huazhong Univ Sci & Technol, Sch Elect & Elect Engn, Wuhan 430074, Hubei, Peoples R China
关键词
many-objective optimisation; shift-based density estimation; multiple-operator ensemble; MOE; decomposition; co-evolution; MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS;
D O I
10.1504/IJBIC.2024.141689
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
MaOPs are typically solved by using evolutionary algorithms (EAs) to search solutions with the help of operators. The strategy of multiple-operator ensemble (MOE) can combine the search capabilities of different operators to ensure better adaptability in different fitness landscapes. This paper proposes a MaOEA/D algorithm based on coevolutionary multi-operator ensemble (MaOEA/D-CME) for solving MaOPs. The algorithm utilises coevolution technique to balance the capabilities of the simulated binary crossover operator (SBX) and the differential evolution operator (DE) in MOEA/D for different types of problems. To reduce computational costs and avoid premature convergence or slow convergence, we propose a 'multi-stage environmental selection' strategy. Tested on benchmark problems of 13 challenging high-dimensional MaOPs, the numerical results in terms of HV and IGD indicators demonstrate that MaOEA/D-CME achieves competitive advantages compared to some state-of-the-art MOEAs.
引用
收藏
页码:191 / 200
页数:11
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