Constrained multi-objective optimization algorithm based on hierarchical environmental selection

被引：0

作者：

Zhang J. ^{[1
,2
]}

Cao J. ^{[1
,2
]}

Zhao F. ^{[1
,2
]}

Chen Z. ^{[1
,2
]}

机构：

[1] College of Computer and Communication, Lanzhou University of Technology, Lanzhou

[2] Gansu Engineering Research Center of Manufacturing Informationization, Lanzhou

来源：

Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition) | 2023年 / 51卷 / 05期

关键词：

constrained multi-objective optimization problems; constraint-handling technique; evolutionary algorithm; feasible solutions; hierarchical environmental selection;

D O I：

10.13245/j.hust.230521

中图分类号：

学科分类号：

摘要：

Aiming at the shortcoming that constrained multi-objective optimization problems (CMOPs) were difficult in balancing the constraints and objectives，a constrained multi-objective optimization algorithm based on hierarchical environmental selection (CMOEA-HES) was proposed．In the CMOEA-HES，simulated binary crossover (SBX) and differential evolution (DE) were first adopted to produce the offspring solutions，respectively．Then，the first environmental selection mechanism was used to choose the promising solutions with better convergence and diversity from two offspring solutions．Subsequently，the second environmental selection mechanism was conducted on the parent solutions and solutions obtained from the first environmental selection，and the feasible solutions were chosen based on the diversity and convergence．Finally，the selected solutions were chosen as population for next generation．To verify the performance of CMOEA-HES，it was simulated with five state-of-the-art constrained multi-objective optimization algorithms on two typical test suite．Experimental results show that the CMOEA-HES is more competitive in solving CMOPs. © 2023 Huazhong University of Science and Technology. All rights reserved.

引用

页码：131 / 136

页数：5

共 18 条

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