A many-objective evolutionary algorithm based on three states for solving many-objective optimization problem

In recent years, researchers have taken the many-objective optimization algorithm, which can optimize 5, 8, 10, 15, 20 objective functions simultaneously, as a new research topic. However, the current research on many-objective optimization technology also encounters some challenges. For example: Pa...

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Veröffentlicht in:Scientific reports Jg. 14; H. 1; S. 19140 - 36
Hauptverfasser: Zhao, Jiale, Zhang, Huijie, Yu, Huanhuan, Fei, Hansheng, Huang, Xiangdang, Yang, Qiuling
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 19.08.2024
Nature Publishing Group
Nature Portfolio
Schlagworte:
631/114/116
631/114/1305
631/114/2397
639/705/1042
639/705/117
Algorithms
Genetic algorithms
Humanities and Social Sciences
multidisciplinary
Optimization
Science
Science (multidisciplinary)
ISSN:2045-2322, 2045-2322
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Zusammenfassung:In recent years, researchers have taken the many-objective optimization algorithm, which can optimize 5, 8, 10, 15, 20 objective functions simultaneously, as a new research topic. However, the current research on many-objective optimization technology also encounters some challenges. For example: Pareto resistance phenomenon, difficult diversity maintenance. Based on the above problems, this paper proposes a many-objective evolutionary algorithm based on three states (MOEA/TS). Firstly, a feature extraction operator is proposed. It can extract the features of the high-quality solution set, and then assist the evolution of the current individual. Secondly, based on Pareto front layer, the concept of “individual importance degree” is proposed. The importance degree of an individual can reflect the importance of the individual in the same Pareto front layer, so as to further distinguish the advantages and disadvantages of different individuals in the same front layer. Then, a repulsion field method is proposed. The diversity of the population in the objective space is maintained by the repulsion field, so that the population can be evenly distributed on the real Pareto front. Finally, a new concurrent algorithm framework is designed. In the algorithm framework, the algorithm is divided into three states, and each state focuses on a specific task. The population can switch freely among these three states according to its own evolution. The MOEA/TS algorithm is compared with 7 advanced many-objective optimization algorithms. The experimental results show that the MOEA/TS algorithm is more competitive in many-objective optimization problems.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-70145-8