A multi-population evolutionary algorithm based on knowledge transfer for constrained many-objective optimization
Constrained Many-objective Optimization Problems (CMaOPs) are challenging in handling objectives and constraints simultaneously. Here, a novel Constrained Many-objective Optimization Evolutionary Algorithm (CMaOEA) based on Multi-population, Knowledge transfer and Improved environmental selection ca...
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| Vydáno v: | Engineering optimization Ročník 57; číslo 3; s. 813 - 843 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Abingdon
Taylor & Francis
04.03.2025
Taylor & Francis Ltd |
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| ISSN: | 0305-215X, 1029-0273 |
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| Abstract | Constrained Many-objective Optimization Problems (CMaOPs) are challenging in handling objectives and constraints simultaneously. Here, a novel Constrained Many-objective Optimization Evolutionary Algorithm (CMaOEA) based on Multi-population, Knowledge transfer and Improved environmental selection called CMaMKI is proposed to handle CMaOPs. The proposed framework evolves a task population to solve the original CMaOP and evolves another population to solve a helper problem derived from the original one. To assist solving the original CMaOP, a knowledge expression and transfer strategy is designed to share useful information in the helper population with the task population. Meanwhile, to balance population convergence, diversity and feasibility, an enhanced environmental selection strategy is devised by combining the ε-constrained technique, θ-dominance and subregional density evaluation. The proposed algorithm is evaluated and contrasted with six state-of-the-art algorithms on a set of benchmark CMaOPs. The experimental results demonstrate the superiority and competitiveness of the proposed method. |
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| AbstractList | Constrained Many-objective Optimization Problems (CMaOPs) are challenging in handling objectives and constraints simultaneously. Here, a novel Constrained Many-objective Optimization Evolutionary Algorithm (CMaOEA) based on Multi-population, Knowledge transfer and Improved environmental selection called CMaMKI is proposed to handle CMaOPs. The proposed framework evolves a task population to solve the original CMaOP and evolves another population to solve a helper problem derived from the original one. To assist solving the original CMaOP, a knowledge expression and transfer strategy is designed to share useful information in the helper population with the task population. Meanwhile, to balance population convergence, diversity and feasibility, an enhanced environmental selection strategy is devised by combining the ε-constrained technique, θ-dominance and subregional density evaluation. The proposed algorithm is evaluated and contrasted with six state-of-the-art algorithms on a set of benchmark CMaOPs. The experimental results demonstrate the superiority and competitiveness of the proposed method. |
| Author | Ge, Wenlong Song, Weida Wang, Wei Zhang, Shanxin |
| Author_xml | – sequence: 1 givenname: Wenlong surname: Ge fullname: Ge, Wenlong organization: Jiangnan University – sequence: 2 givenname: Shanxin orcidid: 0000-0002-7948-9202 surname: Zhang fullname: Zhang, Shanxin email: shanxinzhang@jiangnan.edu.cn organization: Jiangnan University – sequence: 3 givenname: Weida surname: Song fullname: Song, Weida organization: Jiangnan University – sequence: 4 givenname: Wei surname: Wang fullname: Wang, Wei organization: Jiangnan University |
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| SubjectTerms | Algorithms Constrained many-objective optimization Constraints environmental selection evolutionary algorithm Evolutionary algorithms knowledge transfer multi-population Multiple objective analysis Optimization |
| Title | A multi-population evolutionary algorithm based on knowledge transfer for constrained many-objective optimization |
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