Multiple Populations for Multiple Objectives Framework with Bias Sorting for Many-objective Optimization
The convergence and diversity enhancement of multiobjective evolutionary algorithms (MOEAs) to efficiently solve many-objective optimization problems (MaOPs) is an active topic in evolutionary computation. By considering the advantages of the multiple populations for multiple objectives (MPMO) frame...
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| Veröffentlicht in: | IEEE transactions on evolutionary computation Jg. 27; H. 5; S. 1 |
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| Sprache: | Englisch |
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01.10.2023
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| Abstract | The convergence and diversity enhancement of multiobjective evolutionary algorithms (MOEAs) to efficiently solve many-objective optimization problems (MaOPs) is an active topic in evolutionary computation. By considering the advantages of the multiple populations for multiple objectives (MPMO) framework in solving multi-objective optimization problems and even MaOPs, this paper proposes an MPMO-based algorithm with a bias sorting (BS) method (termed MPMO-BS) for solving MaOPs to achieve both good convergence and diversity perfor-mance. For convergence, the BS method is applied to each popu-lation of the MPMO framework to enhance the role of nondomi-nated sorting by biasedly paying more attention to the objective optimized by the corresponding population. This way, all the populations in the MPMO framework evolve together to promote the convergence performance on all objectives of the MaOP. For diversity, an elite learning strategy is adopted to generate locally mutated solutions, and a reference vector-based maintenance method is adopted to preserve diverse solutions. The performance of the proposed MPMO-BS algorithm is assessed on 29 widely used MaOP test problems and two real-world application prob-lems. The experimental results show its high effectiveness and competitiveness when compared with seven state-of-the-art MOEAs for many-objective optimization. |
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| AbstractList | The convergence and diversity enhancement of multiobjective evolutionary algorithms (MOEAs) to efficiently solve many-objective optimization problems (MaOPs) is an active topic in evolutionary computation. By considering the advantages of the multiple populations for multiple objectives (MPMO) framework in solving multiobjective optimization problems and even MaOPs, this article proposes an MPMO-based algorithm with a bias sorting (BS) method (termed MPMO-BS) for solving MaOPs to achieve both good convergence and diversity performance. For convergence, the BS method is applied to each population of the MPMO framework to enhance the role of nondominated sorting by biasedly paying more attention to the objective optimized by the corresponding population. This way, all the populations in the MPMO framework evolve together to promote the convergence performance on all objectives of the MaOP. For diversity, an elite learning strategy is adopted to generate locally mutated solutions, and a reference vector-based maintenance method is adopted to preserve diverse solutions. The performance of the proposed MPMO-BS algorithm is assessed on 29 widely used MaOP test problems and two real-world application problems. The experimental results show its high effectiveness and competitiveness when compared with seven state-of-the-art MOEAs for many-objective optimization. The convergence and diversity enhancement of multiobjective evolutionary algorithms (MOEAs) to efficiently solve many-objective optimization problems (MaOPs) is an active topic in evolutionary computation. By considering the advantages of the multiple populations for multiple objectives (MPMO) framework in solving multi-objective optimization problems and even MaOPs, this paper proposes an MPMO-based algorithm with a bias sorting (BS) method (termed MPMO-BS) for solving MaOPs to achieve both good convergence and diversity perfor-mance. For convergence, the BS method is applied to each popu-lation of the MPMO framework to enhance the role of nondomi-nated sorting by biasedly paying more attention to the objective optimized by the corresponding population. This way, all the populations in the MPMO framework evolve together to promote the convergence performance on all objectives of the MaOP. For diversity, an elite learning strategy is adopted to generate locally mutated solutions, and a reference vector-based maintenance method is adopted to preserve diverse solutions. The performance of the proposed MPMO-BS algorithm is assessed on 29 widely used MaOP test problems and two real-world application prob-lems. The experimental results show its high effectiveness and competitiveness when compared with seven state-of-the-art MOEAs for many-objective optimization. |
| Author | Yang, Qi-Te Zhan, Zhi-Hui Kwong, Sam Zhang, Jun |
| Author_xml | – sequence: 1 givenname: Qi-Te orcidid: 0000-0001-5430-7073 surname: Yang fullname: Yang, Qi-Te organization: School of Computer Science and Engineering, South China University of Technology, Guangzhou, China – sequence: 2 givenname: Zhi-Hui orcidid: 0000-0003-0862-0514 surname: Zhan fullname: Zhan, Zhi-Hui organization: School of Computer Science and Engineering, South China University of Technology, Guangzhou, China – sequence: 3 givenname: Sam orcidid: 0000-0001-7484-7261 surname: Kwong fullname: Kwong, Sam organization: Department of Computer Science, City University of Hong Kong, Hong Kong, Hong Kong – sequence: 4 givenname: Jun orcidid: 0000-0003-4148-4294 surname: Zhang fullname: Zhang, Jun organization: Zhejiang Normal University, Jinhua, China |
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| SubjectTerms | Bias bias sorting coevolution Convergence Evolutionary algorithms Evolutionary computation Maintenance engineering Many-objective optimization problems (MaOPs) multi-objective evolutionary algorithm (MOEA) Multiple objective analysis Optimization Pareto optimization Populations Sociology Sorting Sorting algorithms Statistics |
| Title | Multiple Populations for Multiple Objectives Framework with Bias Sorting for Many-objective Optimization |
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