A Pareto Front Transformation Model for Multi- Objective-Based Constrained Optimization
One of the most promising approaches of handling constrained optimization problems (COPs) is to adopt multi-objective methods, which can provide a trade-off between the objective and constraints. However, the multi-objective-based constraint-handling techniques take preference over infeasible soluti...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/9007671/ |
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| author | Sanyou Zeng Rui Zhang Ruwang Jiao Qinghui Xu |
| author_facet | Sanyou Zeng Rui Zhang Ruwang Jiao Qinghui Xu |
| author_sort | Sanyou Zeng |
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| description | One of the most promising approaches of handling constrained optimization problems (COPs) is to adopt multi-objective methods, which can provide a trade-off between the objective and constraints. However, the multi-objective-based constraint-handling techniques take preference over infeasible solutions, some promising feasible solutions cannot survive during the course of search because they are dominated ones. Furthermore, some nondominated infeasible solutions with worse objective values should not be reserved in that they are far from the feasible optimal solution. To address these two problems, this paper proposes a Pareto front transformation model which transforms a part of potential feasible solutions into nondominated ones. Meanwhile, combined with the dynamic multi-objective technique, certain nondominated infeasible solutions with worse objective values will be rejected. In this way, the search can towards the global optimum from both the feasible and infeasible sides of the search space. The proposed Pareto front transformation model is integrated into a multi-objective-based constrained evolutionary algorithm (CEA). The new designed algorithm is named PT-MOEA, and it is compared with seven peer multi-objective-based CEAs and five state-of-the-art CEAs on solving IEEE CEC 2006 and IEEE CEC 2010 test suites, respectively. Experimental results demonstrate the competitiveness of the proposed method in comparison with its competitors for solving COPs. |
| format | Article |
| id | doaj-art-82610a2aa0b14f89baf0f7d98ab68931 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-82610a2aa0b14f89baf0f7d98ab689312025-08-20T03:55:48ZengIEEEIEEE Access2169-35362025-01-011312347312348610.1109/ACCESS.2020.29760479007671A Pareto Front Transformation Model for Multi- Objective-Based Constrained OptimizationSanyou Zeng0https://orcid.org/0000-0002-0795-9092Rui Zhang1https://orcid.org/0000-0003-3146-7554Ruwang Jiao2https://orcid.org/0000-0003-0780-1110Qinghui Xu3https://orcid.org/0000-0003-3996-3755School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, ChinaSchool of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, ChinaSchool of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, ChinaSchool of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan, ChinaOne of the most promising approaches of handling constrained optimization problems (COPs) is to adopt multi-objective methods, which can provide a trade-off between the objective and constraints. However, the multi-objective-based constraint-handling techniques take preference over infeasible solutions, some promising feasible solutions cannot survive during the course of search because they are dominated ones. Furthermore, some nondominated infeasible solutions with worse objective values should not be reserved in that they are far from the feasible optimal solution. To address these two problems, this paper proposes a Pareto front transformation model which transforms a part of potential feasible solutions into nondominated ones. Meanwhile, combined with the dynamic multi-objective technique, certain nondominated infeasible solutions with worse objective values will be rejected. In this way, the search can towards the global optimum from both the feasible and infeasible sides of the search space. The proposed Pareto front transformation model is integrated into a multi-objective-based constrained evolutionary algorithm (CEA). The new designed algorithm is named PT-MOEA, and it is compared with seven peer multi-objective-based CEAs and five state-of-the-art CEAs on solving IEEE CEC 2006 and IEEE CEC 2010 test suites, respectively. Experimental results demonstrate the competitiveness of the proposed method in comparison with its competitors for solving COPs.https://ieeexplore.ieee.org/document/9007671/Evolutionary computationconstrained optimizationmulti-objective optimization |
| spellingShingle | Sanyou Zeng Rui Zhang Ruwang Jiao Qinghui Xu A Pareto Front Transformation Model for Multi- Objective-Based Constrained Optimization IEEE Access Evolutionary computation constrained optimization multi-objective optimization |
| title | A Pareto Front Transformation Model for Multi- Objective-Based Constrained Optimization |
| title_full | A Pareto Front Transformation Model for Multi- Objective-Based Constrained Optimization |
| title_fullStr | A Pareto Front Transformation Model for Multi- Objective-Based Constrained Optimization |
| title_full_unstemmed | A Pareto Front Transformation Model for Multi- Objective-Based Constrained Optimization |
| title_short | A Pareto Front Transformation Model for Multi- Objective-Based Constrained Optimization |
| title_sort | pareto front transformation model for multi objective based constrained optimization |
| topic | Evolutionary computation constrained optimization multi-objective optimization |
| url | https://ieeexplore.ieee.org/document/9007671/ |
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