A multi-objective bilevel optimisation evolutionary algorithm with dual populations lower-level search
In multi-objective bilevel optimisation problems, the upper-level performance of different lower-level optimal solutions may be very different, even though they belong to the same lower-level problem. It may lead to poor optimisation results. Therefore, the lower-level search should search lower-lev...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2022.2077312 |
| _version_ | 1797684028418031616 |
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| author | Weizhong Wang Hai-Lin Liu Hongjian Shi |
| author_facet | Weizhong Wang Hai-Lin Liu Hongjian Shi |
| author_sort | Weizhong Wang |
| collection | DOAJ |
| description | In multi-objective bilevel optimisation problems, the upper-level performance of different lower-level optimal solutions may be very different, even though they belong to the same lower-level problem. It may lead to poor optimisation results. Therefore, the lower-level search should search lower-level non-dominated solutions that are also non-dominated in the upper-level objective space. In this paper, we use two populations in the lower-level search. The first population maintains non-dominance and diversity in the lower-level objective space and provides the second population with convergence pressure from the lower level. The second population selects the upper-level non-dominated solutions that are not dominated by the first population in the lower-level objective space, which make the second population maintain the non-dominance at both upper and lower levels. Besides, to improve the search efficiency, we set up the upper-level mating pool to generate the upper-level vectors of offsprings near the upper-level vectors of the better individuals in the current population. To balance convergence and diversity, the selection operator of a decomposition based multi-objective evolutionary algorithm is adopted. The proposed algorithm has been evaluated on a set of benchmark problems and a real-world optimisation problem. Experimental results demonstrate that the proposed algorithm is efficient and effective. |
| first_indexed | 2024-03-12T00:24:38Z |
| format | Article |
| id | doaj.art-c6c771e5a49a46c6bb95015a528ef323 |
| institution | Directory Open Access Journal |
| issn | 0954-0091 1360-0494 |
| language | English |
| last_indexed | 2024-03-12T00:24:38Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj.art-c6c771e5a49a46c6bb95015a528ef3232023-09-15T10:48:00ZengTaylor & Francis GroupConnection Science0954-00911360-04942022-12-013411556158110.1080/09540091.2022.20773122077312A multi-objective bilevel optimisation evolutionary algorithm with dual populations lower-level searchWeizhong Wang0Hai-Lin Liu1Hongjian Shi2School of Mathematics and Statistics, Guangdong University of TechnologySchool of Mathematics and Statistics, Guangdong University of TechnologyBeijing Normal University-Hong Kong, Baptist University United International CollegeIn multi-objective bilevel optimisation problems, the upper-level performance of different lower-level optimal solutions may be very different, even though they belong to the same lower-level problem. It may lead to poor optimisation results. Therefore, the lower-level search should search lower-level non-dominated solutions that are also non-dominated in the upper-level objective space. In this paper, we use two populations in the lower-level search. The first population maintains non-dominance and diversity in the lower-level objective space and provides the second population with convergence pressure from the lower level. The second population selects the upper-level non-dominated solutions that are not dominated by the first population in the lower-level objective space, which make the second population maintain the non-dominance at both upper and lower levels. Besides, to improve the search efficiency, we set up the upper-level mating pool to generate the upper-level vectors of offsprings near the upper-level vectors of the better individuals in the current population. To balance convergence and diversity, the selection operator of a decomposition based multi-objective evolutionary algorithm is adopted. The proposed algorithm has been evaluated on a set of benchmark problems and a real-world optimisation problem. Experimental results demonstrate that the proposed algorithm is efficient and effective.http://dx.doi.org/10.1080/09540091.2022.2077312multi-objectivebileveldual populationsmulti-objective to multi-objective (m2m)differential evolution (de) |
| spellingShingle | Weizhong Wang Hai-Lin Liu Hongjian Shi A multi-objective bilevel optimisation evolutionary algorithm with dual populations lower-level search Connection Science multi-objective bilevel dual populations multi-objective to multi-objective (m2m) differential evolution (de) |
| title | A multi-objective bilevel optimisation evolutionary algorithm with dual populations lower-level search |
| title_full | A multi-objective bilevel optimisation evolutionary algorithm with dual populations lower-level search |
| title_fullStr | A multi-objective bilevel optimisation evolutionary algorithm with dual populations lower-level search |
| title_full_unstemmed | A multi-objective bilevel optimisation evolutionary algorithm with dual populations lower-level search |
| title_short | A multi-objective bilevel optimisation evolutionary algorithm with dual populations lower-level search |
| title_sort | multi objective bilevel optimisation evolutionary algorithm with dual populations lower level search |
| topic | multi-objective bilevel dual populations multi-objective to multi-objective (m2m) differential evolution (de) |
| url | http://dx.doi.org/10.1080/09540091.2022.2077312 |
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