A Self-Adaptive Evolutionary Multi-Task Based Constrained Multi-Objective Evolutionary Algorithm
Constrained multi-objective optimization problems (CMOPs) are difficult to solve since they involve the optimization of multiple objectives and the satisfaction of various constraints. Most constrained multi-objective evolutionary algorithms (CMOEAs) are prone to fall into the local optima due to th...
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| Vydáno v: | IEEE transactions on emerging topics in computational intelligence Ročník 7; číslo 4; s. 1 - 15 |
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IEEE
01.08.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2471-285X, 2471-285X |
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| Abstract | Constrained multi-objective optimization problems (CMOPs) are difficult to solve since they involve the optimization of multiple objectives and the satisfaction of various constraints. Most constrained multi-objective evolutionary algorithms (CMOEAs) are prone to fall into the local optima due to the imbalance between objectives and constraints as well as the poor search ability of the population. To better solve CMOPs, this paper proposes a double-balanced evolutionary multi-task optimization (DBEMTO) algorithm, which evolves two populations to respectively solve the main task (CMOP) and the auxiliary task (MOP extracted from the CMOP). In DBEMTO, three evolutionary strategies are assigned to each population for offspring generation. The three evolutionary strategies include an individual transfer-based inter-task strategy and two intra-task strategies, not only utilizing the information of inter-task but also providing diverse search abilities of intra-task. Moreover, a self-adaptive scheme is developed to self-adaptively employ three strategies, so that the population can balance the information utilization of both intra-task and inter-task. Then, in the environmental selection, the performance of the three strategies is adopted to guide the sharing of the two offspring populations. Compared with several other state-of-the-art CMOEAs, DBEMTO has performed more competitively according to the final results. |
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| AbstractList | Constrained multi-objective optimization problems (CMOPs) are difficult to solve since they involve the optimization of multiple objectives and the satisfaction of various constraints. Most constrained multi-objective evolutionary algorithms (CMOEAs) are prone to fall into the local optima due to the imbalance between objectives and constraints as well as the poor search ability of the population. To better solve CMOPs, this paper proposes a double-balanced evolutionary multi-task optimization (DBEMTO) algorithm, which evolves two populations to respectively solve the main task (CMOP) and the auxiliary task (MOP extracted from the CMOP). In DBEMTO, three evolutionary strategies are assigned to each population for offspring generation. The three evolutionary strategies include an individual transfer-based inter-task strategy and two intra-task strategies, not only utilizing the information of inter-task but also providing diverse search abilities of intra-task. Moreover, a self-adaptive scheme is developed to self-adaptively employ three strategies, so that the population can balance the information utilization of both intra-task and inter-task. Then, in the environmental selection, the performance of the three strategies is adopted to guide the sharing of the two offspring populations. Compared with several other state-of-the-art CMOEAs, DBEMTO has performed more competitively according to the final results. |
| Author | Yue, Caitong Qu, Boyang Liang, Jing Guo, Yinan Qiao, Kangjia Yu, Kunjie Wang, Minghui |
| Author_xml | – sequence: 1 givenname: Kangjia orcidid: 0000-0003-1713-7700 surname: Qiao fullname: Qiao, Kangjia organization: School of Electrical Engineering, Zhengzhou University, Zhengzhou, China – sequence: 2 givenname: Jing orcidid: 0000-0003-0811-0223 surname: Liang fullname: Liang, Jing organization: School of Electrical Engineering, Zhengzhou University, Zhengzhou, China – sequence: 3 givenname: Kunjie orcidid: 0000-0001-9945-1976 surname: Yu fullname: Yu, Kunjie organization: School of Electrical Engineering, Zhengzhou University, Zhengzhou, China – sequence: 4 givenname: Minghui surname: Wang fullname: Wang, Minghui organization: School of Information Engineering, Zhengzhou University, Zhengzhou, China – sequence: 5 givenname: Boyang orcidid: 0000-0001-7539-3927 surname: Qu fullname: Qu, Boyang organization: School of Electrical and Information Engineering, Zhongyuan University of Technology, Zhengzhou, China – sequence: 6 givenname: Caitong orcidid: 0000-0002-3362-0703 surname: Yue fullname: Yue, Caitong organization: School of Electrical Engineering, Zhengzhou University, Zhengzhou, China – sequence: 7 givenname: Yinan orcidid: 0000-0002-4276-5410 surname: Guo fullname: Guo, Yinan organization: China University of Mining and Technology, Beijing, China |
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| SubjectTerms | Constrained multi-objective optimization Constraints Evolutionary algorithms evolutionary multi-task optimization Genetic algorithms inter-task intra-task Knowledge transfer Multiple objective analysis Multitasking Optimization Populations self-adaptive Sociology Statistics Task analysis |
| Title | A Self-Adaptive Evolutionary Multi-Task Based Constrained Multi-Objective Evolutionary Algorithm |
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