A coevolutionary constrained multi-objective algorithm with a learning constraint boundary
When solving constrained multi-objective optimization problems, the balance of convergence, diversity, and feasibility plays a pivotal role. To address this issue, this paper proposes a coevolutionary constrained multi-objective algorithm with learning constraint boundary (CCMOLCB). Firstly, the con...
Saved in:
| Published in: | Applied soft computing Vol. 148; p. 110845 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
01.11.2023
|
| Subjects: | |
| ISSN: | 1568-4946, 1872-9681 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | When solving constrained multi-objective optimization problems, the balance of convergence, diversity, and feasibility plays a pivotal role. To address this issue, this paper proposes a coevolutionary constrained multi-objective algorithm with learning constraint boundary (CCMOLCB). Firstly, the constrained multi-objective problems are transformed by adding an additional objective using the constraint violation degree. Then, the transformed problem is solved by an improved coevolutionary framework which employs two populations. The main population explores the objective space and repairs infeasible solutions to maintain the feasibility of population. Meanwhile, the feasibility and diversity of solutions are balanced by using a dynamic weight coefficient during the evolution, it changes as the number of iterations increases. The subordinate population selects solutions by taking into consideration the learning constraint boundary (LCB). This boundary guarantees convergence of solutions by constraining the search range of the main population, thereby enhancing the environmental selection pressure. The performance of CCMOLCB is compared with seven state-of-the-art constrained multi-objective evolutionary algorithms on five test suites. The experimental results illustrate that CCMOLCB exhibits competitive performance when dealing with this family of problems.
•A restrictive relationship is embedded into coevolutionary framework by using the proposed learning constraint boundary.•The learning constraint boundary updates adaptively by absorbing the valuable information from population.•The self-adapting ability of the coevolutionary framework is improved by the learning mechanism.•The comparison of CCMO-LCB with state-of-the-art algorithms is presented. |
|---|---|
| ISSN: | 1568-4946 1872-9681 |
| DOI: | 10.1016/j.asoc.2023.110845 |