A self-organizing assisted multi-task algorithm for constrained multi-objective optimization problems

Constrained multi-objective optimization problems (CMOPs) require a delicate balance between satisfying constraints and optimizing objectives. Existing constrained multi-objective evolutionary algorithms (CMOEAs) often struggle to balance convergence, diversity, and feasibility, especially when deal...

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Vydáno v:Information sciences Ročník 664; s. 120339
Hlavní autoři: Ye, Qianlin, Wang, Wanliang, Li, Guoqing, Dai, Rui
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 01.04.2024
Témata:
Constrained multi-objective optimization
Constraint-to-constraint
Multi-task
Self-organizing mapping
Constrained multi-objective optimization
Constraint-to-constraint
Multi-task
Self-organizing mapping
ISSN:0020-0255, 1872-6291
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Shrnutí:Constrained multi-objective optimization problems (CMOPs) require a delicate balance between satisfying constraints and optimizing objectives. Existing constrained multi-objective evolutionary algorithms (CMOEAs) often struggle to balance convergence, diversity, and feasibility, especially when dealing with CMOPs that have complex feasible regions. This paper proposes a multi-task-based self-organizing mapping evolutionary algorithm (MTSOM) to tackle this challenge, which includes a main and auxiliary task. Two populations independently optimize two tasks without considering constraints in the early stage. Subsequently, in the middle stage, both tasks explore the distribution structure of the population in parallel by employing a novel constraint-to-constraint self-organizing mapping (SOM) approach. In the late stage, the main task fully considers feasibility, while the auxiliary task focuses solely on the highest priority constraints. This approach enables rapid convergence toward feasible regions. To evaluate MTSOM’s effectiveness, we conducted a series of experiments on five benchmark suites. Results indicate that MTSOM is competitive when compared to other state-of-the-art CMOEAs. Additionally, our proposed constraint-to-constraint SOM is superior in handling complex CMOPs.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2024.120339