A multi-task multi-objective evolutionary algorithm for constrained multi-objective optimization problems

Constrained multi-objective evolutionary algorithms often struggle to simultaneously achieve both objective optimization and constraint satisfaction, particularly when dealing with multi-objective optimization problems that involve complex constraints. To address these issues, this paper proposes a...

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Vydané v:Memetic computing Ročník 17; číslo 3; s. 40
Hlavní autori: Liu, Tianyu, Wu, Yu, Xu, He
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2025
Springer Nature B.V
Predmet:
Applications of Mathematics
Artificial Intelligence
Bioinformatics
Complex Systems
Constraints
Control
Design
Engineering
Evolutionary algorithms
Genetic algorithms
Knowledge
Mathematical and Computational Engineering
Mathematical programming
Mechatronics
Multiple objective analysis
Objectives
Optimization
Optimization algorithms
Pareto optimum
Problem solving
Regular Research Paper
Robotics
Constrained multi-objective evolutionary algorithm
Dynamic constraint updating
Multi-task
Optimization stopping
Knowledge transfer
ISSN:1865-9284, 1865-9292
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Shrnutí:Constrained multi-objective evolutionary algorithms often struggle to simultaneously achieve both objective optimization and constraint satisfaction, particularly when dealing with multi-objective optimization problems that involve complex constraints. To address these issues, this paper proposes a multi-task multi-objective evolutionary algorithm (MTMOEA) to solve constrained multi-objective optimization problems. The algorithm includes three optimization tasks: one original task and two auxiliary tasks. Among the auxiliary tasks, one is the unconstrained optimization task with all constraints removed, and the other is the optimization task with partial constraints applied. For the unconstrained task, an optimization stopping strategy is adopted to determine whether to halt further optimization, thereby avoiding unnecessary computational resource usage. For the partially constrained task, a classification-based dynamic constraint updating strategy is employed to ensure that the task focuses on constraints that have a greater impact on problem-solving. Based on the characteristics of the three tasks, a knowledge transfer strategy, which includes two different knowledge transfer methods, is proposed. The proposed MTMOEA is evaluated against ten state-of-the-art constrained multi-objective evolutionary algorithms on three benchmark sets: LIRCMOP, DOC, and DASCMOP. Experimental results validate the effectiveness of the proposed algorithm.
Bibliografia:ObjectType-Article-1
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ISSN:1865-9284
1865-9292
DOI:10.1007/s12293-025-00471-5