Multipopulation Evolution-Based Dynamic Constrained Multiobjective Optimization Under Diverse Changing Environments

Dynamic constrained multiobjective optimization involves irregular changes in the distribution of the true Pareto-optimal fronts, drastic changes in the feasible region caused by constraints, and the movement directions and magnitudes of the optimal distance variables due to diverse changing environ...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 28; no. 3; pp. 763 - 777
Main Authors: Chen, Qingda, Ding, Jinliang, Yen, Gary G., Yang, Shengxiang, Chai, Tianyou
Format: Journal Article
Language:English
Published: New York IEEE 01.06.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithm design and analysis
Algorithms
Changing environments
Constraint handling
Constraints
Dynamic constrained multiobjective optimization
Dynamic response
Evolution
Feasibility
Generators
Multiple objective analysis
Optimization
Pareto optimization
Pareto optimum
population selection
Real variables
Strategy
Tracking
tribe classification operator
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Summary:Dynamic constrained multiobjective optimization involves irregular changes in the distribution of the true Pareto-optimal fronts, drastic changes in the feasible region caused by constraints, and the movement directions and magnitudes of the optimal distance variables due to diverse changing environments. To solve these problems, we propose a multipopulation evolution-based dynamic constrained multiobjective optimization algorithm. In this algorithm, we design a tribe classification operator to divide the population into different tribes according to a feasibility check and the objective values, which is beneficial for driving the population toward the feasible region and Pareto-optimal fronts. Meanwhile, a population selection strategy is proposed to identify promising solutions from tribes and exploit them to update the population. The optimal values of the distance variables vary differently with dynamic environments, thus, we design a dynamic response strategy for solutions in different tribes that estimates their distances to approach the Pareto-optimal fronts and regenerates a promising population when detecting environmental changes. In addition, a scalable generator is designed to simulate diverse movement directions and magnitudes of the optimal distance variables in real-world problems under dynamic environments, obtaining a set of improved test problems. Experimental results show the effectiveness of test problems, and the proposed algorithm is impressively competitive with several chosen state-of-the-art competitors.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2023.3241762