Enhancing Dynamic Constrained Multiobjective Optimization With Multicenters-Based Prediction

Dynamic constrained multiobjective optimization problems (DCMOPs) involve complex changes in objective functions and constraints over time. These changes challenge most existing algorithms to quickly cross infeasible regions and accurately track the changing Pareto optimal set (POS) and Pareto optim...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 29; no. 5; pp. 1604 - 1618
Main Authors: Gong, Quan, Xia, Yizhang, Zou, Juan, Hou, Zhanglu, Liu, Yuan
Format: Journal Article
Language:English
Published: IEEE 01.10.2025
Subjects:
Constraint handling
Convergence
Dynamic constrained multiobjective optimization
Dynamic response
Evolutionary computation
Heuristic algorithms
Linear programming
multiobjective optimization problems (MOPs)
Optical fibers
Optimization
Pareto optimization
Prediction algorithms
prediction strategy
ISSN:1089-778X
Online Access:Get full text
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Summary:Dynamic constrained multiobjective optimization problems (DCMOPs) involve complex changes in objective functions and constraints over time. These changes challenge most existing algorithms to quickly cross infeasible regions and accurately track the changing Pareto optimal set (POS) and Pareto optimal front (POF). To address this issue, this article presents a multicenters-based prediction strategy, termed FCP, for solving DCMOPs more effectively. First, we introduce a penalty function to cluster the historical optimal solutions, thereby obtaining multicenters of these solutions. These centers can roughly represent the distribution of different clusters in POS. Then, we predict cluster centers of the new environment's POS by calculating the distance of centers from the preceding two environments. The prediction strategy can handle the change of POS caused by constraints thereby improving the accuracy of prediction. Finally, a proposed population generator calculates the distances between new centers and utilizes information from these centers to predict a well-distributed initial population. Comprehensive studies on widely used benchmark problems demonstrate that our proposed algorithm is very competitive in dealing with DCMOPs compared with seven state-of-the-art algorithms. Meanwhile, to validate the proposed prediction strategy, it is embedded into the static constraints handling techniques from other DCMOEAs to solving DCMOPs and the experimental results indicate that FCP is superior in generating initial population.
ISSN:1089-778X
DOI:10.1109/TEVC.2025.3551399