A new prediction-based evolutionary dynamic multiobjective optimization algorithm aided by Pareto optimal solution estimation strategy

Dynamic multiobjective optimization problems (DMOPs) typically involve multiple conflicting time-varying objectives that require optimization algorithms to quickly track the changing Pareto-optimal front (POF). To this end, several methods have been developed to predict new locations of moving Paret...

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Veröffentlicht in:	Applied soft computing Jg. 165; S. 112022
Hauptverfasser:	Gao, Kai, Xu, Lihong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.11.2024
Schlagworte:	Adaptive crossover rate Dynamic multiobjective optimization problem Evolutionary algorithm Multi-directional difference prediction POS estimation strategy Multi-directional difference prediction Adaptive crossover rate Dynamic multiobjective optimization problem Evolutionary algorithm POS estimation strategy
ISSN:	1568-4946
Online-Zugang:	Volltext
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Zusammenfassung:	Dynamic multiobjective optimization problems (DMOPs) typically involve multiple conflicting time-varying objectives that require optimization algorithms to quickly track the changing Pareto-optimal front (POF). To this end, several methods have been developed to predict new locations of moving Pareto-optimal solution set (POS) so that populations can be re-initialized around the predicted locations. In this paper, a dynamic multi-objective optimization algorithm based on a multi-directional difference model (MOEA/D-MDDM) is proposed. The multi-directional difference model predicts the initial population through the estimated populations developed by a designed POS estimation strategy. An adaptive crossover-rate approach is incorporated into the optimization process to cope with different POS structures. To investigate the performance of the proposed approach, MOEA/D-MDDM has been compared with six state-of-the-art dynamic multiobjective optimization evolutionary algorithms (DMOEAs) on 19 benchmark problems. The experimental results demonstrate that the proposed algorithm can effectively deal with DMOPs whose POS has a single-modality characteristic and continuous manifolds. •Dynamic multiobjective optimization evolutionary algorithm is designed.•The multi-directional difference model is applied to predict the initial population.•POS estimation strategy improves the quality of historically obtained solutions.•An adaptive crossover rate strategy is designed.•Systematic studies demonstrate the effectiveness of the proposed algorithm.
ISSN:	1568-4946
DOI:	10.1016/j.asoc.2024.112022