Solving dynamic multi-objective problems with a new prediction-based optimization algorithm

Qingyang Zhang*, Shouyong Jiang, Shengxiang Yang, Hui Song

*Corresponding author for this work

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This paper proposes a new dynamic multi-objective optimization algorithm by integrating a new fitting-based prediction (FBP) mechanism with regularity model-based multi-objective estimation of distribution algorithm (RM-MEDA) for multi-objective optimization in changing environments. The prediction-based reaction mechanism aims to generate high-quality population when changes occur, which includes three subpopulations for tracking the moving Pareto-optimal set effectively. The first subpopulation is created by a simple linear prediction model with two different stepsizes. The second subpopulation consists of some new sampling individuals generated by the fitting-based prediction strategy. The third subpopulation is created by employing a recent sampling strategy, generating some effective search individuals for improving population convergence and diversity. Experimental results on a set of benchmark functions with a variety of different dynamic characteristics and difficulties illustrate that the proposed algorithm has competitive effectiveness compared with some state-of-the-art algorithms.

Original languageEnglish
Article numbere0254839
Number of pages39
JournalPloS ONE
Issue number8 August
Publication statusPublished - 3 Aug 2021


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