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

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
6 Downloads (Pure)


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

Bibliographical note

Funding Information:
This work is supported by the National Natural Science Foundation of China under Grants 62006103 and 61872168, in part by the Jiangsu national science research of high education under Grand 20KJB110021. The authors express sincerely appreciation to the anonymous reviewers for their helpful opinions.

Data Availability Statement

All relevant data are within the manuscript and its Supporting information files.


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