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

• Main Authors: Qingyang Zhang, Shouyong Jiang, Shengxiang Yang, Hui Song
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2021-01-01
• Series: PLoS ONE
• Online Access: https://doi.org/10.1371/journal.pone.0254839

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.