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

Date

2021-08-03

Advisors

Journal Title

Journal ISSN

ISSN

Volume Title

Publisher

PLOS

Type

Article

Peer reviewed

Yes

Abstract

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.

Description

open access article

Keywords

Dynamic multi-objective optimization, fitting-based prediction, multi-objective estimation of distribution algorithm

Citation

Zhang, Q., Jiang, S., Yang, S. and Song, H. (2021) Solving dynamic multi-objective problems with a new prediction-based optimization algorithm. PLoS ONE, 16 (8), e0254839

Rights

Research Institute

Institute of Artificial Intelligence (IAI)