Solving dynamic multi-objective problems using polynomial fitting-based prediction algorithm

Date

2022-08-11

Advisors

Journal Title

Journal ISSN

ISSN

0020-0255

Volume Title

Publisher

Elsevier

Type

Article

Peer reviewed

Yes

Abstract

Recently, dynamic multi-objective optimization has received growing attention due to its popularity in real-world applications. Inspired by polynomial fitting, this paper proposes a polynomial fitting-based prediction algorithm (PFPA) and incorporates it into the model-based multi-objective estimation of distribution algorithm (RM-MEDA) for solving dynamic multi-objective optimization problems. When an environment change is detected, the main mission of PFPA is to predict high-quality search populations for tracking the moving Pareto-optimal set effectively. Firstly, the non-dominated solutions obtained in past environments are utilized to predict high-quality solutions based on a multi-step movement strategy. Secondly, a polynomial fitting-based strategy is designed to fit the distribution of variables according to the obtained search populations, and capture the relationship between variables in the new search environment. Thirdly, some effective search agents are generated for improving population convergence and diversity based on characteristics of variables. To evaluate the performance of the proposed algorithm, experimental results on a set of benchmark functions, with a variety of different dynamic characteristics and difficulties, and two classical dynamic engineering design problems show that PFPA is competitive with some state-of-the-art algorithms.

Description

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.

Keywords

Dynamic multi-objective optimization, Polynomial fitting, Prediction mechanism, Dynamic engineering design

Citation

Q. Zhang, X. He, S. Yang, Y. Dong, H. Song, and S. Jiang. (2022) Solving dynamic multi-objective problems using polynomial fitting-based prediction algorithm. Information Sciences, 610, pp. 868-886

Rights

Research Institute

Institute of Artificial Intelligence (IAI)