University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Solving Many-Objective Optimization Problems via Multistage Evolutionary Search

Chen, Huangke, Cheng, Ran, Pedrycz, Witold and Jin, Yaochu (2019) Solving Many-Objective Optimization Problems via Multistage Evolutionary Search IEEE Transactions on Systems, Man, and Cybernetics: Systems. pp. 1-13.

Solving Many-Objective Optimization Problems via Multistage Evolutionary Search.pdf - Accepted version Manuscript

Download (4MB) | Preview


With the increase in the number of optimization objectives, balancing the convergence and diversity in evolutionary multiobjective optimization becomes more intractable. So far, a variety of evolutionary algorithms have been proposed to solve many-objective optimization problems (MaOPs) with more than three objectives. Most of the existing algorithms, however, find difficulties in simultaneously counterpoising convergence and diversity during the whole evolutionary process. To address the issue, this paper proposes to solve MaOPs via multistage evolutionary search. To be specific, a two-stage evolutionary algorithm is developed, where the convergence and diversity are highlighted during different search stages to avoid the interferences between them. The first stage pushes multiple subpopulations with different weight vectors to converge to different areas of the Pareto front. After that, the nondominated solutions coming from each subpopulation are selected for generating a new population for the second stage. Moreover, a new environmental selection strategy is designed for the second stage to balance the convergence and diversity close to the Pareto front. This selection strategy evenly divides each objective dimension into a number of intervals, and then one solution having the best convergence in each interval will be retained. To assess the performance of the proposed algorithm, 48 benchmark functions with 7, 10, and 15 objectives are used to make comparisons with five representative many-objective optimization algorithms.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computer Science
Authors :
Chen, Huangke
Cheng, Ran
Pedrycz, Witold
Date : 7 August 2019
DOI : 10.1109/TSMC.2019.2930737
Copyright Disclaimer : © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : Evolutionary algorithm; Many-objective optimization; Multistage optimization; Convergence; Sociology; Shape; Evolutionary computation; Pareto optimization
Depositing User : Clive Harris
Date Deposited : 09 Aug 2019 08:47
Last Modified : 09 Aug 2019 08:47

Actions (login required)

View Item View Item


Downloads per month over past year

Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800