University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Multi-Modal Optimization Enhanced Cooperative Coevolution for Large-Scale Optimization

Peng, Xingguang, Jin, Yaochu and Wang, Handing (2018) Multi-Modal Optimization Enhanced Cooperative Coevolution for Large-Scale Optimization IEEE Transactions on Cybernetics, 2168-2.

[img]
Preview
Text
Multi-Modal Optimization Enhanced Cooperative Coevolution for Large-Scale Optimization.pdf - Accepted version Manuscript

Download (1MB) | Preview

Abstract

Cooperative coevolutionary algorithms decompose a problem into several subcomponents and optimize them separately. Such a divide-and-conquer strategy makes cooperative coevolutionary algorithms potentially well suited for large-scale optimization. However, decomposition may be inaccurate, resulting in a wrong division of the interacting decision variables into different subcomponents and thereby a loss of important information about the topology of the overall fitness landscape. In this paper, we suggest an idea that concurrently searches for multiple optima and uses them as informative representatives to be exchanged among subcomponents for compensation. To this end, we incorporate a multi-modal optimization procedure into each subcomponent, which is adaptively triggered by the status of subcomponent optimizers. In addition, a non-dominance based selection scheme is proposed to adaptively select one complete solution for evaluation from the ones that constructed by combining informative representatives from each subcomponent with a given solution. The performance of the proposed algorithm has been demonstrated by comparing five popular cooperative coevolutionary algorithms on a set of selected problems that are recognized to be hard for traditional cooperative coevolutionary algorithms. The superior performance of the proposed algorithm is further confirmed by a comprehensive study that compares 17 state-of-the-art cooperative coevolutionary algorithms and other metaheuristic algorithms on 20 1000-dimensional benchmark functions.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
NameEmailORCID
Peng, Xingguang
Jin, YaochuYaochu.Jin@surrey.ac.uk
Wang, Handinghanding.wang@surrey.ac.uk
Date : 6 July 2018
DOI : 10.1109/TCYB.2018.2846179
Copyright Disclaimer : © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works.
Uncontrolled Keywords : Cooperative coevolutionary algorithm; Multimodal optimization; Information compensation; Large-scale optimization
Related URLs :
Depositing User : Clive Harris
Date Deposited : 08 Jun 2018 08:01
Last Modified : 11 Dec 2018 11:24
URI: http://epubs.surrey.ac.uk/id/eprint/847023

Actions (login required)

View Item View Item

Downloads

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