University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Multi-directional Prediction Approach for Dynamic Multi-objective Optimization Problems

Rong, Miao, Gong, Dunwei, Zhang, Yong, Jin, Yaochu and Wang, Gaige (2018) Multi-directional Prediction Approach for Dynamic Multi-objective Optimization Problems IEEE Transactions on Cybernetics.

Multi-directional Prediction Approach for Dynamic Multi-objective Optimization Problems.pdf - Accepted version Manuscript

Download (11MB) | Preview


Various real-world multi-objective optimization problems are dynamic, requiring evolutionary algorithms to be able to rapidly track the moving Pareto front of an optimization problem once an environmental change occurs. To this end, several methods have been developed to predict the new location of the moving Pareto set so that the population can be reinitialized around the predicted location. In this paper, we present a multidirectional prediction strategy to enhance the performance of evolutionary algorithms in solving a dynamic multi-objective optimization problem. To more accurately predict the moving location of the Pareto set, the population is clustered into a number of representative groups by a proposed classification strategy, where the number of clusters is adapted according to the severity of the environmental change. To examine the performance of the proposed algorithm, the proposed prediction strategy is compared with four state-of-the-art prediction methods under the framework of particle swarm optimization as well as five popular evolutionary algorithms for dynamic multiobjective optimization. Our experimental results demonstrate that the proposed algorithm can effectively tackle dynamic multiobjective optimization problems.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
Rong, Miao
Gong, Dunwei
Zhang, Yong
Wang, Gaige
Date : 2018
Copyright Disclaimer : © 2017 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 : Dynamic multi-objective optimization; Multidirection prediction; Representative individual; Adaptation
Related URLs :
Depositing User : Clive Harris
Date Deposited : 29 May 2018 14:18
Last Modified : 18 Jun 2018 12:34

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