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A Population prediction strategy for evolutionary dynamic multiobjective optimization

Zhou, A and Jin, Y and Zhang, Q (2014) 'A Population prediction strategy for evolutionary dynamic multiobjective optimization.' IEEE Transactions on Cybernetics, 44 (1). 40 - 53. ISSN 2168-2267

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Abstract

This paper investigates how to use prediction strategies to improve the performance of multiobjective evolutionary optimization algorithms in dealing with dynamic environments. Prediction-based methods have been applied to predict some isolated points in both dynamic single objective optimization and dynamic multiobjective optimization. We extend this idea to predict a whole population by considering the properties of continuous dynamic multiobjective optimization problems. In our approach, called population prediction strategy (PPS), a Pareto set is divided into two parts: a center point and a manifold. A sequence of center points is maintained to predict the next center, and the previous manifolds are used to estimate the next manifold. Thus, PPS could initialize a whole population by combining the predicted center and estimated manifold when a change is detected. We systematically compare PPS with a random initialization strategy and a hybrid initialization strategy on a variety of test instances with linear or nonlinear correlation between design variables. The statistical results show that PPS is promising for dealing with dynamic environments. © 2013 IEEE.

Item Type: Article
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 12 Nov 2014 20:24
Last Modified: 05 Feb 2019 15:15
URI: http://repository.essex.ac.uk/id/eprint/11562

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