A Population prediction strategy for evolutionary dynamic multiobjective optimization

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

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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
Divisions :	Surrey research (other units)
Authors :	Zhou, A, Jin, Y and Zhang, Q
Date :	January 2014
DOI :	10.1109/TCYB.2013.2245892
Depositing User :	Symplectic Elements
Date Deposited :	28 Mar 2017 10:50
Last Modified :	24 Jan 2020 12:30
URI:	http://epubs.surrey.ac.uk/id/eprint/806714

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