A Population prediction strategy for evolutionary dynamic multiobjective optimization
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
---|---|
Article number | 6471286 |
Pages (from-to) | 40-53 |
Journal / Publication | IEEE Transactions on Cybernetics |
Volume | 44 |
Issue number | 1 |
Publication status | Published - Jan 2014 |
Externally published | Yes |
Link(s)
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.
Research Area(s)
- Dynamic multiobjective optimization, evolutionary algorithm, prediction, time series
Citation Format(s)
A Population prediction strategy for evolutionary dynamic multiobjective optimization. / Zhou, Aimin; Jin, Yaochu; Zhang, Qingfu.
In: IEEE Transactions on Cybernetics, Vol. 44, No. 1, 6471286, 01.2014, p. 40-53.
In: IEEE Transactions on Cybernetics, Vol. 44, No. 1, 6471286, 01.2014, p. 40-53.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review