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A Population Prediction Strategy for Evolutionary Dynamic Multiobjective Optimization
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AI
DOI：10.1109/TCYB.2013.2245892.png)
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摘要
En 中文
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.
Keyword:
Dynamic multiobjective optimization
evolutionary algorithm
prediction
time series
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
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