Paper Info
Title
An Ensemble Learning Based Prediction Strategy For Dynamic Multi-Objective Optimization
Abstract
Prediction strategies are widely-used in dynamic multi-objective evolutionary algorithms (DMOEAs). However, the characteristics of the environmental changes are different and only use one single prediction model cannot react to the changes effectively. The mismatching of the changes and prediction models may make the predicted results inaccurate and unstable. To overcome this shortage, an ensemble learning based prediction strategy (ELPS) is proposed in this paper to help algorithms reinitialize a new population after a change is detected. There are four base prediction models in ELPS, i.e., linear prediction model (LP), knee point-based autoregression model (KP-AR), population-based autoregression model (P-AR) and random re-initialization model (RND). Once a change happens, these four base prediction models are trained by the historical information with ensemble learning and a strong prediction model can be constructed on these four base prediction models. The final reinitialized population is generated by this strong prediction model to react to the new environment. With the help of ELPS, the re-initialized population can adapt different environmental changes and improve the performance on prediction accuracy and robustness. The experimental results show that, compared with other state-of-the-art prediction strategies on benchmark test suite, ELPS has better performance on dealing with dynamic multi-objective optimization problems. (C) 2020 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2020
10.1016/j.asoc.2020.106592
APPLIED SOFT COMPUTING
Keywords
DocType
Volume
Dynamic multi-objective evolutionary algorithm, Prediction strategy, Ensemble learning
Journal
96
ISSN
Citations 
PageRank 
1568-4946
7
0.52
References 
Authors
0
4
Name
Order
Citations
PageRank
Feng Wang119519.03
Yixuan Li2182.33
Fanshu Liao370.52
Hongyang Yan4327.09