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Erschienen in: Soft Computing 9/2015

01.09.2015 | Methodologies and Application

Novel prediction and memory strategies for dynamic multiobjective optimization

verfasst von: Zhou Peng, Jinhua Zheng, Juan Zou, Min Liu

Erschienen in: Soft Computing | Ausgabe 9/2015

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Abstract

Dynamic multiobjective optimization problems (DMOPs) exist widely in real life, which requires the optimization algorithms to be able to track the Pareto optimal solution set after the change efficiently. In this paper, novel prediction and memory strategies (PMS) are proposed to solve DMOPs. Regarding prediction, the prediction strategy contains two parts, i.e., exploration and exploitation. Exploration can enhance the ability to search the entire solution space, making it adapt to the environmental change with a great extent. Exploitation can improve the accuracy of local search, making the algorithm to have a faster response to environmental change particularly in the solution set having relevance in the environment. In terms of memory, an optimal solution set preservation mechanism is employed, by reusing the previously found elite solutions, which improves the performance of the algorithm in solving periodic problems. Compared with two representative prediction strategies and a hybrid strategy combining prediction and memory both on seven traditional benchmark problems and on five newly appeared ones, PMS has been shown to have faster response to the environmental changes than the peer algorithms, performing well in terms of convergence and diversity.

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Metadaten
Titel
Novel prediction and memory strategies for dynamic multiobjective optimization
verfasst von
Zhou Peng
Jinhua Zheng
Juan Zou
Min Liu
Publikationsdatum
01.09.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 9/2015
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-014-1433-3

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