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Novel prediction and memory strategies for dynamic multiobjective optimization

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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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Acknowledgments

The authors wish to thank the support of the National Natural Science Foundation of China (No. 61379062, 61372049), the Science and Technology Project of Hunan Province (NO. 2014GK3027), the Science Research Project of the Education Office of Hunan Province (Grant No. 12A135, 12C0378), the Hunan Province Natural Science Foundation (Grant No. 14JJ2072, 13JJ8006), the Hunan Provincial Innovation Foundation For Postgraduate (Grant No. CX2013A011), and the Construct Program of the Key Discipline in Hunan Province.

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Authors and Affiliations

  1. Key Laboratory of Intelligent Computing and Information Processing, Ministry of Education, Information Engineering College of Xiangtan University, Xiangtan, Hunan Province, China

    Zhou Peng, Jinhua Zheng & Juan Zou

  2. Hunan University of Science and Technology, Xiangtan, Hunan Province, China

    Min Liu

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  1. Zhou Peng
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  2. Jinhua Zheng
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  3. Juan Zou
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  4. Min Liu
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Correspondence to Jinhua Zheng.

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Communicated by V. Loia.

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Peng, Z., Zheng, J., Zou, J. et al. Novel prediction and memory strategies for dynamic multiobjective optimization. Soft Comput 19, 2633–2653 (2015). https://doi.org/10.1007/s00500-014-1433-3

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