Journal
SOFT COMPUTING
Volume 19, Issue 9, Pages 2633-2653Publisher
SPRINGER
DOI: 10.1007/s00500-014-1433-3
Keywords
Dynamic multiobjective optimization; Evolutionary algorithms; Prediction; Memory
Categories
Funding
- National Natural Science Foundation of China [61379062, 61372049]
- Science and Technology Project of Hunan Province [2014GK3027]
- Science Research Project of the Education Office of Hunan Province [12A135, 12C0378]
- Hunan Province Natural Science Foundation [14JJ2072, 13JJ8006]
- Hunan Provincial Innovation Foundation For Postgraduate [CX2013A011]
- Construct Program of the Key Discipline in Hunan Province
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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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