caa a22 4500 605468761 CHVBK 20210128100318.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s00500-014-1433-3 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1433-3 Novel prediction and memory strategies for dynamic multiobjective optimization [Elektronische Daten] [Zhou Peng, Jinhua Zheng, Juan Zou, Min Liu] 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. Springer-Verlag Berlin Heidelberg, 2014 Dynamic multiobjective optimization nationallicence Evolutionary algorithms nationallicence Prediction nationallicence Memory nationallicence Peng Zhou Key Laboratory of Intelligent Computing and Information Processing, Ministry of Education, Information Engineering College of Xiangtan University, Xiangtan, Hunan Province, China aut Zheng Jinhua Key Laboratory of Intelligent Computing and Information Processing, Ministry of Education, Information Engineering College of Xiangtan University, Xiangtan, Hunan Province, China aut Zou Juan Key Laboratory of Intelligent Computing and Information Processing, Ministry of Education, Information Engineering College of Xiangtan University, Xiangtan, Hunan Province, China aut Liu Min Hunan University of Science and Technology, Xiangtan, Hunan Province, China aut Soft Computing Springer Berlin Heidelberg 19/9(2015-09-01), 2633-2653 1432-7643 19:9<2633 2015 19 500 https://doi.org/10.1007/s00500-014-1433-3 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1433-3 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Peng Zhou Key Laboratory of Intelligent Computing and Information Processing, Ministry of Education, Information Engineering College of Xiangtan University, Xiangtan, Hunan Province, China aut NATIONALLICENCE 700 1- Zheng Jinhua Key Laboratory of Intelligent Computing and Information Processing, Ministry of Education, Information Engineering College of Xiangtan University, Xiangtan, Hunan Province, China aut NATIONALLICENCE 700 1- Zou Juan Key Laboratory of Intelligent Computing and Information Processing, Ministry of Education, Information Engineering College of Xiangtan University, Xiangtan, Hunan Province, China aut NATIONALLICENCE 700 1- Liu Min Hunan University of Science and Technology, Xiangtan, Hunan Province, China aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/9(2015-09-01), 2633-2653 1432-7643 19:9<2633 2015 19 500