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Appl. Sci. 2018, 8(9), 1673; https://doi.org/10.3390/app8091673

Memory-Enhanced Dynamic Multi-Objective Evolutionary Algorithm Based on Lp Decomposition

1
School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
2
Institute of Data Science and Technology, Shandong Normal University, Jinan 250358, China
3
School of Mathematics and Statistics, Xi’an JiaoTong University, Xi’an 710049, China
*
Author to whom correspondence should be addressed.
Received: 17 August 2018 / Revised: 4 September 2018 / Accepted: 4 September 2018 / Published: 15 September 2018
(This article belongs to the Special Issue Applied Sciences Based on and Related to Computer and Control)
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Abstract

Decomposition-based multi-objective evolutionary algorithms provide a good framework for static multi-objective optimization. Nevertheless, there are few studies on their use in dynamic optimization. To solve dynamic multi-objective optimization problems, this paper integrates the framework into dynamic multi-objective optimization and proposes a memory-enhanced dynamic multi-objective evolutionary algorithm based on L p decomposition (denoted by dMOEA/D- L p ). Specifically, dMOEA/D- L p decomposes a dynamic multi-objective optimization problem into a number of dynamic scalar optimization subproblems and coevolves them simultaneously, where the L p decomposition method is adopted for decomposition. Meanwhile, a subproblem-based bunchy memory scheme that stores good solutions from old environments and reuses them as necessary is designed to respond to environmental change. Experimental results verify the effectiveness of the L p decomposition method in dynamic multi-objective optimization. Moreover, the proposed dMOEA/D- L p achieves better performance than other popular memory-enhanced dynamic multi-objective optimization algorithms. View Full-Text
Keywords: multi-objective evolutionary optimization; memory enhancement; dynamic environment; decomposition method multi-objective evolutionary optimization; memory enhancement; dynamic environment; decomposition method
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Xu, X.; Tan, Y.; Zheng, W.; Li, S. Memory-Enhanced Dynamic Multi-Objective Evolutionary Algorithm Based on Lp Decomposition. Appl. Sci. 2018, 8, 1673.

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