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- W2890747025 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." @default.
- W2890747025 created "2018-09-27" @default.
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- W2890747025 date "2018-09-15" @default.
- W2890747025 modified "2023-09-29" @default.
- W2890747025 title "Memory-Enhanced Dynamic Multi-Objective Evolutionary Algorithm Based on Lp Decomposition" @default.
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- W2890747025 doi "https://doi.org/10.3390/app8091673" @default.
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