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

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.