Memory-enhanced dynamic multi-objective evolutionary algorithm based on Lp decomposition

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 Lp decomposition (denoted by dMOEA/D-Lp). Specifically, dMOEA/D-Lp decomposes a dynamic multi-objective optimization problem into a number of dynamic scalar optimization subproblems and coevolves them simultaneously, where the Lp 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 Lp decomposition method in dynamic multi-objective optimization. Moreover, the proposed dMOEA/D-Lp achieves better performance than other popular memory-enhanced dynamic multi-objective optimization algorithms.