Large scale multi-objective optimization algorithm with multiple strategies

Abstract

Abstract When dealing with large-scale multi-objective optimization problems (LSMOP), enhancing the dimensionality of decision variables tends to render the MOEA/D algorithm poor scalability in decision space and more susceptible to converging toward local optima. In response to this issue, this article proposed an improved large-scale MOEA/D algorithm with multiple strategies (MSMOEA/D). In the MSMOEA/D algorithm, a hybrid initialization strategy based on automatic encoder was introduced into multi-objective optimization to provide a better initial population. Moreover, a neighborhood adjustment strategy based on the aggregation function value was proposed, which dynamically adjusted the neighborhood in accordance with the evolutionary progression of the current population and the change degree of the aggregation function value, and thus can obtained better search capabilities. Furthermore, a mutation selection strategy based on non-dominated sorting is adopted within the optimization process. Different subproblems select mutation strategies according to the number of individuals located at the first level of non-dominated sorting to avoid the population falling into local optima and enhance the overall performance of the algorithm. Finally, both the MSMOEA/D algorithm and other existing algorithms are evaluated using LSMOP and DTLZ test problems. The experimental outcomes substantiate the effectiveness of the improved algorithm in solving LSMOPs.