A dynamic multi-objective evolutionary algorithm based on polynomial regression and adaptive clustering

Abstract

• This paper proposes A Dynamic Multi-objective Evolutionary Algorithm based on Polynomial Regression and Adaptive Clustering. • A predictor based on polynomial regression is designed for generating initial population according to the change of Pareto-optimal set in the decision space. • An adaptive reference vector regulator based on K-means clustering is suggested to track the complex changes of Pareto-optimal front in the objective space. • The proposed algorithm shows the superiority over other dynamic multi-objective evolutionary algorithms. In this paper, a dynamic multi-objective evolutionary algorithm is proposed based on polynomial regression and adaptive clustering, called DMOEA-PRAC. As the Pareto-optimal solutions and fronts of dynamic multi-objective optimization problems (DMOPs) may dynamically change in the optimization process, two corresponding change response strategies are presented for the decision space and objective space, respectively. In the decision space, the potentially useful information contained in all historical populations is obtained by the proposed predictor based on polynomial regression, which extracts the linear or nonlinear relationship in the historical change. This predictor can generate good initial population for the new environment. In the objective space, in order to quickly adapt to the new environment, an adaptive reference vector regulator is designed in this paper based on K-means clustering for the complex changes of Pareto-optimal fronts, in which the adjusted reference vectors can effectively guide the evolution. Finally, DMOEA-PRAC is compared with some recently proposed dynamic multi-objective evolutionary algorithms and the experimental results verify the effectiveness of DMOEA-PRAC in dealing with a variety of DMOPs.