A decision variable classification strategy based on the degree of environmental change for dynamic multiobjective optimization

Abstract

Dynamic multiobjective optimization problems (DMOPs) are constantly changing over time, which requires algorithms to keep track of the location of the Pareto optimal front (POF) at different moments in time. In this work, a decision variable classification strategy based on the degree of environmental change (DVCEC) is proposed. To accurately capture the occurrence of environmental changes, DVCEC designs an adaptive change detection method based on multiple regions. Since environmental changes affect each decision variable to different degrees, DVCEC classifies decision variables into several types and applies an appropriate prediction method to each type. In addition, an adjustment strategy is developed to minimize the impact of inaccurate predictions. The proposed DVCEC is evaluated on 22 benchmark problems and compared with four algorithms. Statistical results show that DVCEC can quickly approach POF and uniformly distribute it in most problems.