Dynamic multi-objective evolutionary algorithm with objective space prediction strategy

Abstract

To solve dynamic multi-objective optimization problems, a dynamic multi-objective evolutionary algorithm (DMOEA) must be able to deal with the dynamics of the environment, and such modifications can lead to new optimal solutions over time. Various algorithms have been proposed that modify the way a change is handled. Among them, prediction-based methods are promising for solving this kind of problem. They provide guided direction for population evolution through a prediction mechanism that assists the DMOEA to respond quickly to new changes. Based on these strategies, we propose a dynamic non-dominated sorting differential evolution improvement with prediction in the objective space (DOSP-NSDE). The proposal uses the objective space prediction (OSP) strategy for both the static evolutionary process (between changes) and the change reaction mechanism to predict the new optimal front location. Experiments were performed on a real-world problem and four sets of test problems: FDA, dMOP, UDF, and DF. Comparison of DOSP-NSDE with several algorithms in the literature, considering three metrics, is presented, showing that the proposal is competitive with most problems.