A novel quantile-guided dual prediction strategies for dynamic multi-objective optimization

Abstract

Dynamic multi-objective optimization problems (DMOPs) require evolutionary algorithms (EAs) to accurately track the Pareto-optimal Front (PF) and generate the solutions along the PF in the constantly changing environment. In order to solve the DMOPs, a novel quantile-guided dual prediction strategies evolutionary algorithm (NQDPEA) is proposed in this paper. Quantiles are often employed to characterize data in statistics. In NQDPEA, the evolution of the population is guided by the quantile, which is to predict the position of the quantile in a new environment through historical quantile information. Then, a new solution set is expanded according to the location of the new quantile. Moreover, its prediction strategies not only predict Pareto-optimal set (PS) by quantile in the decision space but also predict the PF by quantile in objective space and then mapping back to decision space. Through the adaptive combination strategy, the proportion of the new solutions produced by each prediction strategy changes adaptively. To prove the performance of NQDPEA, it is compared with four powerful EAs on 13 test instances. Experimental results show that NQDPEA can effectively generate high quality solutions uniformly along PF.