Quantile-guided multi-strategy algorithm for dynamic multiobjective optimization

Abstract

Dynamic multiobjective optimization problems (DMOPs) require that the algorithm is capable of tracking the position of Pareto-optimal solution (PS) in a rapidly changing environment. Prediction-based and memory-based methods currently gain much attention for their good tracking ability. However, an elaborate predictor may not be suitable for different problems and the improper reuse of the memory may lower the quality of solution. To overcome these limitations, a quantile-guided multi-strategy algorithm (QMA) for dynamic multiobjective optimization is proposed in this paper. Quantile is always employed to represent characteristics of data because of its robustness to the outliers. In QMA, historical quantile information of decision space is used to find the new position of quantile. Then, the solution set is expanded based on the new quantile. Moreover, historical search information is used to assist in the evolution of population, which is achieved through selecting the mapping matrix as a judgment of retrieval. Meanwhile, the quantile-guided environmental change degree detector is employed to determine the number of retained and the randomly generated individuals when there is no similarity. Experimental results carried out on various DMOPs demonstrate that QMA is highly competitive compared with some state-of-the-art algorithms.