On self-adaptive stochastic ranking in decomposition many-objective evolutionary optimization

Abstract

Evolutionary multi-objective optimization problems have attracted increasingly attention in the evolutionary computing community. Now a lot of efforts have been devoted to this direction. For example, the proposed Pareto-dominated, decomposition-based and indicator-based methods have improved the scalability of multi-objective evolutionary algorithms (MOEAs). However, with the increase of the number of objectives, the portion of non-dominated individuals in the population is too large, and the distinguishability of individuals in the objective space decreases, which will affect the selection of elite solutions, thereby losing the balance of convergence and diversity. In this paper, a self-adaptive stochastic ranking method (SSR) is proposed to adaptively balance the convergence and diversity in high-dimensional space according to the state of the population. It has been embedded into the MOEA/D framework to form a novel multi-objective optimization algorithm, named MOEA/D-SSR. In addition, an improved shift-based density estimation strategy (ISDE) is adopted to enhance the convergence and diversity. Compared with the existing MOEAs on benchmark suite DTLZ and WFG with up to 10 objectives, the performance of the our algorithm has been verified. Experimental results show that the proposed algorithm is competitive compared with the most advanced MOEAs.