Improved NSGA-III with selection-and-elimination operator

Abstract

Abstract A fast non-dominated sorting genetic algorithm based on reference-point strategy (NSGA-III) is a well-known many-objective optimization algorithm in which the reference-point strategy is incorporated to maintain population diversity. However, the convergence capability of NSGA-III is poor in many cases. In this paper, a new selection-and-elimination operator is designed to balance convergence and diversity. First, a selection operator is employed to identify the reference point with the minimum niche count, and then one individual with the shortest penalty-based boundary intersection distance is chosen. Second, a reference point with the maximum niche count is identified, and one individual with the longest penalty-based boundary intersection distance is removed by the elimination operator. To test performance, this modification is verified on benchmark problems with up to 15 objectives, and compared with five other state-of-the-art algorithms. Simulation results demonstrate that our modified strategy can achieve the better performance.