IM-NSGAII: A novel approach to boost convergence speed and population diversity in multi-objective optimization

Abstract

Convergence speed and population diversity have long been central concerns in multi-objective evolutionary algorithms. However, the NSGAII algorithm often shows insufficient ability to maintain diversity when facing complex Pareto fronts. To address this issue, an improved NSGAII algorithm (IM-NSGAII) is proposed. First, a population evaluation technique is incorporated after non-dominated sorting to filter and select the best parent population. Second, a sparse population strategy with a high-pressure criterion is employed to guide sparse individuals in local exploration, thereby enhancing population diversity. Finally, a difference operator is introduced to facilitate information exchange among sparse individuals, compensating for the slow convergence speed of the original algorithm. The proposed IM-NSGAII is evaluated against five widely used algorithms on the ZDT, DTLZ, MaF, and WFG benchmark problems. Experimental results demonstrate that IM-NSGAII significantly improves both population diversity and convergence speed.