NSGA-III algorithm with maximum ranking strategy for many-objective optimisation
Fei Xue, Di Wu
Abstract
In recent years, a non-dominated sorting genetic algorithm III (NSGA-III) based on decomposition strategy had been extensively studied. However, there are still problems of lower Pareto selection pressure and insufficient diversity maintenance mechanism. To address these problems, NSGAIII algorithm with maximum ranking strategy (NSGAIII-MR) is proposed. In this algorithm, the convergence and diversity distance are balanced by adaptive parameter settings to achieve better performance. The maximum ranking strategy exploits the perpendicular distance from the solution to the weight vector to increase Pareto selection pressure. Moreover, the diversity of population is maintained with the reference point strategy to guide the solutions closer to the real Pareto front. Comparing with NSGAIII, the NSGAIII-MR algorithm enhances selection pressure and has good convergence and diversity performance. Also, the performance of algorithm is verified by comparing with other state-of-the-art evolutionary algorithms on the benchmark problems and the NSGAIII-MR is competitive.