An improved NSGA-II with dimension perturbation and density estimation for multi-objective DV-Hop localisation algorithm

Abstract

NSGA-II is a well-known multi-objective optimisation algorithm, which has shown excellent performance on many multi-objective optimisation problems. However, the classical NSGA-II suffers from uneven distribution of convergence, and poor global search ability. To address these issues, this paper proposes an improved NSGA-II (INSGA-II) by employing two strategies: a crossover operation based on dimension perturbation and a novel updating operation based on average individual density estimation. Then the INSGA-II is applied to optimise the multi-objective DV-Hop localisation algorithm. To verify the effectiveness of proposed INSGA-II, we compare it with four other multi-objective evolutionary algorithms on six benchmark functions. Simulation results show that our approach outperforms other compared algorithms. What's more, the performance of the DV-Hop algorithm based on INSGA-II is tested by the simulation experiments. The simulation results show that the DV-Hop localisation with INSGA-II achieves better localisation accuracy than that with CS, WOCS, MODE and NSGA-II.