Research on Target Localization Based on Improved Multi-Swarm Particle Swarm Optimization Algorithm

An improved algorithm based on comprehensive learning and adaptive mutation is proposed in view of the shortcoming of multi-swarm particle swarm optimization (MCPSO), which still has low convergence speed and bad solution accuracy. The method quickens the convergence rate by sharing the best information of all swarms, and improves convergence accuracy by adaptive mutation. The simulation results indicate that it could carry on the localization effectively through adopting the improved multi-swarm particle swarm optimization algorithm. when the variance of random noise interference is 0.5, the localization RMSE is below 0.8 m, and has high convergence speed and steady performance.