Adaptive Opposition-Based Particle Swarm Optimization Algorithm and Application Research

Abstract

On the issue of low precision and easily falling into local optimum for particle swarm optimization (PSO), an adaptive opposition-based particle swarm optimization (AOPSO) is proposed by introducing opposition-based learning strategy to PSO, and the convergence is verified by simulation experiment of typical test functions. The simulation results show that under the same parameter set up and experimental conditions, the convergence speed and precision exaltation of AOPSO algorithm are both improved. Finally, for the practical application problem of radar network deployment, the optimal space deployment scheme of radar network is obtained through simulation experiments. Compared with PSO, all indicators have been improved, and the effectiveness of AOPSO algorithm is further verified.