Particle Swarm Optimization Algorithm Based on Robust Control of Random Discrete Systems
Le Yang, Dakuo He, Qingkai Wang, Jiahuan Luo, Yingjie Huang, Zipeng Zhen · 2017
Particle swarm optimization (PSO) algorithm is a random search method based on population evolution. At present, most of the convergence and search stability of the study are using a method of analyzing deterministic systems. Although these studies have achieved fruitful results, but they ignore the randomness of the PSO algorithm which is the most important feature. The complexity and uncertainty of the algorithm is due to the existence of random factors. This paper considers the stochastic factors of the PSO algorithm, from the point of view of stochastic system robust control, a novel PSO algorithm model based on discrete stochastic control system is proposed. Then the robust controllability analysis of the model is carried out, and the robust controllable condition and control law are obtained. Based on this, a new particle swarm optimization algorithm, named particle swarm optimization algorithm based on random robust control is proposed. The simulation results verify the effectiveness of proposed algorithm.