Combine Discussion Mechanism and Chaos Strategy on Particle Swarm optimization Algorithm
Xinwen Fan, Yin Ye, Zhenyi Chen, Zhixing Hong, Zhenyu Qiu, Chen Dong · 2019
Particle swarm optimization (PSO) is one of the artificial intelligence algorithms and has been proved to be an effective global optimization method. In this paper, a novel particle swarm optimization algorithm (DC-PSO) combining the discussion mechanism and chaos strategy is proposed to improve the standard PSO (SPSO) which is easy to trap into local optimum and has the problems of premature convergence. The four main strategies adapted in DC-PSO are chaos initializes the population, dynamic change of population, discussion mechanism of double populations and random variation of particles. Experiments are performed on eight benchmark functions to test the accuracy and performance of DC-PSO. Experiments show the proposed DC-PSO performs better than DEs intelligent algorithms.