Particle swarm optimization with considering more locally best particles and Gaussian jumps
Yen-Ching Chang, Yilin Chen, Yongxuan Xu, Cheng-Hsueh Hsieh, Chin-Chen Chueh, Yu-Tien Huang, Cheng-Ting Hsieh · 2014
Studies have shown that the velocity updating formula of the standard particle swarm optimization (PSO) with considering more locally best particles has potential advantages compared to the original PSO. In addition, Gaussian mutation or jumps also help particles get away from local minima. In this paper, we will combine these two concepts into a single algorithm. Experimental results show that a combination of more locally best particles and Gaussian jumps into the standard PSO almost outperform the original PSO with Gaussian jumps.