Particle swarm optimization using multi-information characteristics of all personal-best information.
Convergence stagnation is the chief difficulty to solve hard optimization problems for most particle swarm optimization variants. To address this issue, a novel particle swarm optimization using multi-information characteristics of all personal-best information is developed in our research. In the modified algorithm, two positions are defined by personal-best positions and an improved cognition term with three positions of all personal-best information is used in velocity update equation to enhance the search capability. This strategy could make particles fly to a better direction by discovering useful information from all the personal-best positions. The validity of the proposed algorithm is assessed on twenty benchmark problems including unimodal, multimodal, rotated and shifted functions, and the results are compared with that obtained by some published variants of particle swarm optimization in the literature. Computational results demonstrate that the proposed algorithm finds several global optimum and high-quality solutions in most case with a fast convergence speed.
School of Internet of Things Engineering, Jiangnan University, 1800 Lihu Avenue, Wuxi, 214122 Jiangsu Province China.
Song
S
Huang
Department of Educational Technology, Jiangnan University, 1800 Lihu Avenue, Wuxi, Jiangsu Province 214122 China.
Na
N
Tian
School of Internet of Things Engineering, Jiangnan University, 1800 Lihu Avenue, Wuxi, 214122 Jiangsu Province China.
Yan
Y
Wang
School of Internet of Things Engineering, Jiangnan University, 1800 Lihu Avenue, Wuxi, 214122 Jiangsu Province China ; Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi, 214122 China.
Zhicheng
Z
Ji
Intelligence algorithm; Particle swarm optimization; Personal-best position; Premature convergence