An improved particle swarm optimization based on wolves' activities circle
Bin Wei, Qinke Peng, Xiao Chen, Jing Zhao · 2012
Recently nature-inspired algorithms have attracted a lot of attentions. Particle swarm optimization (PSO) is one of the most successful nature-inspired algorithms. However, studies showed that PSO has some drawbacks such as easy to fall into the local optimum and slow convergence rate in the later iterations. In this paper, inspired by the wolves' activities circle we propose a novel PSO (named PSO_WOLVES). The PSO_WOLVES was tested on eight benchmark functions and compared with three modified PSO, and the results showed that our algorithm not only has better search ability but also has faster convergence speed.