Chaotic particle swarm optimization algorithm based on adaptive inertia weight
Junwei Li, Yongmei Cheng, Ke-zhe Chen · 2014
In order to overcome the disadvantages of premature and local convergence in the traditional particle swarm optimization (PSO), an improved chaotic PSO algorithm based on adaptive inertia weight (AIWCPSO) is proposed. The initial population is generated by using chaotic mapping appropriately, in order to improve both the diversity of population and the periodicity of particles. The value of the new inertia weight is adjusted adaptively by feedback parameters, which including iterative number, aggregation degree factor and the improved evolution speed parameter. We judge premature convergence by the relationship between the variance of the population's fitness and the set threshold, if it occurs, we add chaotic disturbance to make it jump out of the local optima. Experimental results on four well-known benchmark functions show that: the AIWCPSO algorithm improves the convergence accuracy and has the ability of suppressing premature convergence.