The impact of parameter adjustment strategies on the performance of particle swarm optimization algorithm
Zhang Xun, Juelong Li, Xing Jianchun, Wang Ping, Qiliang Yang · 2015
To determine the reasonable parameter settings of particle swarm optimization (PSO) algorithm, this paper discusses the impact of the time-varying inertia weight and velocity-based mutation strategies on the performance of PSO algorithm. The performance of the PSO algorithm with these two kinds of parameters adjustment strategies are tested through four well-known benchmark functions. The simulation results show that the PSO algorithm has better convergence performance with the quickly decreasing inertia weight. Also, the velocity-based mutation strategy will slow down the convergence speed of PSO algorithm if the global solutions over the adjacent generations are close to each other.