Empirical study of particle swarm optimization

作者：Yibing Shi, R.C. Eberhart · 年份：2003 · DOI：10.1109/cec.1999.785511 · 被引用次数：3955 · 研究领域：Metaheuristic Optimization Algorithms Research、Advanced Multi-Objective Optimization Algorithms、Evolutionary Algorithms and Applications

We empirically study the performance of the particle swarm optimizer (PSO). Four different benchmark functions with asymmetric initial range settings are selected as testing functions. The experimental results illustrate the advantages and disadvantages of the PSO. Under all the testing cases, the PSO always converges very quickly towards the optimal positions but may slow its convergence speed when it is near a minimum. Nevertheless, the experimental results show that the PSO is a promising optimization method and a new approach is suggested to improve PSO's performance near the optima, such as using an adaptive inertia weight.