An Improved PSO with Time-Varying Accelerator Coefficients
Zhihua Cui, Jianchao Zeng, Yufeng Yin · 2008
Cognitive and social learning factors are two important parameters of particle swarm optimization (PSO), and many different settings have been proposed, in which one famous strategy is the linear manner proposed by Ratnaweera. However, due to the complex nature of the optimization problems, linear-type setting may not work well in many cases. Since the large cognitive coefficient provides a large local search capability, as well as the small one employs a large global search capability, three different non-linear settings are designed to further investigate the potential advantages among these two parameters. Simulation results show the concave function strategy is an effective manner especially for multi-modal functions.