A variant with a time varying PID controller of particle swarm optimizers

Abstract

This paper presents a novel variant of particle swarm optimizers (PSOs) that we call the proportional integral derivative (PID) controller inspired particle swarm optimizer (PidSO), which uses a novel evolutionary strategy whereby a specified PID controller is used to improve particles’ local and global best positions information. This strategy enables PidSO to improve the diversity of swarm in a bid to discourage premature convergence and to perform a global search over the entire search space more efficiently. Empirical experiments were conducted on both analytically unimodal and multimodal test functions. The experimental results demonstrate that PidSO enhances the diversity of the swarm and features better search effectiveness and efficiency in solving most multimodal optimization problems when compared with other recent variants of PSOs and evolutionary optimization algorithms such as integral-controlled PSO (ICPSO), PID-controlled PSO (PIDCPSO), comprehensive learning PSO (CLPSO), I-population covariance matrix adaptation evolution strategy (IPOP-CMA-ES), and a multi algorithm genetically adaptive method for single objective optimization (AMALGAM-SO), and so on. Additionally, it has been observed that PidSO is able to achieve comparatively better success rates and success performances though it is more complex, and that the performance of PidSO is promoted by selecting proper law based controllers. Consequently, PidSO offers a new solution to real engineering optimization designs of industrial systems.