An evolutionary performance-enhanced PSO approach by using a BP neural-learning-based PID controller

In this paper, a novel evolutionary strategy-based particle swarm optimisation (PSO) approach is presented. The evolutionary strategy is dependent on a BP neural proportional integral derivative (PID) controller, which is intentionally placed between the position and the global best position in a closed loop of their relationship. The BP neural PID controller is designed to aid the position to well track the global best position. Furthermore, it can be utilised to improve the evolutionary dynamics of the PSO algorithm. The experiments for performance evaluation are tried on both analytical benchmark functions and an automatic control system of pulp consistency in pulping and paper-making engineering. The experimental results illustrate that the proposed approach enhances the diversity of swarms, considerably improves the global convergence efficiency and outperforms the PSO algorithm. In addition, it is also easily used in real industrial practice and offers a novel and convenient solution to engineering optimisation design of industrial systems.