A simple and scalable particle swarm optimization structure based on linear system theory

Since it was first presented, particle swarm optimization (PSO) has experienced numerous improvements as a traditional optimization approach. PSO becomes more complex as a result of the majority of improvement strategies, which use learning model replacement or parameter adjustment to enhance PSO’s performance. Based on linear system theory, this study proposes a simple and scalable framework for restructuring particle swarm optimization (RPSO) and provides a new example of the RPSO algorithm framework, Q-RPSO. The RPSO framework adopts a single position updating formula instead of the original position and velocity updating formulas, which are unrelated to the PSO’s velocity and the current position. The experiments were carried out to compare with the standard PSO and six PSO variants based on CEC 2013 benchmark functions. The experimental results demonstrate that, whether in terms of global exploration capability or convergence accuracy, Q-RPSO outperforms all competitor algorithms.