Understanding the Limitations of Particle Swarm Algorithm for Dynamic Optimization Tasks

Abstract

One of the most widely used biomimicry algorithms is the Particle Swarm Optimization (PSO). Since its introduction in 1995, it has caught the attention of both researchers and academicians as a way of solving various optimization problems, such as in the fields of engineering and medicine, to computer image processing and mission critical operations. PSO has been widely applied in the field of swarm robotics, however, the trend of creating a new variant PSO for each swarm robotic project is alarming. We investigate the basic properties of PSO algorithms relevant to the implementation of swarm robotics and characterize the limitations that promote this trend to manifest. Experiments were conducted to investigate the convergence properties of three PSO variants (original PSO, SPSO and APSO) and the global optimum and local optimal of these PSO algorithms were determined. We were able to validate the existence of premature convergence in these PSO variants by comparing 16 functions implemented alongside the PSO variant. This highlighted the fundamental flaws in most variant PSOs, and signifies the importance of developing a more generalized PSO algorithm to support the implementation of swarm robotics. This is critical in curbing the influx of custom PSO and theoretically addresses the fundamental flaws of the existing PSO algorithm.