Experimental analysis on the operation of Particle Swarm Optimization

In Particle Swarm Optimization, it has been observed that swarms often stall as opposed to converge. A stall occurs when all of the forward progress that could occur is instead rejected as Failed Exploration. Since the swarms particles are in good regions of the search space with the potential to make more progress, the introduction of perturbations to the pbest positions can lead to significant improvements in the performance of standard Particle Swarm Optimization. The pbest perturbation has been supported by a line search technique that can identify unimodal, globally convex, and non-globally convex search spaces, as well as the approximate size of attraction basin. A deeper analysis of the stall condition reveals that it involves clusters of particles that are performing exploitation, and these clusters are separated by individual particles that are performing exploration. This stall pattern can be identified by a newly developed method that is efficient, accurate, real-time, and search space independent. A more targeted (heterogenous) modification for stall is presented for globally convex search spaces

Improved exploration and exploitation in particle swarm optimization

Exploration and exploitation are analyzed in Particle Swarm Optimization (PSO) through a set of experiments that make new measurements of these key features. Compared to analyses on diversity and particle trajectories, which focus on particle motions and their potential to achieve exploration and exploitation, our analysis also focuses on the pbest positions that reflect the actual levels of exploration and exploitation that have been achieved by PSO. A key contribution of this paper is a clear criterion for when restarting particles can be expected to be a useful strategy in PSO

Improved exploration and exploitation in particle swarm optimization

Exploration and exploitation are analyzed in Particle Swarm Optimization (PSO) through a set of experiments that make new measurements of these key features. Compared to analyses on diversity and particle trajectories, which focus on particle motions and their potential to achieve exploration and exploitation, our analysis also focuses on the pbest positions that reflect the actual levels of exploration and exploitation that have been achieved by PSO. A key contribution of this paper is a clear criterion for when restarting particles can be expected to be a useful strategy in PSO