A Performance Class-Based Particle Swarm Optimizer

Abstract

One of the main concerns with Particle Swarm Optimization (PSO) is to increase or maintain diversity during search in order to avoid premature convergence. In this study, a Performance Class-Based learning PSO (PCB-PSO) algorithm is proposed, that not only increases and maintains swarm diversity but also improves exploration and exploitation while speeding up convergence simultaneously. In the PCB-PSO algorithm, each particle belongs to a class based on its fitness value and particles might change classes at evolutionary stages or search step based on their updated position. The particles are divided into an upper, middle and lower. In the upper class are particles with top fitness values, the middle are those with average while particles in the bottom class are the worst performing in the swarm. The number of particles in each group is predetermined. Each class has a unique learning strategy designed specifically for a given task. The upper class is designed to converge towards the best solution found, Middle class particles exploit the search space while lower class particles explore. The algorithm’s strength is its flexibility and robustness as the population of each class allows us to prioritize a desired swarm behavior. The Algorithm is tested on a set of 8 benchmark functions which have generally proven to be difficult to optimize. The algorithm is able to be on par with some cutting edge PSO variants and outperforms other swarm and evolutionary algorithms on a number of functions. On complex multimodal functions, it is able to outperform other PSO variants showing its ability to escape local optima solutions.