Particle Swarm Optimization Methods, Taxonomy and Applications

Abstract

The Particle Swarm Optimization (PSO) algorithm, as one of the latest algorithms inspired from the nature, was introduced in the mid 1990s and since then, it has been utilized as an optimization tool in various applications, ranging from biological and medical applications to computer graphics and music composition. In this paper, following a brief introduction to the PSO algorithm, the chronology of its evolution is presented and all major PSO-based methods are comprehensively surveyed. Next, these methods are studied separately and their important factors and parameters are summarized in a comparative table. In addition, a new taxonomy of PSO-based methods is presented. It is the purpose of this paper is to present an overview of previous and present conditions of the PSO algorithm as well as its opportunities and challenges. Accordingly, the history, various methods, and taxonomy of this algorithm are discussed and its different applications together with an analysis of these applications are evaluated. among agents on survival of the fittest. Algorithms related to this group include Evolutionary Programming (EP), Genetic Programming (GP), and Differential Evolutionary (DE). The Ontogeny group is associated with the algorithms in which the adaptation of a special organism to its environment is happened. The algorithms like PSO and Genetic Algorithms (GA) are of this type and in fact, they have a cooperative nature in comparison with other types (16). The advantages of above-mention ed categories can be noted as their ability to be developed for various applications and not needing the previous knowledge of the problem space. Their drawbacks include no guarantee in finding an optimum solution and high computational costs in completing Fitness Function (F.F.) in intensive iterations. Among the aforementioned paradigms, the PSO algorithm seems to be an attractive one to study since it has a simple but efficient nature added to being novel. It can even be a substitution for other basic and important evolutionary algorithms. The most important similarity between these paradigms and the GA is in having the seam interactive population. This algorithm, compared to GA, has a faster speed in finding the solutions close to the optimum and it is faster than GA in premature convergence (4).