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.

Cellular Organism Based Particle Swarm Optimization Algorithm for Complex Non-Linear Problems

“…The chronology of PSO evolution and comprehensive PSO based methods surveyed by (Davoud Sedighizadeh & Ellips Masehian, 2009) …”

Particle swarm optimization with crossover: a review and empirical analysis

“…Many improvements have been made to the standard PSO (Engelbrecht, 2005;Poli et al, 2007;Sedighizadeh and Masehian, 2009). Hybrid PSO algorithms have been developed that incorporate operators from evolutionary algorithms into PSO.…”

A Deoxyribonucleic Acid Compression Algorithm Using Auto-Regression and Swarm Intelligence

“…Where: W max = Initial weight W min = Final weight iter max = Maximum iteration number iter = Current iteration number According to (Sedighizadeh and Masehian, 2009) more than ninety modification are applied to original PSO, in this research the original PSO with dynamic weighting factor is applied to solve the optimization problem of the compression of DNA sequences using AR by determining the linear prediction coefficients, since these coefficients of the AR are numbers between 0 and 1, the PSO role here is to optimize the coefficients to reach maximum compression rate.…”