Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives

TL;DR

This survey comprehensively reviews Particle Swarm Optimization (PSO), covering its historical development, key concepts, enhancements, hybridization strategies, and recent advances in various application domains.

Contribution

It provides an extensive overview of PSO's evolution, implementation details, and hybridization approaches, highlighting recent developments and future research directions.

Findings

Detailed analysis of PSO variants and improvements

Discussion on hybridization with other algorithms

Insights into PSO applications and convergence strategies

Abstract

Particle Swarm Optimization (PSO) is a metaheuristic global optimization paradigm that has gained prominence in the last two decades due to its ease of application in unsupervised, complex multidimensional problems which cannot be solved using traditional deterministic algorithms. The canonical particle swarm optimizer is based on the flocking behavior and social co-operation of birds and fish schools and draws heavily from the evolutionary behavior of these organisms. This paper serves to provide a thorough survey of the PSO algorithm with special emphasis on the development, deployment and improvements of its most basic as well as some of the state-of-the-art implementations. Concepts and directions on choosing the inertia weight, constriction factor, cognition and social weights and perspectives on convergence, parallelization, elitism, niching and discrete optimization as well as neighborhood topologies are outlined. Hybridization attempts with other evolutionary and swarm paradigms in selected applications are covered and an up-to-date review is put forward for the interested reader.