Preprint Review Version 1 This version is not peer-reviewed

Particle Swarm Optimization: A survey of Historical and Recent Developments with Hybridization Perspectives

Version 1 : Received: 1 September 2018 / Approved: 2 September 2018 / Online: 2 September 2018 (15:29:55 CEST)

A peer-reviewed article of this Preprint also exists.

Sengupta, S.; Basak, S.; Peters, R.A., II. Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives. Mach. Learn. Knowl. Extr. 2018, 1, 157-191. Sengupta, S.; Basak, S.; Peters, R.A., II. Particle Swarm Optimization: A Survey of Historical and Recent Developments with Hybridization Perspectives. Mach. Learn. Knowl. Extr. 2018, 1, 157-191.

Journal reference: Mach. Learn. Knowl. Extr. 2018, 1, 10
DOI: 10.3390/make1010010

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 very recent 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.

Subject Areas

Particle Swarm Optimization; Swarm Intelligence; Evolutionary Computation; Intelligent Agents; Optimization; Hybrid Algorithms; Heuristic Search; Approximate Algorithms; Robotics and Autonomous Systems; Applications of PSO

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