Next Article in Journal
An Algorithm for Generating Invisible Data Poisoning Using Adversarial Noise That Breaks Image Classification Deep Learning
Previous Article in Journal
A Machine Learning Perspective on Personalized Medicine: An Automized, Comprehensive Knowledge Base with Ontology for Pattern Recognition
Open AccessReview

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

Department of Electrical Engineering and Computer Science, Vanderbilt University, 2201 West End Ave, Nashville, TN 37235, USA
*
Author to whom correspondence should be addressed.
Mach. Learn. Knowl. Extr. 2019, 1(1), 157-191; https://doi.org/10.3390/make1010010
Received: 1 September 2018 / Revised: 3 October 2018 / Accepted: 4 October 2018 / Published: 10 October 2018
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 that 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. View Full-Text
Keywords: Particle Swarm Optimization; swarm intelligence; evolutionary computation; intelligent agents; optimization; hybrid algorithms; heuristic search; approximate algorithms; robotics and autonomous systems; applications of PSO Particle Swarm Optimization; swarm intelligence; evolutionary computation; intelligent agents; optimization; hybrid algorithms; heuristic search; approximate algorithms; robotics and autonomous systems; applications of PSO
MDPI and ACS Style

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. 2019, 1, 157-191.

Show more citation formats Show less citations formats

Article Access Map

1
Back to TopTop