Article
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A general framework of particle swarm optimization
Version 1
: Received: 25 January 2021 / Approved: 26 January 2021 / Online: 26 January 2021 (08:57:39 CET)
How to cite: Nguyen, L. A general framework of particle swarm optimization. Preprints 2021, 2021010528. https://doi.org/10.20944/preprints202101.0528.v1 Nguyen, L. A general framework of particle swarm optimization. Preprints 2021, 2021010528. https://doi.org/10.20944/preprints202101.0528.v1
Abstract
Particle swarm optimization (PSO) is an effective algorithm to solve the optimization problem in case that derivative of target function is inexistent or difficult to be determined. Because PSO has many parameters and variants, I propose a general framework of PSO called GPSO which aggregates important parameters and generalizes important variants so that researchers can customize PSO easily. Moreover, two main properties of PSO are exploration and exploitation. The exploration property aims to avoid premature converging so as to reach global optimal solution whereas the exploitation property aims to motivate PSO to converge as fast as possible. These two aspects are equally important. Therefore, GPSO also aims to balance the exploration and the exploitation. It is expected that GPSO supports users to tune parameters for not only solving premature problem but also fast convergence.
Keywords
global optimization, particle swarm optimization (PSO), exploration, exploitation
Subject
Computer Science and Mathematics, Algebra and Number Theory
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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