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Phasor particle swarm optimization: a simple and efficient variant of PSO.

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Title: Phasor particle swarm optimization: a simple and efficient variant of PSO.
Authors: Ghasemi, Mojtaba1 (AUTHOR) mojtaba.ghasemi1365@yahoo.com, Akbari, Ebrahim2 (AUTHOR) ebrahimakbary@gmail.com, Rahimnejad, Abolfazl3 (AUTHOR) arahimne@uoguelph.ca, Razavi, Seyed Ehsan4 (AUTHOR) erazavi@birjand.ac.ir, Ghavidel, Sahand5 (AUTHOR) sahand.ghavideljirsaraie@student.uts.edu.au, Li, Li5 (AUTHOR) li.li@uts.edu.au
Source: Soft Computing - A Fusion of Foundations, Methodologies & Applications. Oct2019, Vol. 23 Issue 19, p9701-9718. 18p.
Subjects: Particle swarm optimization, Global optimization, Source code
Abstract: Particle swarm optimizer is a well-known efficient population and control parameter-based algorithm for global optimization of different problems. This paper focuses on a new and primary sample for PSO, which is named phasor particle swarm optimization (PPSO) and is based on modeling the particle control parameters with a phase angle (θ), inspired from phasor theory in the mathematics. This phase angle (θ) converts PSO algorithm to a self-adaptive, trigonometric, balanced, and nonparametric meta-heuristic algorithm. The performance of PPSO is tested on real-parameter optimization problems including unimodal and multimodal standard test functions and traditional benchmark functions. The optimization results show good and efficient performance of PPSO algorithm in real-parameter global optimization, especially for high-dimensional optimization problems compared with other improved PSO algorithms taken from the literature. The phasor model can be used to expand different types of PSO and other algorithms. The source codes of the PPSO algorithms are publicly available at https://github.com/ebrahimakbary/PPSO. [ABSTRACT FROM AUTHOR]
Copyright of Soft Computing - A Fusion of Foundations, Methodologies & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 138521997
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Phasor particle swarm optimization: a simple and efficient variant of PSO.
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  Data: <searchLink fieldCode="AR" term="%22Ghasemi%2C+Mojtaba%22">Ghasemi, Mojtaba</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mojtaba.ghasemi1365@yahoo.com</i><br /><searchLink fieldCode="AR" term="%22Akbari%2C+Ebrahim%22">Akbari, Ebrahim</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> ebrahimakbary@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Rahimnejad%2C+Abolfazl%22">Rahimnejad, Abolfazl</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> arahimne@uoguelph.ca</i><br /><searchLink fieldCode="AR" term="%22Razavi%2C+Seyed+Ehsan%22">Razavi, Seyed Ehsan</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> erazavi@birjand.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Ghavidel%2C+Sahand%22">Ghavidel, Sahand</searchLink><relatesTo>5</relatesTo> (AUTHOR)<i> sahand.ghavideljirsaraie@student.uts.edu.au</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Li%22">Li, Li</searchLink><relatesTo>5</relatesTo> (AUTHOR)<i> li.li@uts.edu.au</i>
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  Data: <searchLink fieldCode="JN" term="%22Soft+Computing+-+A+Fusion+of+Foundations%2C+Methodologies+%26+Applications%22">Soft Computing - A Fusion of Foundations, Methodologies & Applications</searchLink>. Oct2019, Vol. 23 Issue 19, p9701-9718. 18p.
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  Data: Particle swarm optimizer is a well-known efficient population and control parameter-based algorithm for global optimization of different problems. This paper focuses on a new and primary sample for PSO, which is named phasor particle swarm optimization (PPSO) and is based on modeling the particle control parameters with a phase angle (θ), inspired from phasor theory in the mathematics. This phase angle (θ) converts PSO algorithm to a self-adaptive, trigonometric, balanced, and nonparametric meta-heuristic algorithm. The performance of PPSO is tested on real-parameter optimization problems including unimodal and multimodal standard test functions and traditional benchmark functions. The optimization results show good and efficient performance of PPSO algorithm in real-parameter global optimization, especially for high-dimensional optimization problems compared with other improved PSO algorithms taken from the literature. The phasor model can be used to expand different types of PSO and other algorithms. The source codes of the PPSO algorithms are publicly available at https://github.com/ebrahimakbary/PPSO. [ABSTRACT FROM AUTHOR]
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  Label:
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  Data: <i>Copyright of Soft Computing - A Fusion of Foundations, Methodologies & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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