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Particle Swarm Optimization for Solving Sine-Gordan Equation.

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Title: Particle Swarm Optimization for Solving Sine-Gordan Equation.
Authors: Arora, Geeta1, Chauhan, Pinkey2, Asjad, Muhammad Imran3, Joshi, Varun1, Emadifar, Homan4, Jarad, Fahd5,6,7 fahd@cankaya.edu.tr
Source: Computer Systems Science & Engineering. 2023, Vol. 46 Issue 1, p2647-2658. 12p.
Subjects: Particle swarm optimization, Mathematical functions, Approximation theory, Solitons, Exponential functions
Abstract: The term 'optimization' refers to the process of maximizing the beneficial attributes of a mathematical function or system while minimizing the unfavorable ones. The majority of real-world situations can be modelled as an optimization problem. The complex nature of models restricts traditional optimization techniques to obtain a global optimal solution and paves the path for global optimization methods. Particle Swarm Optimization is a potential global optimization technique that has been widely used to address problems in a variety of fields. The idea of this research is to use exponential basis functions and the particle swarm optimization technique to find a numerical solution for the Sine-Gordan equation, whose numerical solutions show the soliton form and has diverse applications. The implemented optimization technique is employed to determine the involved parameter in the basis functions, which was previously approximated as a random number in the work reported till now in the literature. The obtained results are comparable with the results obtained in the literature. The work is presented in the form of figures and tables and is found encouraging. [ABSTRACT FROM AUTHOR]
Copyright of Computer Systems Science & Engineering is the property of Tech Science Press 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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  Data: Particle Swarm Optimization for Solving Sine-Gordan Equation.
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  Data: <searchLink fieldCode="JN" term="%22Computer+Systems+Science+%26+Engineering%22">Computer Systems Science & Engineering</searchLink>. 2023, Vol. 46 Issue 1, p2647-2658. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+functions%22">Mathematical functions</searchLink><br /><searchLink fieldCode="DE" term="%22Approximation+theory%22">Approximation theory</searchLink><br /><searchLink fieldCode="DE" term="%22Solitons%22">Solitons</searchLink><br /><searchLink fieldCode="DE" term="%22Exponential+functions%22">Exponential functions</searchLink>
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  Data: The term 'optimization' refers to the process of maximizing the beneficial attributes of a mathematical function or system while minimizing the unfavorable ones. The majority of real-world situations can be modelled as an optimization problem. The complex nature of models restricts traditional optimization techniques to obtain a global optimal solution and paves the path for global optimization methods. Particle Swarm Optimization is a potential global optimization technique that has been widely used to address problems in a variety of fields. The idea of this research is to use exponential basis functions and the particle swarm optimization technique to find a numerical solution for the Sine-Gordan equation, whose numerical solutions show the soliton form and has diverse applications. The implemented optimization technique is employed to determine the involved parameter in the basis functions, which was previously approximated as a random number in the work reported till now in the literature. The obtained results are comparable with the results obtained in the literature. The work is presented in the form of figures and tables and is found encouraging. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Computer Systems Science & Engineering is the property of Tech Science Press 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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      – Type: doi
        Value: 10.32604/csse.2023.032404
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      – Code: eng
        Text: English
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        PageCount: 12
        StartPage: 2647
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      – SubjectFull: Particle swarm optimization
        Type: general
      – SubjectFull: Mathematical functions
        Type: general
      – SubjectFull: Approximation theory
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      – SubjectFull: Solitons
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      – SubjectFull: Exponential functions
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              Text: 2023
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