Improve Particle Swarm Optimization and Differential Evolution Algorithms Using Nash Bargaining Theory
<p><span style="font-size: 10.0pt; line-height: 125%; font-family: 'Georgia',serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Georgia; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman';...
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Islamic Azad University Bushehr Branch
2025-04-01
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| Series: | مهندسی مخابرات جنوب |
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| Online Access: | https://sanad.iau.ir/journal/jce/Article/1115549 |
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| author | Marziye Dadvar Hamidreza Navidi Hamid Haj Seyyed Javadi Mitra Mirzarezaee |
| author_facet | Marziye Dadvar Hamidreza Navidi Hamid Haj Seyyed Javadi Mitra Mirzarezaee |
| author_sort | Marziye Dadvar |
| collection | DOAJ |
| description | <p><span style="font-size: 10.0pt; line-height: 125%; font-family: 'Georgia',serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Georgia; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: JA; mso-bidi-language: AR-SA;"> </span><span style="font-size: 11pt; line-height: 125%; font-family: Calibri, sans-serif;">This article proposes a new approach in solving optimization (issues) problems in which two known optimization algorithm of particle swarm algorithm (PSO) and differential evolution (DE) a cooperate. The proposed approach uses a coalition or cooperation model in the game theory to improve the DE and PSO algorithms. This is done in an attempt to keep a balance between the exploration and exploitation capabilities by preventing population stagnation and avoiding the local optimum. The DE and PSO algorithms are two players in the state space, which play cooperative games together using the Nash bargaining theory to find the best solution. To evaluate the performance of the proposed algorithm, 25 benchmark functions are used in terms of the CEC2005 structure. The proposed algorithm is then compared with the classical DE and PSO algorithms and the hybrid algorithms recently proposed. The results indicated that the proposed hybrid algorithm outperformed the classical algorithms and other hybrid models.</span></p> |
| format | Article |
| id | doaj-art-919f1955dc694f86a3ffaf980d4d216c |
| institution | DOAJ |
| issn | 2980-9231 |
| language | fas |
| publishDate | 2025-04-01 |
| publisher | Islamic Azad University Bushehr Branch |
| record_format | Article |
| series | مهندسی مخابرات جنوب |
| spelling | doaj-art-919f1955dc694f86a3ffaf980d4d216c2025-08-20T03:04:59ZfasIslamic Azad University Bushehr Branchمهندسی مخابرات جنوب2980-92312025-04-0114557093Improve Particle Swarm Optimization and Differential Evolution Algorithms Using Nash Bargaining TheoryMarziye Dadvar0Hamidreza Navidi1Hamid Haj Seyyed Javadi2Mitra Mirzarezaee3Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, IranDepartment of Mathematics and Computer Sciences, Shahed University, Tehran, IranDepartment of Mathematics and Computer Science, Shahed University, Tehran, IranDepartment of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran<p><span style="font-size: 10.0pt; line-height: 125%; font-family: 'Georgia',serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Georgia; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-ansi-language: EN-US; mso-fareast-language: JA; mso-bidi-language: AR-SA;"> </span><span style="font-size: 11pt; line-height: 125%; font-family: Calibri, sans-serif;">This article proposes a new approach in solving optimization (issues) problems in which two known optimization algorithm of particle swarm algorithm (PSO) and differential evolution (DE) a cooperate. The proposed approach uses a coalition or cooperation model in the game theory to improve the DE and PSO algorithms. This is done in an attempt to keep a balance between the exploration and exploitation capabilities by preventing population stagnation and avoiding the local optimum. The DE and PSO algorithms are two players in the state space, which play cooperative games together using the Nash bargaining theory to find the best solution. To evaluate the performance of the proposed algorithm, 25 benchmark functions are used in terms of the CEC2005 structure. The proposed algorithm is then compared with the classical DE and PSO algorithms and the hybrid algorithms recently proposed. The results indicated that the proposed hybrid algorithm outperformed the classical algorithms and other hybrid models.</span></p>https://sanad.iau.ir/journal/jce/Article/1115549cooperative game theory nash bargaining theory differential evolution particle swarm optimization. |
| spellingShingle | Marziye Dadvar Hamidreza Navidi Hamid Haj Seyyed Javadi Mitra Mirzarezaee Improve Particle Swarm Optimization and Differential Evolution Algorithms Using Nash Bargaining Theory مهندسی مخابرات جنوب cooperative game theory nash bargaining theory differential evolution particle swarm optimization. |
| title | Improve Particle Swarm Optimization and Differential Evolution Algorithms Using Nash Bargaining Theory |
| title_full | Improve Particle Swarm Optimization and Differential Evolution Algorithms Using Nash Bargaining Theory |
| title_fullStr | Improve Particle Swarm Optimization and Differential Evolution Algorithms Using Nash Bargaining Theory |
| title_full_unstemmed | Improve Particle Swarm Optimization and Differential Evolution Algorithms Using Nash Bargaining Theory |
| title_short | Improve Particle Swarm Optimization and Differential Evolution Algorithms Using Nash Bargaining Theory |
| title_sort | improve particle swarm optimization and differential evolution algorithms using nash bargaining theory |
| topic | cooperative game theory nash bargaining theory differential evolution particle swarm optimization. |
| url | https://sanad.iau.ir/journal/jce/Article/1115549 |
| work_keys_str_mv | AT marziyedadvar improveparticleswarmoptimizationanddifferentialevolutionalgorithmsusingnashbargainingtheory AT hamidrezanavidi improveparticleswarmoptimizationanddifferentialevolutionalgorithmsusingnashbargainingtheory AT hamidhajseyyedjavadi improveparticleswarmoptimizationanddifferentialevolutionalgorithmsusingnashbargainingtheory AT mitramirzarezaee improveparticleswarmoptimizationanddifferentialevolutionalgorithmsusingnashbargainingtheory |