Chaotic Jaya Approaches to Solving Electromagnetic Optimization Benchmark Problems
The Jaya optimization algorithm is a simple, fast, robust, and powerful population-based stochastic metaheuristic that in recent years has been successfully applied in a variety of global optimization problems in various application fields. The essential idea of the Jaya algorithm is that the search...
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MDPI AG
2021-06-01
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| author | Leandro dos S. Coelho Viviana C. Mariani Sotirios K. Goudos Achilles D. Boursianis Konstantinos Kokkinidis Nikolaos V. Kantartzis |
| author_facet | Leandro dos S. Coelho Viviana C. Mariani Sotirios K. Goudos Achilles D. Boursianis Konstantinos Kokkinidis Nikolaos V. Kantartzis |
| author_sort | Leandro dos S. Coelho |
| collection | DOAJ |
| description | The Jaya optimization algorithm is a simple, fast, robust, and powerful population-based stochastic metaheuristic that in recent years has been successfully applied in a variety of global optimization problems in various application fields. The essential idea of the Jaya algorithm is that the searching agents try to change their positions toward the best obtained solution by avoiding the worst solution at every generation. The important difference between Jaya and other metaheuristics is that Jaya does not require the tuning of its control, except for the maximum number of iterations and population size parameters. However, like other metaheuristics, Jaya still has the dilemma of an appropriate tradeoff between its exploration and exploitation abilities during the evolution process. To enhance the convergence performance of the standard Jaya algorithm in the continuous domain, chaotic Jaya (CJ) frameworks based on chaotic sequences are proposed in this paper. In order to obtain the performance of the standard Jaya and CJ approaches, tests related to electromagnetic optimization using two different benchmark problems are conducted. These are the Loney’s solenoid benchmark and a brushless direct current (DC) motor benchmark. Both problems are realized to evaluate the effectiveness and convergence rate. The simulation results and comparisons with the standard Jaya algorithm demonstrated that the performance of the CJ approaches based on Chebyshev-type chaotic mapping and logistic mapping can be competitive results in terms of both efficiency and solution quality in electromagnetics optimization. |
| first_indexed | 2024-03-10T10:45:55Z |
| format | Article |
| id | doaj.art-7c689aa3326947329a9dfe45e29b9081 |
| institution | Directory Open Access Journal |
| issn | 2673-4001 |
| language | English |
| last_indexed | 2024-03-10T10:45:55Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Telecom |
| spelling | doaj.art-7c689aa3326947329a9dfe45e29b90812023-11-21T22:34:51ZengMDPI AGTelecom2673-40012021-06-012222223110.3390/telecom2020015Chaotic Jaya Approaches to Solving Electromagnetic Optimization Benchmark ProblemsLeandro dos S. Coelho0Viviana C. Mariani1Sotirios K. Goudos2Achilles D. Boursianis3Konstantinos Kokkinidis4Nikolaos V. Kantartzis5Department of Electrical Engineering, Federal University of Parana, Curitiba 80210-170, BrazilDepartment of Electrical Engineering, Federal University of Parana, Curitiba 80210-170, BrazilDepartment of Physics, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceDepartment of Physics, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceDepartment of Applied Informatics, University of Macedonia, 54636 Thessaloniki, GreeceDepartment of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceThe Jaya optimization algorithm is a simple, fast, robust, and powerful population-based stochastic metaheuristic that in recent years has been successfully applied in a variety of global optimization problems in various application fields. The essential idea of the Jaya algorithm is that the searching agents try to change their positions toward the best obtained solution by avoiding the worst solution at every generation. The important difference between Jaya and other metaheuristics is that Jaya does not require the tuning of its control, except for the maximum number of iterations and population size parameters. However, like other metaheuristics, Jaya still has the dilemma of an appropriate tradeoff between its exploration and exploitation abilities during the evolution process. To enhance the convergence performance of the standard Jaya algorithm in the continuous domain, chaotic Jaya (CJ) frameworks based on chaotic sequences are proposed in this paper. In order to obtain the performance of the standard Jaya and CJ approaches, tests related to electromagnetic optimization using two different benchmark problems are conducted. These are the Loney’s solenoid benchmark and a brushless direct current (DC) motor benchmark. Both problems are realized to evaluate the effectiveness and convergence rate. The simulation results and comparisons with the standard Jaya algorithm demonstrated that the performance of the CJ approaches based on Chebyshev-type chaotic mapping and logistic mapping can be competitive results in terms of both efficiency and solution quality in electromagnetics optimization.https://www.mdpi.com/2673-4001/2/2/15chaotic mapselectromagnetic optimizationJaya optimization algorithmmetaheuristicsevolutionary computation |
| spellingShingle | Leandro dos S. Coelho Viviana C. Mariani Sotirios K. Goudos Achilles D. Boursianis Konstantinos Kokkinidis Nikolaos V. Kantartzis Chaotic Jaya Approaches to Solving Electromagnetic Optimization Benchmark Problems Telecom chaotic maps electromagnetic optimization Jaya optimization algorithm metaheuristics evolutionary computation |
| title | Chaotic Jaya Approaches to Solving Electromagnetic Optimization Benchmark Problems |
| title_full | Chaotic Jaya Approaches to Solving Electromagnetic Optimization Benchmark Problems |
| title_fullStr | Chaotic Jaya Approaches to Solving Electromagnetic Optimization Benchmark Problems |
| title_full_unstemmed | Chaotic Jaya Approaches to Solving Electromagnetic Optimization Benchmark Problems |
| title_short | Chaotic Jaya Approaches to Solving Electromagnetic Optimization Benchmark Problems |
| title_sort | chaotic jaya approaches to solving electromagnetic optimization benchmark problems |
| topic | chaotic maps electromagnetic optimization Jaya optimization algorithm metaheuristics evolutionary computation |
| url | https://www.mdpi.com/2673-4001/2/2/15 |
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