Automatic Design of Metaheuristics for Practical Engineering Applications
It is common to find multiple metaheuristics to solve continuous optimization problems. However, choosing what optimizer may obtain the best results for a given task requires exhaustive evaluations that are highly application-dependent. Besides, it is necessary to find sufficiently good tuning param...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10016719/ |
| _version_ | 1797902308171841536 |
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| author | Daniel F. Zambrano-Gutierrez Jorge Mario Cruz-Duarte Juan Gabriel Avina-Cervantes Jose Carlos Ortiz-Bayliss Jesus Joaquin Yanez-Borjas Ivan Amaya |
| author_facet | Daniel F. Zambrano-Gutierrez Jorge Mario Cruz-Duarte Juan Gabriel Avina-Cervantes Jose Carlos Ortiz-Bayliss Jesus Joaquin Yanez-Borjas Ivan Amaya |
| author_sort | Daniel F. Zambrano-Gutierrez |
| collection | DOAJ |
| description | It is common to find multiple metaheuristics to solve continuous optimization problems. However, choosing what optimizer may obtain the best results for a given task requires exhaustive evaluations that are highly application-dependent. Besides, it is necessary to find sufficiently good tuning parameters to achieve satisfactory performance with the selected approach. In this context, the automatic design of algorithms, particularly those based on heuristics, has been increasing in popularity in the previous years due to its undoubted relevance nowadays. This paper explores a novel approach based on hyper-heuristics to carefully select population-based search operators and their tuning parameters to generate metaheuristics capable of dealing with a given practical engineering problem. The proposed strategy is assessed using three highly relevant and illustrative problems: training Artificial Neural Networks, designing PID controllers, and modeling a calorimetric phenomenon based on fractional calculus. In addition, we implement three well-known optimization metaheuristics to compare achieved solutions via the proposed hyper-heuristic strategy, namely Particle Swarm Optimization, Genetic Algorithm, and Cuckoo Search. Results from extensive numerical tests prove that the customized metaheuristics are generally superior to the three well-known algorithms, taking only a few iterations to converge to an optimal solution. This is an excellent indicator of alleviating the effort and expertise required to choose the proper methodology when dealing with real-valued optimization problems. |
| first_indexed | 2024-04-10T09:15:36Z |
| format | Article |
| id | doaj.art-a6de439d2e23406886c939ff4e7837d6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T09:15:36Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a6de439d2e23406886c939ff4e7837d62023-02-21T00:00:59ZengIEEEIEEE Access2169-35362023-01-01117262727610.1109/ACCESS.2023.323683610016719Automatic Design of Metaheuristics for Practical Engineering ApplicationsDaniel F. Zambrano-Gutierrez0https://orcid.org/0000-0002-8632-4342Jorge Mario Cruz-Duarte1https://orcid.org/0000-0003-4494-7864Juan Gabriel Avina-Cervantes2https://orcid.org/0000-0003-1730-3748Jose Carlos Ortiz-Bayliss3https://orcid.org/0000-0003-3408-2166Jesus Joaquin Yanez-Borjas4https://orcid.org/0000-0001-7057-3806Ivan Amaya5https://orcid.org/0000-0002-8821-7137Advanced Artificial Intelligence Research Group, School of Engineering and Science, Tecnologico de Monterrey, Monterrey, Nuevo León, MexicoAdvanced Artificial Intelligence Research Group, School of Engineering and Science, Tecnologico de Monterrey, Monterrey, Nuevo León, MexicoDepartment of Electronics Engineering, Telematics Research Group, University of Guanajuato, Comunidad de Palo Blanco, Salamanca, Guanajuato, MexicoAdvanced Artificial Intelligence Research Group, School of Engineering and Science, Tecnologico de Monterrey, Monterrey, Nuevo León, MexicoAdvanced Artificial Intelligence Research Group, School of Engineering and Science, Tecnologico de Monterrey, Monterrey, Nuevo León, MexicoAdvanced Artificial Intelligence Research Group, School of Engineering and Science, Tecnologico de Monterrey, Monterrey, Nuevo León, MexicoIt is common to find multiple metaheuristics to solve continuous optimization problems. However, choosing what optimizer may obtain the best results for a given task requires exhaustive evaluations that are highly application-dependent. Besides, it is necessary to find sufficiently good tuning parameters to achieve satisfactory performance with the selected approach. In this context, the automatic design of algorithms, particularly those based on heuristics, has been increasing in popularity in the previous years due to its undoubted relevance nowadays. This paper explores a novel approach based on hyper-heuristics to carefully select population-based search operators and their tuning parameters to generate metaheuristics capable of dealing with a given practical engineering problem. The proposed strategy is assessed using three highly relevant and illustrative problems: training Artificial Neural Networks, designing PID controllers, and modeling a calorimetric phenomenon based on fractional calculus. In addition, we implement three well-known optimization metaheuristics to compare achieved solutions via the proposed hyper-heuristic strategy, namely Particle Swarm Optimization, Genetic Algorithm, and Cuckoo Search. Results from extensive numerical tests prove that the customized metaheuristics are generally superior to the three well-known algorithms, taking only a few iterations to converge to an optimal solution. This is an excellent indicator of alleviating the effort and expertise required to choose the proper methodology when dealing with real-valued optimization problems.https://ieeexplore.ieee.org/document/10016719/Metaheuristicshyper-heuristicsPID controllersartificial neural networksfractional model designcontrol theory |
| spellingShingle | Daniel F. Zambrano-Gutierrez Jorge Mario Cruz-Duarte Juan Gabriel Avina-Cervantes Jose Carlos Ortiz-Bayliss Jesus Joaquin Yanez-Borjas Ivan Amaya Automatic Design of Metaheuristics for Practical Engineering Applications IEEE Access Metaheuristics hyper-heuristics PID controllers artificial neural networks fractional model design control theory |
| title | Automatic Design of Metaheuristics for Practical Engineering Applications |
| title_full | Automatic Design of Metaheuristics for Practical Engineering Applications |
| title_fullStr | Automatic Design of Metaheuristics for Practical Engineering Applications |
| title_full_unstemmed | Automatic Design of Metaheuristics for Practical Engineering Applications |
| title_short | Automatic Design of Metaheuristics for Practical Engineering Applications |
| title_sort | automatic design of metaheuristics for practical engineering applications |
| topic | Metaheuristics hyper-heuristics PID controllers artificial neural networks fractional model design control theory |
| url | https://ieeexplore.ieee.org/document/10016719/ |
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