An Innovative Hybrid Approach Producing Trial Solutions for Global Optimization
Global optimization is critical in engineering, computer science, and various industrial applications as it aims to find optimal solutions for complex problems. The development of efficient algorithms has emerged from the need for optimization, with each algorithm offering specific advantages and di...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/22/10567 |
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| author | Vasileios Charilogis Glykeria Kyrou Ioannis G. Tsoulos Anna Maria Gianni |
| author_facet | Vasileios Charilogis Glykeria Kyrou Ioannis G. Tsoulos Anna Maria Gianni |
| author_sort | Vasileios Charilogis |
| collection | DOAJ |
| description | Global optimization is critical in engineering, computer science, and various industrial applications as it aims to find optimal solutions for complex problems. The development of efficient algorithms has emerged from the need for optimization, with each algorithm offering specific advantages and disadvantages. An effective approach to solving complex problems is the hybrid method, which combines established global optimization algorithms. This paper presents a hybrid global optimization method, which produces trial solutions for an objective problem utilizing a genetic algorithm’s genetic operators and solutions obtained through a linear search process. Then, the generated solutions are used to form new test solutions, by applying differential evolution techniques. These operations are based on samples derived either from internal line searches or genetically modified samples in specific subsets of Euclidean space. Additionally, other relevant approaches are explored to enhance the method’s efficiency. The new method was applied on a wide series of benchmark problems from recent studies and comparison was made against other established methods of global optimization. |
| format | Article |
| id | doaj-art-0ab9f8b8c108416aa8baeeca4aebee87 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-0ab9f8b8c108416aa8baeeca4aebee872024-11-26T17:49:17ZengMDPI AGApplied Sciences2076-34172024-11-0114221056710.3390/app142210567An Innovative Hybrid Approach Producing Trial Solutions for Global OptimizationVasileios Charilogis0Glykeria Kyrou1Ioannis G. Tsoulos2Anna Maria Gianni3Department of Informatics and Telecommunications, University of Ioannina, 45110 Ioannina, GreeceDepartment of Informatics and Telecommunications, University of Ioannina, 45110 Ioannina, GreeceDepartment of Informatics and Telecommunications, University of Ioannina, 45110 Ioannina, GreeceDepartment of Informatics and Telecommunications, University of Ioannina, 45110 Ioannina, GreeceGlobal optimization is critical in engineering, computer science, and various industrial applications as it aims to find optimal solutions for complex problems. The development of efficient algorithms has emerged from the need for optimization, with each algorithm offering specific advantages and disadvantages. An effective approach to solving complex problems is the hybrid method, which combines established global optimization algorithms. This paper presents a hybrid global optimization method, which produces trial solutions for an objective problem utilizing a genetic algorithm’s genetic operators and solutions obtained through a linear search process. Then, the generated solutions are used to form new test solutions, by applying differential evolution techniques. These operations are based on samples derived either from internal line searches or genetically modified samples in specific subsets of Euclidean space. Additionally, other relevant approaches are explored to enhance the method’s efficiency. The new method was applied on a wide series of benchmark problems from recent studies and comparison was made against other established methods of global optimization.https://www.mdpi.com/2076-3417/14/22/10567optimizationdifferential evolutiongenetic algorithmline searchevolutionary techniquesstochastic methods |
| spellingShingle | Vasileios Charilogis Glykeria Kyrou Ioannis G. Tsoulos Anna Maria Gianni An Innovative Hybrid Approach Producing Trial Solutions for Global Optimization Applied Sciences optimization differential evolution genetic algorithm line search evolutionary techniques stochastic methods |
| title | An Innovative Hybrid Approach Producing Trial Solutions for Global Optimization |
| title_full | An Innovative Hybrid Approach Producing Trial Solutions for Global Optimization |
| title_fullStr | An Innovative Hybrid Approach Producing Trial Solutions for Global Optimization |
| title_full_unstemmed | An Innovative Hybrid Approach Producing Trial Solutions for Global Optimization |
| title_short | An Innovative Hybrid Approach Producing Trial Solutions for Global Optimization |
| title_sort | innovative hybrid approach producing trial solutions for global optimization |
| topic | optimization differential evolution genetic algorithm line search evolutionary techniques stochastic methods |
| url | https://www.mdpi.com/2076-3417/14/22/10567 |
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