Success History-Based Position Adaptation in Fuzzy-Controlled Ensemble of Biology-Inspired Algorithms
In this study, a new modification of the meta-heuristic approach called Co-Operation of Biology-Related Algorithms (COBRA) is proposed. Originally the COBRA approach was based on a fuzzy logic controller and used for solving real-parameter optimization problems. The basic idea consists of a cooperat...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/13/4/89 |
| _version_ | 1827718926934474752 |
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| author | Shakhnaz Akhmedova Vladimir Stanovov Danil Erokhin Olga Semenkina |
| author_facet | Shakhnaz Akhmedova Vladimir Stanovov Danil Erokhin Olga Semenkina |
| author_sort | Shakhnaz Akhmedova |
| collection | DOAJ |
| description | In this study, a new modification of the meta-heuristic approach called Co-Operation of Biology-Related Algorithms (COBRA) is proposed. Originally the COBRA approach was based on a fuzzy logic controller and used for solving real-parameter optimization problems. The basic idea consists of a cooperative work of six well-known biology-inspired algorithms, referred to as components. However, it was established that the search efficiency of COBRA depends on its ability to keep the exploitation and exploration balance when solving optimization problems. The new modification of the COBRA approach is based on other method for generating potential solutions. This method keeps a historical memory of successful positions found by individuals to lead them in different directions and therefore to improve the exploitation and exploration capabilities. The proposed technique was applied to the COBRA components and to its basic steps. The newly proposed meta-heuristic as well as other modifications of the COBRA approach and components were evaluated on three sets of various benchmark problems. The experimental results obtained by all algorithms with the same computational effort are presented and compared. It was concluded that the proposed modification outperformed other algorithms used in comparison. Therefore, its usefulness and workability were demonstrated. |
| first_indexed | 2024-03-10T20:33:33Z |
| format | Article |
| id | doaj.art-0f08cdf9e1104f929ed3bda411de62e0 |
| institution | Directory Open Access Journal |
| issn | 1999-4893 |
| language | English |
| last_indexed | 2024-03-10T20:33:33Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj.art-0f08cdf9e1104f929ed3bda411de62e02023-11-19T21:09:38ZengMDPI AGAlgorithms1999-48932020-04-011348910.3390/a13040089Success History-Based Position Adaptation in Fuzzy-Controlled Ensemble of Biology-Inspired AlgorithmsShakhnaz Akhmedova0Vladimir Stanovov1Danil Erokhin2Olga Semenkina3Department of Higher Mathematics, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, RussiaDepartment of Higher Mathematics, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, RussiaDepartment of Higher Mathematics, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, RussiaDepartment of Higher Mathematics, Reshetnev Siberian State University of Science and Technology, 660037 Krasnoyarsk, RussiaIn this study, a new modification of the meta-heuristic approach called Co-Operation of Biology-Related Algorithms (COBRA) is proposed. Originally the COBRA approach was based on a fuzzy logic controller and used for solving real-parameter optimization problems. The basic idea consists of a cooperative work of six well-known biology-inspired algorithms, referred to as components. However, it was established that the search efficiency of COBRA depends on its ability to keep the exploitation and exploration balance when solving optimization problems. The new modification of the COBRA approach is based on other method for generating potential solutions. This method keeps a historical memory of successful positions found by individuals to lead them in different directions and therefore to improve the exploitation and exploration capabilities. The proposed technique was applied to the COBRA components and to its basic steps. The newly proposed meta-heuristic as well as other modifications of the COBRA approach and components were evaluated on three sets of various benchmark problems. The experimental results obtained by all algorithms with the same computational effort are presented and compared. It was concluded that the proposed modification outperformed other algorithms used in comparison. Therefore, its usefulness and workability were demonstrated.https://www.mdpi.com/1999-4893/13/4/89optimizationco-operationbiology-inspired algorithmsexternal archiveprobabilistic distribution |
| spellingShingle | Shakhnaz Akhmedova Vladimir Stanovov Danil Erokhin Olga Semenkina Success History-Based Position Adaptation in Fuzzy-Controlled Ensemble of Biology-Inspired Algorithms Algorithms optimization co-operation biology-inspired algorithms external archive probabilistic distribution |
| title | Success History-Based Position Adaptation in Fuzzy-Controlled Ensemble of Biology-Inspired Algorithms |
| title_full | Success History-Based Position Adaptation in Fuzzy-Controlled Ensemble of Biology-Inspired Algorithms |
| title_fullStr | Success History-Based Position Adaptation in Fuzzy-Controlled Ensemble of Biology-Inspired Algorithms |
| title_full_unstemmed | Success History-Based Position Adaptation in Fuzzy-Controlled Ensemble of Biology-Inspired Algorithms |
| title_short | Success History-Based Position Adaptation in Fuzzy-Controlled Ensemble of Biology-Inspired Algorithms |
| title_sort | success history based position adaptation in fuzzy controlled ensemble of biology inspired algorithms |
| topic | optimization co-operation biology-inspired algorithms external archive probabilistic distribution |
| url | https://www.mdpi.com/1999-4893/13/4/89 |
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