Global gbest guided-artificial bee colony algorithm for numerical function optimization
Numerous computational algorithms are used to obtain a high performance in solving mathematics, engineering and statistical complexities. Recently, an attractive bio-inspired method—namely the Artificial Bee Colony (ABC)—has shown outstanding performance with some typical computational algorithms in...
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| Language: | en |
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MDPI
2018
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| Online Access: | http://eprints.uthm.edu.my/4531/1/AJ%202018%20%28785%29%20Global%20gbest%20guided-artificial%20bee%20colony%20algorithm%20for%20numerical%20function%20optimization.pdf http://eprints.uthm.edu.my/4531/ |
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| author | Shah, Habib Tairan, Nasser Garg, Harish Ghazali, Rozaida |
| author_facet | Shah, Habib Tairan, Nasser Garg, Harish Ghazali, Rozaida |
| author_sort | Shah, Habib |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Numerous computational algorithms are used to obtain a high performance in solving mathematics, engineering and statistical complexities. Recently, an attractive bio-inspired method—namely the Artificial Bee Colony (ABC)—has shown outstanding performance with some typical computational algorithms in different complex problems. The modification, hybridization and improvement strategies made ABC more attractive to science and engineering researchers. The two well-known honeybees-based upgraded algorithms, Gbest Guided Artificial Bee Colony (GGABC) and Global Artificial Bee Colony Search (GABCS), use the foraging behavior of the global best and guided best honeybees for solving complex optimization tasks. Here, the hybrid of the above GGABC and GABC methods is called the 3G-ABC algorithm for strong discovery and exploitation processes. The proposed and typical methods were implemented on the basis of maximum fitness values instead of maximum cycle numbers, which has provided an extra strength to the proposed and existing methods. The experimental results were tested with sets of fifteen numerical benchmark functions. The obtained results from the proposed approach are compared with the several existing approaches such as ABC, GABC and GGABC, result and found to be very profitable. Finally, obtained results are verified with some statistical testing. |
| format | Article |
| id | my.uthm.eprints-4531 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2018 |
| publisher | MDPI |
| record_format | eprints |
| spelling | my.uthm.eprints-45312021-12-07T06:18:49Z http://eprints.uthm.edu.my/4531/ Global gbest guided-artificial bee colony algorithm for numerical function optimization Shah, Habib Tairan, Nasser Garg, Harish Ghazali, Rozaida QA76 Computer software T58.6-58.62 Management information systems Numerous computational algorithms are used to obtain a high performance in solving mathematics, engineering and statistical complexities. Recently, an attractive bio-inspired method—namely the Artificial Bee Colony (ABC)—has shown outstanding performance with some typical computational algorithms in different complex problems. The modification, hybridization and improvement strategies made ABC more attractive to science and engineering researchers. The two well-known honeybees-based upgraded algorithms, Gbest Guided Artificial Bee Colony (GGABC) and Global Artificial Bee Colony Search (GABCS), use the foraging behavior of the global best and guided best honeybees for solving complex optimization tasks. Here, the hybrid of the above GGABC and GABC methods is called the 3G-ABC algorithm for strong discovery and exploitation processes. The proposed and typical methods were implemented on the basis of maximum fitness values instead of maximum cycle numbers, which has provided an extra strength to the proposed and existing methods. The experimental results were tested with sets of fifteen numerical benchmark functions. The obtained results from the proposed approach are compared with the several existing approaches such as ABC, GABC and GGABC, result and found to be very profitable. Finally, obtained results are verified with some statistical testing. MDPI 2018 Article PeerReviewed text en http://eprints.uthm.edu.my/4531/1/AJ%202018%20%28785%29%20Global%20gbest%20guided-artificial%20bee%20colony%20algorithm%20for%20numerical%20function%20optimization.pdf Shah, Habib and Tairan, Nasser and Garg, Harish and Ghazali, Rozaida (2018) Global gbest guided-artificial bee colony algorithm for numerical function optimization. Computers, 7 (69). pp. 1-17. ISSN 2073-431X |
| spellingShingle | QA76 Computer software T58.6-58.62 Management information systems Shah, Habib Tairan, Nasser Garg, Harish Ghazali, Rozaida Global gbest guided-artificial bee colony algorithm for numerical function optimization |
| title | Global gbest guided-artificial bee colony algorithm for numerical function optimization |
| title_full | Global gbest guided-artificial bee colony algorithm for numerical function optimization |
| title_fullStr | Global gbest guided-artificial bee colony algorithm for numerical function optimization |
| title_full_unstemmed | Global gbest guided-artificial bee colony algorithm for numerical function optimization |
| title_short | Global gbest guided-artificial bee colony algorithm for numerical function optimization |
| title_sort | global gbest guided-artificial bee colony algorithm for numerical function optimization |
| topic | QA76 Computer software T58.6-58.62 Management information systems |
| url | http://eprints.uthm.edu.my/4531/1/AJ%202018%20%28785%29%20Global%20gbest%20guided-artificial%20bee%20colony%20algorithm%20for%20numerical%20function%20optimization.pdf http://eprints.uthm.edu.my/4531/ |
| url_provider | http://eprints.uthm.edu.my/ |