Opposition-Based Artificial Bee Colony Algorithm
The Artificial Bee Colony (ABC) algorithm is a relatively new algorithm for function optimization. The algorithm is inspired by the foraging behavior of honey bees. In this work, the performance of ABC is enhanced by introducing the concept of generalized opposition-based learning. This concept is i...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| منشور في: |
2011
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| الوصول للمادة أونلاين: | http://hdl.handle.net/11675/921 |
| الوسوم: |
إضافة وسم
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| _version_ | 1870679718998573056 |
|---|---|
| author | El-Abd, Mohammed |
| author_facet | El-Abd, Mohammed |
| author_role | author |
| dc.creator.none.fl_str_mv | El-Abd, Mohammed |
| dc.date.none.fl_str_mv | 2011 2016-04-07T08:38:38Z 2016-04-07T08:38:38Z |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/11675/921 |
| dc.relation.none.fl_str_mv | Genetic and Evolutionary Computation Conference GECCO |
| dc.title.none.fl_str_mv | Opposition-Based Artificial Bee Colony Algorithm |
| dc.type.none.fl_str_mv | Conference Paper info:eu-repo/semantics/publishedVersion |
| description | The Artificial Bee Colony (ABC) algorithm is a relatively new algorithm for function optimization. The algorithm is inspired by the foraging behavior of honey bees. In this work, the performance of ABC is enhanced by introducing the concept of generalized opposition-based learning. This concept is introduced through the initialization step and through generation jumping. The performance of the proposed generalized opposition-based ABC (GOABC) is compared to the performance of ABC and opposition-based ABC (OABC) using the CEC05 benchmarks library. |
| id | AUKR_5485f35698db343fa5807c2dabffff95 |
| network_acronym_str | AUKR |
| network_name_str | AU Kuwait Rep |
| oai_identifier_str | oai:dspace.auk.edu.kw:11675/921 |
| publishDate | 2011 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Opposition-Based Artificial Bee Colony AlgorithmEl-Abd, MohammedThe Artificial Bee Colony (ABC) algorithm is a relatively new algorithm for function optimization. The algorithm is inspired by the foraging behavior of honey bees. In this work, the performance of ABC is enhanced by introducing the concept of generalized opposition-based learning. This concept is introduced through the initialization step and through generation jumping. The performance of the proposed generalized opposition-based ABC (GOABC) is compared to the performance of ABC and opposition-based ABC (OABC) using the CEC05 benchmarks library.2016-04-07T08:38:38Z2016-04-07T08:38:38Z2011Conference Paperinfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/11675/921Genetic and Evolutionary Computation Conference GECCOoai:dspace.auk.edu.kw:11675/9212022-01-13T09:22:02Z |
| spellingShingle | Opposition-Based Artificial Bee Colony Algorithm El-Abd, Mohammed |
| status_str | publishedVersion |
| title | Opposition-Based Artificial Bee Colony Algorithm |
| title_full | Opposition-Based Artificial Bee Colony Algorithm |
| title_fullStr | Opposition-Based Artificial Bee Colony Algorithm |
| title_full_unstemmed | Opposition-Based Artificial Bee Colony Algorithm |
| title_short | Opposition-Based Artificial Bee Colony Algorithm |
| title_sort | Opposition-Based Artificial Bee Colony Algorithm |
| url | http://hdl.handle.net/11675/921 |