An Interleaved Artificial Bee Colony algorithm for dynamic optimisation problems
Dynamic optimisation problems (DOPs) have attracted a lot of research attention in recent years due to their practical applications and complexity. DOPs are more challenging than static optimisation problems because the problem information or data is either revealed or changed during the course of a...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2018-07-01
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| Series: | Connection Science |
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| Online Access: | http://dx.doi.org/10.1080/09540091.2017.1379949 |
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| author | Salwani Abdullah Shams K. Nseef Ayad Turky |
| author_facet | Salwani Abdullah Shams K. Nseef Ayad Turky |
| author_sort | Salwani Abdullah |
| collection | DOAJ |
| description | Dynamic optimisation problems (DOPs) have attracted a lot of research attention in recent years due to their practical applications and complexity. DOPs are more challenging than static optimisation problems because the problem information or data is either revealed or changed during the course of an ongoing optimisation process. This requires an optimisation algorithm that should be able to monitor the movement of the optimal point and the changes in the landscape solutions. In this paper, we proposed an Interleaved Artificial Bee Colony (I-ABC) algorithm for DOPs. Artificial Bee Colony (ABC) is a nature inspired algorithm which has been successfully used in various optimisation problems. The proposed I-ABC algorithm has two populations, called ABC1 and ABC2, which worked in an interleaved manner. While ABC1 focused on exploring the search space though using a probabilistic solution acceptance mechanism, ABC2 worked inside ABC1 and focused on the search around the current best solutions by using a greedy mechanism. The proposed algorithm was tested on the Moving Peak Benchmark. The experimental results indicated that the proposed algorithm achieved better results than the compared methods for 8 out of 11 scenarios. |
| format | Article |
| id | doaj-art-82baa8d8b7514558b52b9ab1429c36c4 |
| institution | OA Journals |
| issn | 0954-0091 1360-0494 |
| language | English |
| publishDate | 2018-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj-art-82baa8d8b7514558b52b9ab1429c36c42025-08-20T02:08:46ZengTaylor & Francis GroupConnection Science0954-00911360-04942018-07-0130327228410.1080/09540091.2017.13799491379949An Interleaved Artificial Bee Colony algorithm for dynamic optimisation problemsSalwani Abdullah0Shams K. Nseef1Ayad Turky2Universiti Kebangsaan MalaysiaUniversiti Kebangsaan MalaysiaRMIT UniversityDynamic optimisation problems (DOPs) have attracted a lot of research attention in recent years due to their practical applications and complexity. DOPs are more challenging than static optimisation problems because the problem information or data is either revealed or changed during the course of an ongoing optimisation process. This requires an optimisation algorithm that should be able to monitor the movement of the optimal point and the changes in the landscape solutions. In this paper, we proposed an Interleaved Artificial Bee Colony (I-ABC) algorithm for DOPs. Artificial Bee Colony (ABC) is a nature inspired algorithm which has been successfully used in various optimisation problems. The proposed I-ABC algorithm has two populations, called ABC1 and ABC2, which worked in an interleaved manner. While ABC1 focused on exploring the search space though using a probabilistic solution acceptance mechanism, ABC2 worked inside ABC1 and focused on the search around the current best solutions by using a greedy mechanism. The proposed algorithm was tested on the Moving Peak Benchmark. The experimental results indicated that the proposed algorithm achieved better results than the compared methods for 8 out of 11 scenarios.http://dx.doi.org/10.1080/09540091.2017.1379949dynamic optimisationinterleaved artificial bee colony algorithmmoving peak benchmark problemprobabilistic acceptance |
| spellingShingle | Salwani Abdullah Shams K. Nseef Ayad Turky An Interleaved Artificial Bee Colony algorithm for dynamic optimisation problems Connection Science dynamic optimisation interleaved artificial bee colony algorithm moving peak benchmark problem probabilistic acceptance |
| title | An Interleaved Artificial Bee Colony algorithm for dynamic optimisation problems |
| title_full | An Interleaved Artificial Bee Colony algorithm for dynamic optimisation problems |
| title_fullStr | An Interleaved Artificial Bee Colony algorithm for dynamic optimisation problems |
| title_full_unstemmed | An Interleaved Artificial Bee Colony algorithm for dynamic optimisation problems |
| title_short | An Interleaved Artificial Bee Colony algorithm for dynamic optimisation problems |
| title_sort | interleaved artificial bee colony algorithm for dynamic optimisation problems |
| topic | dynamic optimisation interleaved artificial bee colony algorithm moving peak benchmark problem probabilistic acceptance |
| url | http://dx.doi.org/10.1080/09540091.2017.1379949 |
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