An enhancement to the Bees Algorithm with slope angle computation and Hill Climbing Algorithm and its applications on scheduling and continuous-type optimisation problem
This paper focuses on improvements to the Bees Algorithm (BA) with slope angle computation and Hill Climbing Algorithm (SACHCA) during the local search process. First, the SAC was employed to determine the inclination of the current sites. Second, according to the slope angle, the HCA was utilised t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2015-01-01
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| Series: | Production and Manufacturing Research: An Open Access Journal |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/21693277.2014.976321 |
| _version_ | 1811194600824504320 |
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| author | B. Yuce D.T. Pham M.S. Packianather E. Mastrocinque |
| author_facet | B. Yuce D.T. Pham M.S. Packianather E. Mastrocinque |
| author_sort | B. Yuce |
| collection | DOAJ |
| description | This paper focuses on improvements to the Bees Algorithm (BA) with slope angle computation and Hill Climbing Algorithm (SACHCA) during the local search process. First, the SAC was employed to determine the inclination of the current sites. Second, according to the slope angle, the HCA was utilised to guide the algorithm to converge to the local optima. This enabled the global optimum of the given problem to be found faster and more precisely by focusing on finding the available local optima first before turning the attention on the global optimum. The proposed enhancements to the BA have been tested on continuous-type benchmark functions and compared with other optimisation techniques. The results show that the proposed algorithm performed better than other algorithms on most of the benchmark functions. The enhanced BA performs better than the basic BA, in particular on higher dimensional and complex optimisation problems. Finally, the proposed algorithm has been used to solve the single machine scheduling problem and the results show that the proposed SAC and HCA-BA outperformed the basic BA in almost all the considered instances, in particular when the complexity of the problem increases. |
| first_indexed | 2024-04-12T00:29:07Z |
| format | Article |
| id | doaj.art-3963aa50e7894ca99779c04eab3c9f0b |
| institution | Directory Open Access Journal |
| issn | 2169-3277 |
| language | English |
| last_indexed | 2024-04-12T00:29:07Z |
| publishDate | 2015-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Production and Manufacturing Research: An Open Access Journal |
| spelling | doaj.art-3963aa50e7894ca99779c04eab3c9f0b2022-12-22T03:55:24ZengTaylor & Francis GroupProduction and Manufacturing Research: An Open Access Journal2169-32772015-01-013131910.1080/21693277.2014.976321976321An enhancement to the Bees Algorithm with slope angle computation and Hill Climbing Algorithm and its applications on scheduling and continuous-type optimisation problemB. Yuce0D.T. Pham1M.S. Packianather2E. Mastrocinque3Cardiff UniversityUniversity of BirminghamCardiff UniversityUniversity of LondonThis paper focuses on improvements to the Bees Algorithm (BA) with slope angle computation and Hill Climbing Algorithm (SACHCA) during the local search process. First, the SAC was employed to determine the inclination of the current sites. Second, according to the slope angle, the HCA was utilised to guide the algorithm to converge to the local optima. This enabled the global optimum of the given problem to be found faster and more precisely by focusing on finding the available local optima first before turning the attention on the global optimum. The proposed enhancements to the BA have been tested on continuous-type benchmark functions and compared with other optimisation techniques. The results show that the proposed algorithm performed better than other algorithms on most of the benchmark functions. The enhanced BA performs better than the basic BA, in particular on higher dimensional and complex optimisation problems. Finally, the proposed algorithm has been used to solve the single machine scheduling problem and the results show that the proposed SAC and HCA-BA outperformed the basic BA in almost all the considered instances, in particular when the complexity of the problem increases.http://dx.doi.org/10.1080/21693277.2014.976321the Bees Algorithmslope angle computationHill Climbing Algorithmbenchmark functionssingle machine scheduling |
| spellingShingle | B. Yuce D.T. Pham M.S. Packianather E. Mastrocinque An enhancement to the Bees Algorithm with slope angle computation and Hill Climbing Algorithm and its applications on scheduling and continuous-type optimisation problem Production and Manufacturing Research: An Open Access Journal the Bees Algorithm slope angle computation Hill Climbing Algorithm benchmark functions single machine scheduling |
| title | An enhancement to the Bees Algorithm with slope angle computation and Hill Climbing Algorithm and its applications on scheduling and continuous-type optimisation problem |
| title_full | An enhancement to the Bees Algorithm with slope angle computation and Hill Climbing Algorithm and its applications on scheduling and continuous-type optimisation problem |
| title_fullStr | An enhancement to the Bees Algorithm with slope angle computation and Hill Climbing Algorithm and its applications on scheduling and continuous-type optimisation problem |
| title_full_unstemmed | An enhancement to the Bees Algorithm with slope angle computation and Hill Climbing Algorithm and its applications on scheduling and continuous-type optimisation problem |
| title_short | An enhancement to the Bees Algorithm with slope angle computation and Hill Climbing Algorithm and its applications on scheduling and continuous-type optimisation problem |
| title_sort | enhancement to the bees algorithm with slope angle computation and hill climbing algorithm and its applications on scheduling and continuous type optimisation problem |
| topic | the Bees Algorithm slope angle computation Hill Climbing Algorithm benchmark functions single machine scheduling |
| url | http://dx.doi.org/10.1080/21693277.2014.976321 |
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