Hybridizing artificial bee colony with biogeography-based optimization for constrained mechanical design problems
A novel hybrid algorithm named ABC-BBO, which integrates artificial bee colony (ABC) algorithm with biogeography-based optimization (BBO) algorithm, is proposed to solve constrained mechanical design problems. ABC-BBO combined the exploration of ABC algorithm with the exploitation of BBO algorithm e...
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| Published in | Journal of Central South University Vol. 22; no. 6; pp. 2250 - 2259 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Changsha
Central South University
01.06.2015
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| Subjects | |
| Online Access | Get full text |
| ISSN | 2095-2899 2227-5223 |
| DOI | 10.1007/s11771-015-2749-6 |
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| Abstract | A novel hybrid algorithm named ABC-BBO, which integrates artificial bee colony (ABC) algorithm with biogeography-based optimization (BBO) algorithm, is proposed to solve constrained mechanical design problems. ABC-BBO combined the exploration of ABC algorithm with the exploitation of BBO algorithm effectively, and hence it can generate the promising candidate individuals. The proposed hybrid algorithm speeds up the convergence and improves the algorithm’s performance. Several benchmark test functions and mechanical design problems are applied to verifying the effects of these improvements and it is demonstrated that the performance of this proposed ABC-BBO is superior to or at least highly competitive with other population-based optimization approaches. |
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| AbstractList | A novel hybrid algorithm named ABC-BBO, which integrates artificial bee colony (ABC) algorithm with biogeography-based optimization (BBO) algorithm, is proposed to solve constrained mechanical design problems. ABC-BBO combined the exploration of ABC algorithm with the exploitation of BBO algorithm effectively, and hence it can generate the promising candidate individuals. The proposed hybrid algorithm speeds up the convergence and improves the algorithm’s performance. Several benchmark test functions and mechanical design problems are applied to verifying the effects of these improvements and it is demonstrated that the performance of this proposed ABC-BBO is superior to or at least highly competitive with other population-based optimization approaches. |
| Author | Jiao, Jian-jun Cai, Shao-hong Long, Wen |
| Author_xml | – sequence: 1 givenname: Shao-hong surname: Cai fullname: Cai, Shao-hong email: gzcjdx_csh@163.com organization: Guizhou Key Laboratory of Economics System Simulation, Guizhou University of Finance & Economics – sequence: 2 givenname: Wen surname: Long fullname: Long, Wen organization: Guizhou Key Laboratory of Economics System Simulation, Guizhou University of Finance & Economics – sequence: 3 givenname: Jian-jun surname: Jiao fullname: Jiao, Jian-jun organization: Guizhou Key Laboratory of Economics System Simulation, Guizhou University of Finance & Economics, School of Mathematics and Statistics, Guizhou University of Finance & Economics |
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