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Hybridizing artificial bee colony with biogeography-based optimization for constrained mechanical design problems

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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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References

  1. CAI Zi-xing, WANG Yong. A multi-objective optimization-based evolutionary algorithm for constrained optimization [J]. IEEE Transactions on Evolutionary Computation, 2006, 10(6): 658–675.

    Article  Google Scholar 

  2. DANESHYARI M, YEN G G. Constrained multiple-swarm particle swarm optimization within a cultural framework [J]. IEEE Transactions on Systems, Man and Cybernetics, 2012, 42(2): 475–490.

    Article  Google Scholar 

  3. LONG Wen, LIANG Xi-ming, HUANG Ya-fei, CHEN Yi-xiong. A hybrid differential evolution augmented Lagrangian method for constrained numerical and engineering optimization [J]. Computer-Aided Design, 2013, 45(12): 1562–1574.

    Article  MathSciNet  Google Scholar 

  4. KARABOGA D, AKAY B. A modified artificial bee colony (ABC) algorithm for constrained optimization problems [J]. Applied Soft Computing, 2011, 11(3): 3021–3031.

    Article  Google Scholar 

  5. BOUSSAID I, CHATTERJEE A, SIARRY P, AHMED-NACER M. Biogeography- based optimization for constrained optimization problems [J]. Computers & Operations Research, 2012, 39(12): 3293–3304.

    Article  MathSciNet  Google Scholar 

  6. BONYADI M R, LI Xiang, MICHALEWICZ Z. A hybrid particle swarm with a time-adaptive topology for constrained optimization [J]. Swarm and Evolutionary Computation, 2014, 18: 22–37.

    Article  Google Scholar 

  7. ELSAYED S M, SARKER R A, MEZURA-MONTES E. Self-adaptive mix of particle swarm methodologies for constrained optimization [J]. Information Sciences, 2014, 277: 216–233.

    Article  MathSciNet  Google Scholar 

  8. LONG Wen, ZHANG Wen-zhuan, HUANG Ya-fei, CHEN Yi-xiang. A hybrid cuckoo search algorithm with feasibility-based rule for constrained structural optimization [J]. Journal of Central South University, 2014, 21(8): 3197–3204.

    Article  Google Scholar 

  9. JIA Guan-bo, WANG Yong, CAI Zi-xing, JIN Yao-chu. An improved (µ+?)-constrained differential evolution for constrained optimization [J]. Information Sciences, 2013, 222: 302–322.

    Article  MATH  MathSciNet  Google Scholar 

  10. KARABOGA D. An idea based on honey bee swarm for numerical optimization [R]. Technical Report-TR06, Kayseri, Turkey: Erciyes University, 2005.

    Google Scholar 

  11. SIMON D. Biogeography-based optimization [J]. IEEE Transactions on Evolutionary Computation, 2008, 12(6): 702–713.

    Article  Google Scholar 

  12. DEB K. An efficient constraint handling method for genetic algorithms [J]. Computer Methods in Applied Mechanics and Engineering, 2000, 186(2/3/4): 311–338.

    Article  MATH  Google Scholar 

  13. RUNARSSON T P, YAO X. Stochastic ranking for constrained evolutionary optimization [J]. IEEE Transactions on Evolutionary Computation, 2000, 4(3): 284–294.

    Article  Google Scholar 

  14. MEZURA-MONTES E, CETINA-DOMINGUEZ O. Empirical analysis of a modified artificial bee colony for constrained numerical optimization [J]. Applied Mathematics and Computation, 2012, 218(22): 10943–10973.

    Article  MATH  MathSciNet  Google Scholar 

  15. GANDOMI A H, YANG X S, TALATAHARI S, DEB S. Couple eagle strategy and differential evolution for unconstrained and constrained global optimization [J]. Computers and Mathematics with Applications, 2012, 63(1): 191–200.

    Article  MATH  MathSciNet  Google Scholar 

  16. LONG Wen, LIANG Xi-ming, HUANG Ya-fei, CHEN Yi-xiong. An effective hybrid cuckoo search algorithm for constrained global optimization [J]. Neural Computation and Applications, 2014, 25(3/4): 911–926.

    Article  Google Scholar 

  17. ROCHA A, FERNANDES E. Feasibility and dominance rules in the electromagnetism-like mechanism for global optimization [J]. Lecture Notes in Computer Science, 2008, 5071: 768–783.

    Google Scholar 

  18. ESKANDAR H, SADOLLAH A, BAHREININEJAD A, HAMDI M. Water cycle algorithm—A novel metaheuristic optimization method for solving constrained engineering optimization problems [J]. Computers and Structures, 2012, 110/111: 151–166.

    Article  Google Scholar 

  19. KANNAN B K, KRAMER S N. An augmented lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design [J]. Journal of Mechanical Design, 1994, 116(2): 405–411.

    Article  Google Scholar 

  20. COELLO C A C. Use of a self-adaptive penalty approach for engineering optimization problems [J]. Computers in Industry, 2000, 41(2): 113–127.

    Article  Google Scholar 

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Authors and Affiliations

  1. Guizhou Key Laboratory of Economics System Simulation, Guizhou University of Finance & Economics, Guiyang, 550004, China

    Shao-hong Cai  (蔡绍洪), Wen Long  (龙文) & Jian-jun Jiao  (焦建军)

  2. School of Mathematics and Statistics, Guizhou University of Finance & Economics, Guiyang, 550004, China

    Jian-jun Jiao  (焦建军)

Authors
  1. Shao-hong Cai  (蔡绍洪)
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  2. Wen Long  (龙文)
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  3. Jian-jun Jiao  (焦建军)
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Corresponding author

Correspondence to Shao-hong Cai  (蔡绍洪).

Additional information

Foundation item: Projects(61463009, 11264005, 11361014) supported by the National Natural Science Foundation of China; Project([2013]2082) supported by the Science Technology Foundation of Guizhou Province, China

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Cai, Sh., Long, W. & Jiao, Jj. Hybridizing artificial bee colony with biogeography-based optimization for constrained mechanical design problems. J. Cent. South Univ. 22, 2250–2259 (2015). https://doi.org/10.1007/s11771-015-2749-6

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  • DOI: https://doi.org/10.1007/s11771-015-2749-6

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