A novel hybrid artificial bee colony algorithm with crossover operator for numerical optimization

详细信息    查看全文

• 作者：Xiaohui Yan (1)
  Yunlong Zhu (2)
  Hanning Chen (2)
  Hao Zhang (2)
  
  1. Department of Mechanical Engineering ; Dongguan University of Technology ; Dongguan ; 523808 ; China
  2. Key Laboratory of Industrial Informatics ; Shenyang Institute of Automation ; Chinese Academy of Sciences ; Shenyang ; 110016 ; China
  
• 关键词：Artificial Bee Colony ; Hybrid artificial bee colony ; Crossover operator ; Crossover rate
• 刊名：Natural Computing
• 出版年：2015
• 出版时间：March 2015
• 年：2015
• 卷：14
• 期：1
• 页码：169-184
• 全文大小：520 KB
• 参考文献：1. Akay B, Karaboga D (2009) Parameter tuning for the artificial bee colony algorithm. In: Proceeding of the first international conference on computational collective intelligence, ICCCI 2009, Wroclaw
  2. Chidambaram C, Lopes HS (2009) A new approach for template matching in digital images using an artificial bee colony algorithm. In: 2009 World congress on nature and biologically inspired computing (NABIC 2009), pp 146鈥?51
  3. Dorigo M, Gambardella LM (1997) Ant Colony System: a cooperating learning approach to the travelling salesman problem. IEEE T Evol Comput 1(1):53鈥?6 CrossRef
  4. Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution. Wiley, New York
  5. Goldberg DE (1989) Genetic algorithms in search, optimisation and machine learning. Addison-Wesley, Reading
  6. Gong M, Jiao L, Liu F, Ma W (2010) Immune algorithm with orthogonal design based initialization, cloning, and selection for global optimization. Knowl Inf Syst 25(3):523鈥?49 CrossRef
  7. Holland JJ (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor
  8. Juang CF (2004) A hybrid genetic algorithm and particle swarm optimization for recurrent network design. IEEE Trans Syst Man Cybern B 34:997鈥?006 CrossRef
  9. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report-TR06. Computer Engineering Department, Engineering Faculty, Erciyes University, Kayseri
  10. Karaboga D, Akay B (2009) A comparative study of Artificial Bee Colony algorithm. Appl Math Comput 214:108鈥?32 CrossRef
  11. Karaboga D, Akay B (2010) A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems. Appl Soft Comput 11(3):3021鈥?031 CrossRef
  12. Karaboga D, Basturk B (2007a) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Global Optim 39(3):47鈥?59 CrossRef
  13. Karaboga D, Basturk B (2007b) Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. LNCS 4529:789鈥?98
  14. Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687鈥?97 CrossRef
  15. Karaboga D, Ozturk C (2011) A novel clustering approach: artificial bee colony (ABC) algorithm. Appl Soft Comput 11(1):652鈥?57 CrossRef
  16. Karaboga D, Akay B, Ozturk C (2007) Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks. LNCS 4617:318鈥?29
  17. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the 1995 IEEE international conference on neural networks, vol 4, pp 1942鈥?948
  18. Li S, Wu X, Tan M (2008) Gene selection using hybrid particle swarm optimization and genetic algorithm. Soft Comput 12(11):1039鈥?048 CrossRef
  19. Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281鈥?95 CrossRef
  20. Ma M, Liang J, Guo M, Fan Y, Yin Y (2011) SAR image segmentation based on artificial bee colony algorithm. Appl Soft Comput 11(8):5205鈥?214 CrossRef
  21. Mallipeddi R, Suganthan PN, Pan QK, Tasgetiren MF (2011) Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl Soft Comput 11(2):1679鈥?696 CrossRef
  22. Mandal SK, Chan FTS, Tiwari MK (2012) Leak detection of pipeline: an integrated approach of rough set theory and artificial bee colony trained SVM. Expert Syst Appl 39(3):3071鈥?080 CrossRef
  23. Ozkan C, Kisi O, Akay B (2011) Neural networks with artificial bee colony algorithm for modeling daily reference evapotranspiration. Irrigation Sci 29:431鈥?41 CrossRef
  24. Passino KM (2002) Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst Mag 22:52鈥?7 CrossRef
  25. Rao RS (2010) Capacitor placement in radial distribution system for loss reduction using artificial bee colony algorithm. Int J Eng Nat Sci 4(2):84鈥?8
  26. Ravi V, Duraiswamy K (2011) A novel power system stabilization using artificial bee colony optimization. Eur J Sci Res 62(4):506鈥?17
  27. Rechenberg I (1973) Evolutionsstrategie: Optimierung technischer Systeme und Prinzipien der biologischen Evolution. Frommann-Holzboog, Stuttgart
  28. Salomon R (1996) Reevaluating genetic algorithm performance under coordinate rotation of benchmark functions. Biosystems 39:263鈥?78 CrossRef
  29. Sonmez M (2011) Discrete optimum design of truss structures using artificial bee colony algorithm. Struct Multidiscip Optim 43(1):85鈥?7 CrossRef
  30. Storn R, Price K (1997) Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341鈥?59 CrossRef
  31. Syswerda G (1989) Uniform crossover in genetic algorithms. In: Proceedings of the third international conference on genetic algorithms
  32. Tasgetiren MF, Pan QK, Suganthan PN, Chen AHL (2011) A discrete artificial bee colony algorithm for the total flowtime minimization in permutation flow shops. Inf Sci 181:3459鈥?475 CrossRef
  33. Yalcinoz T, Altun H, Uzam M (2001) Economic dispatch solution using a genetic algorithm based on arithmetic crossover. In: IEEE Porto PowerTech鈥?2001, Porto, pp 10鈥?3
  34. Yan X, Zhu Y, Zou W, Wang L (2012) A new approach for data clustering using hybrid artificial bee colony algorithm. Neurocomputing. doi:10.1016/j.neucom.2012.04.025
  35. Zhao H, Pei Z, Jiang J, Guan R, Wang C, Shi X (2010) A hybrid swarm intelligent method based on genetic algorithm and artificial bee colony. LNCS 6145:558鈥?65
  36. Zhao SZ, Suganthan PN, Pan QK, Tasgetiren MF (2011) Dynamic multi-swarm particle swarm optimizer with harmony search. Expert Syst Appl 38(4):3735鈥?742 CrossRef
  37. Ziarati K, Akbari R, Zeighami V (2011) On the performance of bee algorithms for resource-constrained project scheduling problem. Appl Soft Comput 11(4):3720鈥?733 CrossRef
  38. Zou W, Zhu Y, Chen H, Sui X (2010) A clustering approach using cooperative artificial bee colony algorithm. DDNS 2010:16
  
• 刊物类别：Computer Science
• 刊物主题：Theory of Computation
  Evolutionary Biology
  Processor Architectures
  Artificial Intelligence and Robotics
  Complexity
  
• 出版者：Springer Netherlands
• ISSN：1572-9796

文摘

Artificial Bee Colony (ABC) algorithm is one of the most recently introduced swarm intelligence algorithms which inspired by the foraging behavior of honey bee swarms. It has been widely used in numerical and engineering optimization problems. This paper presents a hybrid artificial bee colony (HABC) model to improve the canonical ABC algorithm. The main idea of HABC is to enhance the information exchange between bees by introducing the crossover operator of genetic algorithm to ABC. With suitable crossover operation, valuable information is fully utilized and it is expected that the algorithm can converge faster and more accurate. Eight versions of HABC algorithm combined by different selection and crossover methods under the model were proposed and tested on several benchmark functions. Then, the settings of the new parameter crossover rate for two well performed HABC versions are tested to verify their best values. Finally, four rotated functions and four shifted functions are used to test the performance of the two algorithms on complex functions and asymmetric functions. Experiment results showed that these two versions of HABC algorithm offer significant improvement over the original ABC and are superior to other two state of the art algorithms on some functions.