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An improved artificial bee colony algorithm for minimal time cost reduction

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Abstract

The artificial bee colony (ABC) is a popular heuristic optimization algorithm. Although it has fewer control parameters, it shows competitive performance compared with other population-based algorithms. The ABC algorithm is good at exploration, but poor at exploitation. Recently, a global best-guided ABC (GABC) algorithm, inspired by particle swarm optimization, has been developed to tackle this issue. However, GABC cannot be applied to binary optimization problems. In this paper, we develop an improved ABC (IABC) algorithm with a new food source update strategy. IABC employs information about the global best solution as well as personal best solutions, thus enhancing the local search abilities of the bees. The new algorithm is adjusted to solve the binary optimization problem of minimal time cost reduction. We conduct a series of experiments on four UCI datasets, and our results clearly indicate that our algorithm outperforms the existing ABC algorithms, especially on the medium-sized Mushroom dataset.

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References

  1. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132

    Article  MATH  MathSciNet  Google Scholar 

  2. Sundar S, Singh A (2010) A swarm intelligence approach to the quadratic minimum spanning tree problem. Inf Sci 180(17):3182–3191

    Article  MathSciNet  Google Scholar 

  3. Karaboga D, Akay B, Ozturk C (2007) Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks. Model Decisions Artif Intell 4617:318–329

    Article  Google Scholar 

  4. Karaboga N (2009) A new design method based on artificial bee colony algorithm for digital IIR filters. J Franklin Inst 346(4):328–348

    Article  MATH  MathSciNet  Google Scholar 

  5. Linh NT, Anh NQ (2010) Application artificial bee colony algorithm (ABC) for reconfiguring distribution network. In: Second International Conference on Computer modeling and simulation. ICCMS’10, Vol. 1, IEEE. 2010. pp 102–106

  6. Chang WL, Zeng D, Chen RC, Guo S (2013) An artificial bee colony algorithm for data collection path planning in sparse wireless sensor networks. Int J Machine Learn Cybern 4:1–9

    Google Scholar 

  7. Rajasekhar A, Abraham A, Pant M (2011) A hybrid differential artificial bee colony algorithm based tuning of fractional order controller for permanent magnet synchronous motor drive. Int J Mach Learn Cybern 4:1–11

    Google Scholar 

  8. Blum C, Roli A (2003) Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput Surveys (CSUR) 35(3):268–308

    Article  Google Scholar 

  9. Zhu GP, Kwong S (2010) Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math Comput 217(7):3166–3173

    Article  MATH  MathSciNet  Google Scholar 

  10. Rana S, Jasola S, Kumar R (2012) A boundary restricted adaptive particle swarm optimization for data clustering. Int J Mach Learn Cybern 4:1–10

    Google Scholar 

  11. Wang XZ, He YL, Dong LC, Zhao HY (2011) Particle swarm optimization for determining fuzzy measures from data. Inf Sci 181(19):4230–4252

    Article  MATH  Google Scholar 

  12. Kıran MMS (2013) Xor-based artificial bee colony for binary optimization. Turkish J Electrical Eng Comput Sci 21:2307–2328

    Article  Google Scholar 

  13. Kashan MH, Nahavandi N, Kashan AH (2012) Disabc: a new artificial bee colony algorithm for binary optimization. Appl Soft Comput 12(1):342–352

    Article  Google Scholar 

  14. Cai JL, Ding HJ, Zhu W, Zhu XZ (2013) Artificial bee colony algorithm to minimal time cost reduction. J Comput Inf Systems 9 (21):8725–8734

    Google Scholar 

  15. Min F, He HP, Qian YH, Zhu W (2011) Test-cost-sensitive attribute reduction. Inf Sci 181:4928–4942

    Article  Google Scholar 

  16. Zhang S (2010) Cost-sensitive classification with respect to waiting cost. Knowl Based Systems 23(5):369–378

    Article  Google Scholar 

  17. Nguyen S, Pallottino S (1988) Equilibrium traffic assignment for large scale transit networks. Eur J Oper Res 37(2):176–186

    Article  MATH  MathSciNet  Google Scholar 

  18. Cromwell DA (2004) Waiting time information services: an evaluation of how well clearance time statistics can forecast a patient’s wait. Soc Sci Med 59(9):1937–1948

    Article  Google Scholar 

  19. Ji S, Carin L (2007) Cost-sensitive feature acquisition and classification. Pattern Recognit 40(5):1474–1485

    Article  MATH  Google Scholar 

  20. Sheng VS, Ling CX (2006) Feature value acquisition in testing: a sequential batch test algorithm. In: Proceedings of the 23rd International Conference on Machine Learning, ACM 809–816

  21. Min F, Zhu W (2012) Attribute reduction of data with error ranges and test costs. Inf Sci 211:48–67

    Article  MATH  MathSciNet  Google Scholar 

  22. Min F, Zhu W, Zhao H, Pan GY, Liu JB, Xu ZL (2012) Coser: cost-senstive rough sets. http://grc.fjzs.edu.cn/~fmin/coser/

  23. Min F, Liu Q (2009) A hierarchical model for test-cost-sensitive decision systems. Inf Sci 179(14):2442–2452

    Article  MATH  MathSciNet  Google Scholar 

  24. Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11:341–356

    Article  MATH  MathSciNet  Google Scholar 

  25. Zhang W, Mi J, Wu W (2003) Knowledge reductions in inconsistent information systems. Chin J Comput 26(1):12–18

    MathSciNet  Google Scholar 

  26. Jensen R, Shen Q (2004) Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches. IEEE Transact Knowl Data Eng 16(12):1457–1471

    Article  Google Scholar 

  27. Yao YY, Zhao Y (2008) Attribute reduction in decision-theoretic rough set models. Inf Sci 178(17):3356–3373

    Article  MATH  MathSciNet  Google Scholar 

  28. Zhu W (2009) Relationship among basic concepts in covering-based rough sets. Inf Sci 179(14):2478–2486

    Article  MATH  Google Scholar 

  29. Zhu W, Wang F (2003) Reduction and axiomization of covering generalized rough sets. Inf Sci 152(1):217–230

    Article  MATH  Google Scholar 

  30. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Techn. Rep. TR06, Erciyes Univ. Press, Erciyes

  31. Bae C, Yeh WC, Chung YY, Liu SL (2010) Feature selection with intelligent dynamic swarm and rough set. Expert Systems Appl 37(10):7026–7032

    Article  Google Scholar 

  32. Song MP, Gu GC (2004) Research on particle swarm optimization: a review, In: Machine learning and cybernetics. Proceedings of 2004 International Conference on, Vol. 4, IEEE, 2004, 2236–2241

  33. Blake CL, Merz CJ (1998) UCI repository of machine learning databases. http://www.ics.uci.edu/~mlearn/mlrepository.html

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Acknowledgments

This work is in part supported by the National Science Foundation of China under Grant Nos. 61379089, 61379049, 61170128.

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Authors and Affiliations

  1. College of IOT Engineering, Hohai University, Changzhou, 213022, China

    **ling Cai, William Zhu & Haijun Ding

  2. Lab of Granular Computing, Minnan Normal University, Zhangzhou, 363000, China

    William Zhu & Fan Min

  3. Deparment of Computer Science, Southwest Petroleum University, Chengdu, 610500, China

    Fan Min

Authors
  1. **ling Cai
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  2. William Zhu
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  3. Haijun Ding
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  4. Fan Min
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Correspondence to William Zhu.

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Cai, J., Zhu, W., Ding, H. et al. An improved artificial bee colony algorithm for minimal time cost reduction. Int. J. Mach. Learn. & Cyber. 5, 743–752 (2014). https://doi.org/10.1007/s13042-013-0219-8

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  • DOI: https://doi.org/10.1007/s13042-013-0219-8

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