Binary Ant Colony Optimization for Subset Problems

  • Nadia Abd-AlsabourEmail author
Part of the Studies in Computational Intelligence book series (SCI, volume 592)


Many optimization problems involve selecting the best subset of solution components. Besides, many other optimization problems can be modelled as a subset problem. This chapter focuses on developing a new framework in ant colony optimization (ACO) for optimization problems that require selection rather than ordering with an application to feature selection for regression problems as a representative for subset problems. This is addressed through three steps that are: explaining the main guidelines of developing an ant algorithm, demonstrating different solution representations for subset problems using ACO algorithms, and proposing a binary ant algorithm for feature selection for regression problems.


Ant colony optimization Binary ant colony optimization (BACO) Subset problems Feature selection 


  1. 1.
    Montgomery, E.J.: Solution biases and pheromone representation selection in ant colony optimization. Ph.D Thesis, Bond University, Australia (2005)Google Scholar
  2. 2.
    Solnon, C., Bridge, D.: An ant colony optimization meta-heuristic for subset selection problems. In: Nedjah, N., Mourelle, L.M. (eds.) Systems Engineering Using Particle Swarm Optimization, pp. 3–25. Nova Science Publishers, New York (2006)Google Scholar
  3. 3.
    Mirzayans, T., Parimi, N., Pilarski, P., Backhouse, C., Wyard-Scott, L., Musilek, P.: A swarm-based system for object recognition. Neural Netw. World 15, 243–255 (2005)Google Scholar
  4. 4.
    Piatrik, T., Chandramouli, K., Izquierdo, E.: Image classification using biologically inspired systems. In: Proceedings of the 2nd International Mobile Multimedia Communications Conference MobiMedia’06, pp. 18–20 (2006)Google Scholar
  5. 5.
    Dorigo, M., Bonabeou, E., Theraulaz, G.: Inspiration for optimization from social insect behavior. Nature 406, 39–42 (2000)CrossRefGoogle Scholar
  6. 6.
    Dorigo, M., Stutzle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)CrossRefzbMATHGoogle Scholar
  7. 7.
    Maniezzo, V., Milandri, M.: An ant-based framework for very strongly constrained problems. In: Dorigo, M., et al. (eds.) Ants Algorithms. LNCS, pp. 222–227. Springer, Berlin (2002)CrossRefGoogle Scholar
  8. 8.
    Cordon, O., Herrera, F., Stutzle, T.: A review on the ant colony optimization metaheuristic: basis, models and new trends. Mathw. Soft Comput. 9(3), 141–175 (2002)zbMATHMathSciNetGoogle Scholar
  9. 9.
    Galea, M.: Applying swarm intelligence to rule induction. M.Sc. Thesis, Division of Informatics, University of Edinburgh (2002)Google Scholar
  10. 10.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)zbMATHGoogle Scholar
  11. 11.
    Liu, H., Motoda, H.: Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic Publishers, Boston (1998)CrossRefzbMATHGoogle Scholar
  12. 12.
    Klosgen, W., Zytkow, J.M.: Handbook of Data Mining and Knowledge Discovery. Oxford University Press, New York (2002)Google Scholar
  13. 13.
    Cios, K.J., Pedrycz, W., Wiswiniarski, R.: Data Mining Methods for Knowledge Discovery. Kluwer Academic Publishers, Boston (1998)CrossRefzbMATHGoogle Scholar
  14. 14.
    Weiss, S., Indurkhya, N.: Predictive Data Mining: A Practical Guide. Morgan Kaufmann Publishers, San Francisco (1998)zbMATHGoogle Scholar
  15. 15.
    Rokach, L., Maimon, O.: Data Mining with Decision Trees. World Scientific Publishing, Singapore (2008)zbMATHGoogle Scholar
  16. 16.
    Gong, S., McKenna, S., Psarrou, A.: Dynamic Vision: From Images to Face Recognition. Imperial College Press, London (1999)Google Scholar
  17. 17.
    Jahne, B., Haussecker, H., Geissler, P.: Handbook of Computer Vision and Applications. Academic Press, San Diego (1999)Google Scholar
  18. 18.
    Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision. Chapman & Hall Computing, New York (1993)CrossRefGoogle Scholar
  19. 19.
    Bradski, G., Kaehler, A.: Learning OpenCV. O’Reilly, California (2008)Google Scholar
  20. 20.
    Jain, A., Flynn, P., Ross, A.: Handbook of Biometrics. Springer, New York (2008)CrossRefGoogle Scholar
  21. 21.
    Whelan, P., Molloy, D.: Machine Vision Algorithms in Java: Techniques and Implementation. Springer, New York (2001)CrossRefGoogle Scholar
  22. 22.
    Le, D., Satoh, S.: An efficient feature selection method for object detection. Pattern Recognition and Data Mining. LNCS, pp. 461–468. Springer, Berlin (2005)CrossRefGoogle Scholar
  23. 23.
    Serre, T., Heisele, B., Mukherjee, S., Poggio, T.: Feature Selection for Face Detection. Massachusetts Institute of Technology, Cambridge (2000)Google Scholar
  24. 24.
    Lesk, A.: Introduction to Bioinformatics. Oxford University Press, New York (2002)Google Scholar
  25. 25.
    Silva, P., Hashimoto, R., Kim, S., Barrera, J.: Feature selection algorithms to find strong genes. Pattern Recognit. Lett. 26, 1444–1453 (2005)CrossRefGoogle Scholar
  26. 26.
    Baxevanis, A., Quellette, B.F.F.: Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins. Wiley, New York (2001)CrossRefGoogle Scholar
  27. 27.
    Riboni, D.: Feature selection for web page classification. In: Proceedings of the Workshop EURASIA-ICT 2002, pp. 473–477 (2002)Google Scholar
  28. 28.
    Sun, A., Lim, E., Ng, W.: Web classification using support vector machine. In: Proceedings of the WIDM’02, November, McLean, Virginia (2002)Google Scholar
  29. 29.
    Yang, Y., Pedersen, J.: A comparative study on feature selection in text categorization. In: Proceedings of the 14th International Conference on Machine Learning (ICML 1997), pp. 412–420 (1997)Google Scholar
  30. 30.
    Chen, J., Huang, H., Tian, S., Qua, Y.: Feature selection for text classification with naive bayes. Expert Syst. Appl. 36, 5432–5435 (2009)CrossRefGoogle Scholar
  31. 31.
    Erta, F.: Feature selection and classification techniques for speaker recognition. J. Eng. Sci. 7(1), 47–54 (2001)Google Scholar
  32. 32.
    Ye, N.: The Handbook of Data Mining. Lawrence Erlbaum Associates, Mahwah (2003)Google Scholar
  33. 33.
    Abd-Alsabour, N., Randall, M., Lewis, A.: Investigating the effect of fixing the subset length using ant colony optimization algorithms for feature subset selection problems. In: Proceedings of the PDCAT, China (2012)Google Scholar
  34. 34.
    Han, J., Kamber, M.: Data Mining Concepts and Techniques, 1st edn. Morgan Kaufmann Publishers, San Francisco (2001)Google Scholar
  35. 35.
    Skillicorn, D.: Understanding Datasets: Datamining with Matrix Decomposition. Chapman & Hall/CRC, London (2007)CrossRefGoogle Scholar
  36. 36.
    Hand, D., Mannila, H., Smyth, P.: Principles of Data Mining. Massachusetts Institute of Technology, Cambridge (2001)Google Scholar
  37. 37.
    Berry, M., Linoff, G.: Data Mining Techniques for Marketing, Sales, and Customer Relationship Management, 2nd edn. Wiley, New York (2004)Google Scholar
  38. 38.
    Leguizam’on, G., Michalewicz, Z.: A new version of ant system for subset problems. In: Angeline, P.J., Michalewicz, Z., Schoenauer, M., Yao, X., Zalzala, A. (eds.) Proceedings of Congress on Evolutionary Computation (CEC99), Washington DC, July 6–9. IEEE Press, (1999)Google Scholar
  39. 39.
    Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization- artificial ants as a computational intelligence technique. IEEE Comput. Intell. Mag. 28–39 (2006)Google Scholar
  40. 40.
    Lee, K., Joo, J., Yang, J., Honavar, V.: Experimental comparison of feature subset selection using GA and ACO algorithm. In: Li, X., Zaiane, O.R., Li, Z. (eds.) ADMA 2006. LNAI, pp. 465–472. Springer, Berlin (2006)Google Scholar
  41. 41.
    Bello, R., Nowe, A., Caballero, Y., Gomez, Y., Vrancx, P.: A model based on ant colony system and rough set theory to feature selection. In: Proceedings of the GECCO’05. Washington (2005)Google Scholar
  42. 42.
    Jensen, R., Shen, Q.: Finding rough set reducts with ant colony optimization. In: Proceedings of the Workshop on Computational Intelligence, UK. 15–22 (2003)Google Scholar
  43. 43.
    Aghdam, M., Tanha, J., Naghsh-Nilchi, A., Basiri, M.: Combination of ant colony optimization and Bayesian classification for feature selection in a bioinformatics dataset. J. Comput. Sci. Syst. Biol. 2(3), 186–199 (2009)CrossRefGoogle Scholar
  44. 44.
    Trafalis, T.B., Ince, H.: Support vector machine for regression and applications to financial forecasting. In: Proceedings of the IEEE-INNS-ENNS International Joint Conference, vol. 6, pp. 348–353. IEEE Press (2000)Google Scholar
  45. 45.
    Durbha, S.S., King, R.L., Younan, N.H.: Support vector machines regression for retrieval of leaf area index from multi-angle imaging spectroradiometer. Remote Sens. Environ. 107, 348–361 (2007)CrossRefGoogle Scholar
  46. 46.
    Lui, B.: Web Data Mining. Springer, Berlin (2010)Google Scholar
  47. 47.
    Hsu, C.W., Chang, C.C., Lin, C.J.: A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University, Taipei, Available at, (2003), viewed 25th March 2013
  48. 48.
    Wang, L., Fu, X.: Data Mining with Computational Intelligence. Springer, Berlin (2005)zbMATHGoogle Scholar
  49. 49.
    Marsland, S.: Machine Learning: An Algorithmic Perspective, CRC Press-Taylor & Francis Group, (2009)Google Scholar
  50. 50.
    Miller, T.: Data and Text Mining- A Business Applications Approach. Pearson/ Prentice Hall, Upper Saddle River (2005)Google Scholar
  51. 51.
    Novat, A.: On the role of feature selection in machine learning. Ph.D Thesis, Hebrew University, Israel (2006)Google Scholar
  52. 52., viewed 25th March 2013
  53. 53.
    Amasyali, M., Ersoy, O.: A comparative review of regression ensembles on drug design datasets. Turkish J. Electr. Eng. Comput. Sci. 1–17 (2013)Google Scholar
  54. 54.
    R: A Language and Environment for Statistical Computing 2006 []. R Foundation for Statistical Computing, Vienna, Austria
  55. 55., viewed 25th March 2011
  56. 56.
    Shen, Q., Jiang, J.H., Tao, J.C., Shen, G.L., Yu, R.Q.: Modified ant colony optimization algorithm for variable selection in QSAR modeling: QSAR studies of cyclooxygenase inhibitors. J. Chem. Inf. Model. 45, 1024–1029 (2005)CrossRefGoogle Scholar
  57. 57.
    Izrailev, S., Agrafiotis, D.: Variable selection for QSAR by artificial ant colony systems. SAR QSAR Environ. Res. 13, 417–423 (2002)CrossRefGoogle Scholar
  58. 58.
    Gunturi, S., Narayanan, R., Khandelwal, A.: In silico ADME modeling 2: computational models to predict human serum albumin binding affinity using ant colony systems. Biorgan. Med. Chem. 14, 4118–4129 (2006)CrossRefGoogle Scholar
  59. 59.
    Shamsipur, M., Zare-Shahabadi, V., Hemmateenejad, B., Akhond, M.: Ant colony optimization: a powerful tool for wavelength selection. J. Chemom. 20, 146–157 (2006)CrossRefGoogle Scholar
  60. 60.
    Palanisamy, S., Kanmani, S.: Artificial bee colony approach for optimizing feature selection. Int. J. Comput. Sci. Issues 9(3), 432–438 (2012)Google Scholar
  61. 61.
    Shamsipur, M., Zare-Shahabadi, V., Hemmateenejad, B., Akhond, M.: An efficient variable selection method based on the use of external memory in ant colony optimization: application to QSAR/QSPR. Anal. Chim. Acta 646, 39–46 (2009)CrossRefGoogle Scholar
  62. 62.
    Vieira, S.M., Sousa, J.M.C., Runkler, T.A.: Two cooperative ant colonies for feature selection using fuzzy models. Expert Syst. Appl. 37, 2714–2723 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Cairo University CairoCairoEgypt

Personalised recommendations