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Transactions on Computational Collective Intelligence XVII pp 232–247Cite as

A Subset-Based Ant Colony Optimisation with Tournament Path Selection for High-Dimensional Problems

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Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 8790))

Abstract

The analysis of big data, particularly from the biosciences, provides unique challenges to the methods used to analyse such data. Datasets such as those used in genome-wide association studies can have a very high number of variables/dimensions (e.g. 400,000+) and therefore modifications are required to standard methods to allow them to function correctly.

A variety of methods can be used for such problems, among them ant colony optimisation is a promising method, inspired by the way in which ants find the shortest path in nature. The selection of paths traditionally uses a roulette wheel which works well for problems of smaller dimensionality but breaks down when higher numbers of variables are considered. In this paper, a subset-based tournament selection ACO approach is proposed that is shown to outperform the roulette wheel-based approach for operations research problems of higher dimensionality in terms of the performance of the final solutions and execution time on problems taken from the literature.

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References

  • Blum, C.: ACO applied to group shop scheduling: a case study on intensification and diversification. In: Dorigo, M., Di Caro, G.A., Sampels, M. (eds.) Ant Algorithms 2002. LNCS, vol. 2463, pp. 14–27. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  • Christmas, J., Keedwell, E., Frayling, T., Perry, J.: Ant colony optimisation to identify genetic variant association with type 2 diabetes. Inf. Sci. 181, 1609–1622 (2011)

    Article  Google Scholar 

  • Dantzig, T.: Numbers: The Language of Science. Macmillan, New York (1930)

    Google Scholar 

  • Dorigo, M., Caro, G.D.: New Ideas in Optimization, pp. 11–32. McGraw-Hill Ltd., Maidenhead (1999)

    Google Scholar 

  • Dorigo, M., Maniezzo, V., Colorni, A.: Positive Feedback as a Search Strategy (1991)

    Google Scholar 

  • Greene, C.S., White, B.C., Moore, J.H.: Ant colony optimization for genome-wide genetic analysis. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds.) ANTS 2008. LNCS, vol. 5217, pp. 37–47. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  • Leguizamon, 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 the Congress on Evolutionary Computation, Mayflower Hotel, Washington, vol. 2, pp. 1459–1464. IEEE Press (1999)

    Google Scholar 

  • Mathews, G.B.: On the partition of numbers. Proc. London Math. Soc. 28, 486–490 (1897)

    MATH  MathSciNet  Google Scholar 

  • Merkle, D., Middendorf, M., Schmeck, H.: Ant colony optimization for resource-constrained project scheduling. IEEE Trans. Evol. Comput. 6, 893–900 (2000)

    Google Scholar 

  • Mohamad, M.M.: Articulated robots motion planning using foraging ant strategy. J. Inf. Technol. 20, 163–181 (2008). Special Issues in Artificial Intelligence

    Google Scholar 

  • Moore, J.H.: A global view of epistasis. Nat. Genet. 37, 13–14 (2005). (Nature Publishing Group)

    Article  Google Scholar 

  • Sapin, E., Keedwell, E.: T-aco - tournament ant colony optimisation for high dimensional problems, pp. 81–86, SciTePress (2012)

    Google Scholar 

  • Sapin, E., Keedwell, E., Frayling, T.: Ant colony optimisation for exploring logical gene-gene associations in genome wide association studies (2013a)

    Google Scholar 

  • Sapin, E., Keedwell, E., Frayling, T.: Subset-based ant colony optimisation for the discovery of gene-gene interactions in genome wide association studies (2013b)

    Google Scholar 

  • Zecchin, A., Maier, H., Simpson, A., Leonard, M., Nixon, J.: Ant colony optimization applied to water distribution system design: comparative study of five algorithms. J. Water Resour. Plan. Manage. 133, 87–92 (2007)

    Article  Google Scholar 

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Acknowledgments

The work contained in this paper was supported by an EPSRC First Grant (EP/J007439/1).

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Authors and Affiliations

  1. College of Engineering, Mathematics and Physical Sciences, University of Exeter, Harrison Building, Exeter, England, UK

    Emmanuel Sapin & Ed Keedwell

Authors
  1. Emmanuel Sapin
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  2. Ed Keedwell
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Corresponding author

Correspondence to Emmanuel Sapin .

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Editors and Affiliations

  1. Institute of Informatics, Wrocław University of Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Swinburne University of Technology, Melbourne, Australia and Polish Academy of Sciences, Warsaw, Poland

    Ryszard Kowalczyk

  3. Technical University of Lisbon, Lisbon, Portugal

    Ana Fred

  4. Polytechnic Institute of Sétubal, Sétubal, Portugal

    Filipe Joaquim

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Sapin, E., Keedwell, E. (2014). A Subset-Based Ant Colony Optimisation with Tournament Path Selection for High-Dimensional Problems. In: Nguyen, N., Kowalczyk, R., Fred, A., Joaquim, F. (eds) Transactions on Computational Collective Intelligence XVII. Lecture Notes in Computer Science(), vol 8790. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44994-3_12

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  • DOI: https://doi.org/10.1007/978-3-662-44994-3_12

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44993-6

  • Online ISBN: 978-3-662-44994-3

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