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Application of Negative Learning Ant Colony Optimization to the Far from Most String Problem

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Evolutionary Computation in Combinatorial Optimization (EvoCOP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13987))

Abstract

We propose the application of a recently introduced version of ant colony optimization—negative learning ant colony optimization—to the far from most string problem. This problem is a notoriously difficult combinatorial optimization problem from the group of string selection problems. The proposed algorithm makes use of negative learning in addition to the standard positive learning mechanism in order to achieve better guidance for the exploration of the search space. In addition, we compare different versions of our algorithm characterized by the use of different objective functions. The obtained results show that our algorithm is especially successful for instances with specific characteristics. Moreover, it becomes clear that none of the existing state-of-the-art methods is best for all problem instances.

This paper was supported by grant PID2019-104156GB-I00 funded by MCIN/AEI/10.13039/501100011033.

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References

  1. Blum, C., Dorigo, M.: The hyper-cube framework for ant colony optimization. IEEE Trans. Man Syst. Cybern. Part B 34(2), 1161–1172 (2004)

    Article  Google Scholar 

  2. Blum, C., Festa, P.: A hybrid ant colony optimization algorithm for the far from most string problem. In: Blum, C., Ochoa, G. (eds.) EvoCOP 2014. LNCS, vol. 8600, pp. 1–12. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44320-0_1

    Chapter  Google Scholar 

  3. Ferone, D., Festa, P., Resende, M.G.C.: Hybrid metaheuristics for the far from most string problem. In: Blesa, M.J., Blum, C., Festa, P., Roli, A., Sampels, M. (eds.) HM 2013. LNCS, vol. 7919, pp. 174–188. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38516-2_14

    Chapter  Google Scholar 

  4. Ferone, D., Festa, P., Resende, M.G.: Hybridizations of grasp with path relinking for the far from most string problem. Int. Trans. Oper. Res. 23(3), 481–506 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  5. Festa, P.: On some optimization problems in mulecolar biology. Math. Biosci. 207(2), 219–234 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  6. Festa, P., Pardalos, P.: Efficient solutions for the far from most string problem. Ann. Oper. Res. 196(1), 663–682 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  7. Gallardo, J.E., Cotta, C.: A GRASP-based memetic algorithm with path relinking for the far from most string problem. Eng. Appl. Artif. Intell. 41, 183–194 (2015)

    Article  Google Scholar 

  8. Kennelly, P.J., Krebs, E.G.: Consensus sequences as substrate specificity determinants for protein kinases and protein phosphatases. J. Biol. Chem. 266(24), 15555–15558 (1991)

    Article  Google Scholar 

  9. Lanctot, J., Li, M., Ma, B., Wang, S., Zhang, L.: Distinguishing string selection problems. Inf. Comput. 185(1), 41–55 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  10. Liu, X., Liu, S., Hao, Z., Mauch, H.: Exact algorithm and heuristic for the closest string problem. Comput. Oper. Res. 38(11), 1513–1520 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  11. López-Ibáñez, M., Dubois-Lacoste, J., Cáceres, L.P., Birattari, M., Stützle, T.: The irace package: iterated racing for automatic algorithm configuration. Oper. Res. Perspect. 3, 43–58 (2016)

    MathSciNet  Google Scholar 

  12. Meneses, C.N., Oliveira, C.A., Pardalos, P.M.: Optimization techniques for string selection and comparison problems in genomics. IEEE Eng. Med. Biol. Mag. 24(3), 81–87 (2005)

    Article  Google Scholar 

  13. Mousavi, S.R.: A hybridization of constructive beam search with local search for far from most strings problem. Int. J. Comput. Inf. Eng. 4(8), 1200–1208 (2010)

    MathSciNet  Google Scholar 

  14. Mousavi, S., Babaie, M., Montazerian, M.: An improved heuristic for the far from most strings problem. J. Heuristics 18, 239–262 (2012)

    Article  Google Scholar 

  15. Nurcahyadi, T., Blum, C.: Adding negative learning to ant colony optimization: a comprehensive study. Mathematics 9(4), 361 (2021)

    Article  Google Scholar 

  16. Nurcahyadi, T., Blum, C., Manyà, F.: Negative learning ant colony optimization for maxsat. Int. J. Comput. Intell. Syst. 15(1), 1–19 (2022)

    Article  Google Scholar 

  17. Rojas-Morales, N., Riff, M.C., Montero, E.: Opposition-inspired synergy in sub-colonies of ants: the case of focused ant solver. Knowl.-Based Syst. 229, 107341 (2021)

    Article  Google Scholar 

  18. Ye, K., Zhang, C., Ning, J., Liu, X.: Ant-colony algorithm with a strengthened negative-feedback mechanism for constraint-satisfaction problems. Inf. Sci. 406–407, 29–41 (2017)

    Article  Google Scholar 

  19. Zörnig, P.: Reduced-size integer linear programming models for string selection problems: application to the farthest string problem. J. Comput. Biol. 22(8), 729–742 (2015)

    Article  Google Scholar 

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Authors and Affiliations

  1. Artificial Intelligence Research Institute (IIIA-CSIC) Campus of the UAB, Bellaterra, Spain

    Christian Blum

  2. Department of Computer Science, Faculty of Engineering, Universidad de Concepción, Concepción, Chile

    Pedro Pinacho-Davidson

Authors
  1. Christian Blum
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  2. Pedro Pinacho-Davidson
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Correspondence to Christian Blum .

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Editors and Affiliations

  1. Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile

    Leslie Pérez Cáceres

  2. Université libre de Bruxelles, Bruxelles, Belgium

    Thomas Stützle

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Blum, C., Pinacho-Davidson, P. (2023). Application of Negative Learning Ant Colony Optimization to the Far from Most String Problem. In: Pérez Cáceres, L., Stützle, T. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2023. Lecture Notes in Computer Science, vol 13987. Springer, Cham. https://doi.org/10.1007/978-3-031-30035-6_6

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  • DOI: https://doi.org/10.1007/978-3-031-30035-6_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30034-9

  • Online ISBN: 978-3-031-30035-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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