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Adjusting Population Size of Ant Colony System Using Fuzzy Logic Controller

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Book cover Computational Collective Intelligence (ICCCI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11684))

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Abstract

The population size has a very strong impact on the efficiency, solution quality, and computational cost in a Swarm Intelligence (SI). In Ant Colony System algorithm, and as a Swarm Intelligence and population size based algorithm, the number of ants plays a very important role in directing the colony toward a high quality solution within a reasonable time. In this paper, a Fuzzy Logic strategy for adjusting the number of ants during runtime is presented. The based indicators for this adjustment are: Iteration and Convergence Rate. Some experiments are conducted using Travelling Salesman Problems, and results show that modifying the number of ants has a crucial effect on the performance of the Ant Colony System algorithm especially on the quality of solution.

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References

  1. Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: an autocatalytic optimizing process (1991)

    Google Scholar 

  2. Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1(1), 53–66 (1997)

    Article  Google Scholar 

  3. Bouzbita, S., El Afia, A., Faizi, R.: A novel based hidden markov model approach for controlling the ACS-TSP evaporation parameter. In: 2016 5th International Conference on Multimedia Computing and Systems (ICMCS), pp. 633–638. IEEE (2016)

    Google Scholar 

  4. Bouzbita, S., El Afia, A., Faizi, R., Zbakh, M.: Dynamic adaptation of the ACS-TSP local pheromone decay parameter based on the hidden markov model. In: 2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech), pp. 344–349. IEEE (2016)

    Google Scholar 

  5. Bouzbita, S., El Afia, A., Faizi, R.: Hidden markov model classifier for the adaptive ACS-TSP pheromone parameters. In: Talbi, E.-G., Nakib, A. (eds.) Bioinspired Heuristics for Optimization. SCI, vol. 774, pp. 153–169. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-95104-1_10

    Chapter  Google Scholar 

  6. Lalaoui, M., El Afia, A., Chiheb, R.: Hidden markov model for a self-learning of simulated annealing cooling law. In: 5th International Conference on Multimedia Computing and Systems (ICMCS) (2016)

    Google Scholar 

  7. Lalaoui, M., El Afia, A., Chiheb, R.: A self-tuned simulated annealing algorithm using hidden markov model. Int. J. Electr. Comput. Eng. 8(1), 291–298 (2018)

    Google Scholar 

  8. Lalaoui, M., El Afia, A., Chiheb, R.: A self-adaptive very fast simulated annealing based on hidden markov model. In: 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech) (2017)

    Google Scholar 

  9. Aoun, O., Sarhani, M., El Afia, A.: Investigation of hidden markov model for the tuning of metaheuristics in airline scheduling problems. IFAC-PapersOnLine 49(3), 347–352 (2016)

    Article  Google Scholar 

  10. Aoun, O., Sarhani, M., Afia, A.E.: Hidden markov model classifier for the adaptive particle swarm optimization. In: Amodeo, L., Talbi, E.-G., Yalaoui, F. (eds.) Recent Developments in Metaheuristics. ORSIS, vol. 62, pp. 1–15. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-58253-5_1

    Chapter  Google Scholar 

  11. El Afia, A., Sarhani, M., Aoun, O.: Hidden markov model control of inertia weight adaptation for particle swarm optimization. IFAC-PapersOnLine 50(1), 9997–10002 (2017)

    Article  Google Scholar 

  12. El Afia, A., Aoun, O., Garcia, S.: Adaptive cooperation of multi-swarm particle swarm optimizer-based hidden markov model. Prog. Artif. Intell. 8, 1–12 (2019)

    Article  Google Scholar 

  13. El Afia, A., Lalaoui, M., Chiheb, R.: A self controlled simulated annealing algorithm using hidden markov model state classification. Procedia Comput. Sci. 148, 512–521 (2019)

    Article  Google Scholar 

  14. Bouzbita, S., El Afia, A., Faizi, R.: Parameter adaptation for ant colony system algorithm using hidden markov model for TSP problems. In: Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications, p. 6. ACM (2018)

    Google Scholar 

  15. Kabbaj, M.M., El Afia, A.: Towards learning integral strategy of branch and bound. In: 2016 5th International Conference on Multimedia Computing and Systems (ICMCS), pp. 621–626. IEEE (2016)

    Google Scholar 

  16. El Afia, A., Kabbaj., M.M.: Supervised learning in branch-and-cut strategies. In: Proceedings of the 2nd International Conference on Big Data, Cloud and Applications, p. 114. ACM (2017)

    Google Scholar 

  17. Kabbaj, M.M., El Afia, A.: Adapted branch-and-bound algorithm using SVM with model selection. Int. J. Electr. Comput. Eng. (IJECE) 9(4), 2481–2490 (2019)

    Google Scholar 

  18. Aoun, O., El Afia, A., Garcia, S.: Self inertia weight adaptation for the particle swarm optimization. In: Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications, p. 8. ACM (2018)

    Google Scholar 

  19. Alobaedy, M.M., Khalaf, A.A., Muraina, I.D.: Analysis of the number of ants in ant colony system algorithm. In: 2017 5th International Conference on Information and Communication Technology (ICoIC7), pp. 1–5. IEEE (2017)

    Google Scholar 

  20. Siemiński, A.: Using hyper populated ant colonies for solving the TSP. Vietnam J. Comput. Sci. 3(2), 103–117 (2016)

    Article  Google Scholar 

  21. Liu, Y., Liu, J., Li, X., Zhang, Z.: A self-adaptive control strategy of population size for ant colony optimization algorithms. In: Tan, Y., Shi, Y., Niu, B. (eds.) Advances in Swarm Intelligence. LNCS, vol. 9712, pp. 443–450. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41000-5_44

    Chapter  Google Scholar 

  22. Liu, F., Zhong, J., Liu, C., Gao, C., Li, X.: A novel strategy of initializing the population size for ant colony optimization algorithms in TSP. In: 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), pp. 249–253. IEEE (2017)

    Google Scholar 

  23. Fidanova, S., Marinov, P.: Number of ants versus number of iterations on ant colony optimization algorithm for wireless sensor layout. In: Proceedings of the Workshop of ICT for New Materials and Nanotechnologies, Bankya, pp. 90–93 (2013)

    Google Scholar 

  24. Bai, Y., Wang, D.: Fundamentals of fuzzy logic control—fuzzy sets, fuzzy rules and defuzzifications. In: Bai, Y., Zhuang, H., Wang, D. (eds.) Advanced Fuzzy Logic Technologies in Industrial Applications. Advances in Industrial Control, pp. 17–36. Springer, London (2006). https://doi.org/10.1007/978-1-84628-469-4_2

    Chapter  MATH  Google Scholar 

  25. El Afia, A., Bouzbita, S., Faizi, R.: The effect of updating the local pheromone on ACS performance using fuzzy logic. Int. J. Electr. Comput. Eng. (IJECE) 7(4), 2161–2168 (2017)

    Article  Google Scholar 

  26. Olivas, F., Valdez, F., Castillo, O.: Ant colony optimization with parameter adaptation using fuzzy logic for TSP problems. In: Melin, P., Castillo, O., Kacprzyk, J. (eds.) Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization. SCI, vol. 601, pp. 593–603. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-17747-2_45

    Chapter  Google Scholar 

  27. Neyoy, H., Castillo, O., Soria, J.: Dynamic fuzzy logic parameter tuning for ACO and its application in TSP problems. In: Castillo, O., Melin, P., Kacprzyk, J. (eds.) Recent Advances on Hybrid Intelligent Systems. Studies in Computational Intelligence, vol. 451, pp. 259–271. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-33021-6_21

    Chapter  Google Scholar 

  28. Lalaoui, M., El Afia, A., Chiheb, R.: Simulated annealing with adaptive neighborhood using fuzzy logic controller. In: Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications, p. 7. ACM (2018)

    Google Scholar 

  29. Amir, C., Badr, A., Farag, I.: A fuzzy logic controller for ant algorithms. Comput. Inf. Syst. 11(2), 26 (2007)

    Google Scholar 

  30. Olivas, F., Valdez, F., Castillo, O., Gonzalez, C.I., Martinez, G., Melin, P.: Ant colony optimization with dynamic parameter adaptation based on interval type-2 fuzzy logic systems. Appl. Soft Comput. 53, 74–87 (2017)

    Article  Google Scholar 

  31. Reinelt, G.: TSPLIB—a traveling salesman problem library. ORSA J. Comput. 3(4), 376–384 (1991)

    Article  Google Scholar 

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Authors and Affiliations

  1. Smart Systems Laboratory, ENSIAS - Mohammed V University, Rabat, Morocco

    Safae Bouzbita, Abdellatif El Afia & Rdouan Faizi

Authors
  1. Safae Bouzbita
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  2. Abdellatif El Afia
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  3. Rdouan Faizi
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Corresponding author

Correspondence to Safae Bouzbita .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. University of Pau and Pays de l'Adour, Pau, France

    Richard Chbeir

  3. University of Pau and Pays de l'Adour, Pau, France

    Ernesto Exposito

  4. University of Pau and Pays de l'Adour, Pau, France

    Philippe Aniorté

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

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Bouzbita, S., El Afia, A., Faizi, R. (2019). Adjusting Population Size of Ant Colony System Using Fuzzy Logic Controller. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11684. Springer, Cham. https://doi.org/10.1007/978-3-030-28374-2_27

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  • DOI: https://doi.org/10.1007/978-3-030-28374-2_27

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  • Print ISBN: 978-3-030-28373-5

  • Online ISBN: 978-3-030-28374-2

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