Abstract
In order to solve combinatorial optimization problem are used mainly hybrid heuristics. Inspired from nature, both genetic and ant colony algorithms could be used in a hybrid model by using their benefits. The paper introduces a new model of Ant Colony Optimization using multiple colonies with different level of sensitivity to the ant’s pheromone. The colonies react different to the changing environment, based on their level of sensitivity and thus the exploration of the solution space is extended. Several discussion follows about the fuzziness degree of sensitivity and its influence on the solution of a complex problem.
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Aggarwal, C.C., Yu, P.S.: A survey of uncertain data algorithms and applications. IEEE Trans. Knowl. Data Eng. 21(5), 609–623 (2009)
Bonabeau, E., et al.: Swarm intelligence from natural to artificial systems. Oxford University Press, Oxford (1999)
Camazine, S., et al.: Self Organization in Biological Systems. Princeton University Press, Princeton (2001)
Chira, C., Dumitrescu, D., Pintea, C.-M.: Learning sensitive stigmergic agents for solving complex problems. Comput. Inform. 29(3), 337–356 (2010)
Crisan, G.C., Nechita, E., Palade, V.: Ant-based system analysis on the traveling salesman problem under real-world settings. In: CIMA, pp. 39–59 (2014)
Crisan, G.C., Pintea, C.-M., Pop, P.C.: On the resilience of an ant-based system in fuzzy environments. An empirical study. In: FUZZ-IEEE, pp. 2588–2593 (2014)
Dorigo, M., Di Caro, G., Gambardella, L.M.: Ant algorithms for discrete optimization. Artif. Life 5(2), 137–172 (1999)
Finocchi, F., et al: Designing reliable algorithms in unreliable memories algorithms, LNCS, vol. 3669, pp. 1–8 (2005)
Grasse, P.-P: La reconstruction du nid et les coordinations interindividuelles chez bellicositermes natalensis et cubitermes sp. La theorie de la stigmergie: essai d’interpretation du comportement des termites constructeurs. Insect Soc. 6, 41–80 (1959)
Holzinger, A.: Interactive machine learning for health informatics: when do we need the human-in-the-loop? Brain Inf. 3(2), 119–131 (2016)
Holzinger, A., et al.: Towards interactive machine learning: applying ant colony algorithms to solve the traveling salesman problem with the human-in-the-loop approach. In: CD-ARES (2016)
Helsgaun, K.: An effective implementation of the Lin Kernighan TSP heuristic. Eur. J. Oper. Res. 126, 106–130 (2000)
Michener, C.D.: The social behavior of bees: a comparative study. Harvard University Press, Massachusetts (1974)
Lahrichi, N., et al.: An integrative cooperative search framework for multi-decision-attribute combinatorial optimization: application to the MDPVRP. Eur. J. Oper. Res. 246(2), 400–412 (2015)
Pintea, C.-M., Chira, C., Dumitrescu, D.: Sensitive ants: inducing diversity in the colony. Stud. Comput. Intell. 236, 15–24 (2008)
Pintea, C.-M., Chira, C., Dumitrescu, D., Pop, P.C.: Sensitive ants in solving the generalized vehicle routing problem. Int. J. Comput. Commun. 6(4), 734–741 (2011)
Pintea, C.-M., Pop, P.C.: Sensor networks security based on sensitive robots agents. A conceptual model. In: Conference CISIS, Czech Republic, vol. 89, pp. 47–56 (2012)
Pintea, C.-M., Ludwig, S.A., Crisan, G.-C.: Adaptability of a discrete PSO algorithm applied to the traveling salesman problem with fuzzy data. In: FUZZ-IEEE, pp. 1–6 (2015)
Pintea, C.-M.: A unifying survey of agent-based approaches for equality-generalized traveling salesman problem. Informatica. 26(3), 509–522 (2015)
Pop, P.C., Matei, O., Sabo, C.: A new approach for solving the generalized traveling salesman problem. In: International Workshop on Hybrid Metaheuristics, pp. 62–72 (2010)
Pop, P.C., Matei, O., Sitar, C.P.: An improved hybrid algorithm for solving the generalized vehicle routing problem. Neurocomputing 109, 76–83 (2013)
Popescu-Bodorin, N., Balas, V.E.: Fuzzy membership, possibility, probability and negation in biometrics. Acta Polytechnica Hung. 11(4), 79–100 (2014)
von Neumann, J.: Probabilistic logics and the synthesis of reliable organisms from unreliable components. In: Automata Studies, pp. 43–98 (1956)
Balas-Timar, D.V., Balas, V.E.: Ability estimation in CAT with fuzzy logic. In: IEEE ISCIII 2009, pp. 55–62 (2009)
Software ACO. http://iridia.ulb.ac.be/mdorigo/ACO/aco-code/public-software.html
Stutzle, T., Hoos, H.H.: MAX-MIN ant system. Future Gen. Comput. Syst. 16, 889–914 (2000)
TSPLibrary. http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/
Warneke, B., et al.: Smart dust: communicating with a cubic-millimeter. Computer 34, 44–51 (2001)
Yordanova, S., Merazchiev, D., Jain, L.: A two-variable fuzzy control design with application to an air-conditioning system. IEEE T. Fuzzy Sys. 23(2), 474–481 (2015)
Zadeh, L.A.: Fuzzy Sets. Inf. Control 8, 338–353 (1965)
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The study was conducted under the auspices of the IEEE-CIS Interdisciplinary Emergent Technologies task force.
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Pintea, CM., Matei, O., Ramadan, R.A., Pavone, M., Niazi, M., Azar, A.T. (2018). A Fuzzy Approach of Sensitivity for Multiple Colonies on Ant Colony Optimization. In: Balas, V., Jain, L., Balas, M. (eds) Soft Computing Applications. SOFA 2016. Advances in Intelligent Systems and Computing, vol 634. Springer, Cham. https://doi.org/10.1007/978-3-319-62524-9_8
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DOI: https://doi.org/10.1007/978-3-319-62524-9_8
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