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Principles and applications of swarm intelligence for adaptive routing in telecommunications networks

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Abstract

In the past few years, there has been much research on the application of swarm intelligence to the problem of adaptive routing in telecommunications networks. A large number of algorithms have been proposed for different types of networks, including wired networks and wireless ad hoc networks. In this paper, we give an overview of this research area. We address both the principles underlying the research and the practical applications that have been proposed. We start by giving a detailed description of the challenges in this problem domain, and we investigate how swarm intelligence can be used to address them. We identify typical building blocks of swarm intelligence systems and we show how they are used to solve routing problems. Then, we present Ant Colony Routing, a general framework in which most swarm intelligence routing algorithms can be placed. After that, we give an extensive overview of existing algorithms, discussing for each of them their contributions and their relative place in this research area. We conclude with an overview of future research directions that we consider important for the further development of this field.

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References

  • Akyildiz, I. F., Weilian, S., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40(8), 102–116.

    Google Scholar 

  • Akyildiz, I. F., Wang, X., & Wang, W. (2005). Wireless mesh networks: a survey. Computer Networks Journal, 47(4), 445–487.

    MATH  Google Scholar 

  • Asokan, R., Natarajan, A., & Nivetha, A. (2007). A swarm-based distance vector routing to support multiple quality of service metrics in mobile ad hoc networks. Journal of Computer Science, 3(9), 700–707.

    Google Scholar 

  • Babaoglu, O., Canright, G., Deutsch, A., Di Caro, G. A., Ducatelle, F., Gambardella, L. M., Ganguly, N., Jelasity, M., Montemanni, R., Montresor, A., & Urnes, T. (2006). Design patterns from biology for distributed computing. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 1(1), 26–66.

    Google Scholar 

  • Baran, B., & Sosa, R. (2000). A new approach for AntNet routing. In Proceedings of the 9th international conference on computer communications and networks (ICCCN) (pp. 303–308). Washington, DC, 2000. New York: IEEE Computer Society.

    Google Scholar 

  • Baras, J. S., & Mehta, H. (2003). A probabilistic emergent routing algorithm (PERA) for mobile ad hoc networks. In Proceedings of WiOpt’03: Modeling and optimization in mobile, ad hoc and wireless networks (pp. 20–24).

  • Bellman, R. (1957). Dynamic programming. Princeton: Princeton University Press.

    Google Scholar 

  • Bellman, R. (1958). On a routing problem. Quarterly of Applied Mathematics, 16(1), 87–90.

    MATH  MathSciNet  Google Scholar 

  • Blazevic, L., Buttyan, L., Capkun, S., Giordano, S., Hubaux, J.-P., & Le Boudec, J.-Y. (2001). Self-organization in mobile ad-hoc networks: the approach of terminodes. IEEE Communications Magazine, 39(6), 166–174.

    Google Scholar 

  • Bonabeau, E., Henaux, F., Guérin, S., Snyers, D., Kuntz, P., & Theraulaz, G. (1998). Routing in telecommunication networks with “smart” ant-like agents. In S. Albayrak, & F. Garijo (Eds.), Lecture notes in artificial intelligence : Vol. 1437. Proceedings of IATA’98, 2nd Int. workshop on intelligent agents for telecommunication applications (pp. 60–71). Berlin: Springer.

    Google Scholar 

  • Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm intelligence: from natural to artificial systems. New York: Oxford University Press.

    MATH  Google Scholar 

  • Boyan, J. A., & Littman, M. L. (1994). Packet routing in dynamically changing networks: a reinforcement learning approach. In J. D. Cowan, G. Tesauro, & J. Alspector (Eds.), Advances in neural information processing systems 6 (NIPS6) (pp. 671–678). San Francisco: Morgan Kaufmann.

    Google Scholar 

  • Braden, R., Clark, D., & Shenker, S. (1994). Integrated services in the Internet architecture: an overview. Internet Engineering Task Force, RFC 1633 (Informational).

  • Broch, J., Maltz, D. A., Johnson, D. B., Hu, Y.-C., & Jetcheva, J. (1998). A performance comparison of multi-hop wireless ad hoc network routing protocols. In Proceedings of the fourth annual ACM/IEEE international conference on mobile computing and networking (MobiCom) (pp. 85–97). New York: ACM.

    Google Scholar 

  • Câmara, D., & Loureiro, A. A. F. (2001). GPS/ANT-like routing in ad hoc networks. Telecommunication Systems, 18(1–3), 85–100.

    MATH  Google Scholar 

  • Camilo, T., Carreto, C., Sá Silva, J., & Boavida, F. (2006). An energy-efficient ant-based routing algorithm for wireless sensor networks. In M. Dorigo, L. Gambardella, M. Birattari, A. Martinoli, R. Poli, & T. Stützle (Eds.), Lecture notes in computer science : Vol. 4150. Proceedings of the 5th international workshop on ant colony optimization and swarm intelligence (ANTS) (pp. 49–59). Berlin: Springer.

    Google Scholar 

  • Canright, G. (2002). Ants and loops. In M. Dorigo, G. A. Di Caro, & M. Sampels (Eds.), Lecture notes in computer science : Vol. 2463. Proceedings of the 3rd international workshop on ant algorithms (ANTS) (pp. 235–242). Berlin: Springer.

    Google Scholar 

  • Carrillo, L., Marzo, J. L., Harle, D., & Vilà, P. (2003). A review of scalability and its application in the evaluation of the scalability measure of AntNet routing. In C. E. Palau Salvador (Ed.), Proceedings of the IASTED conference on communication systems and networks (CSN) (pp. 317–323). Calgary: ACTA Press.

    Google Scholar 

  • Carrillo, L., Guadall, C., Marzo, J. L., Di Caro, G., Ducatelle, F., & Gambardella, L. M. (2005). Differentiated quality of service scheme based on the use of multi-classes of ant-like mobile agents. In M. Diaz, A. Azcorra, P. Owezarski, & S. Fdida (Eds.), CoNEXT’05: Proceedings of the 2005 ACM conference on emerging network experiment and technology (pp. 234–235). New York: ACM.

    Google Scholar 

  • Cauvery, N., & Viswanatha, K. (2008). Enhanced ant colony based algorithm for routing in mobile ad hoc network. Proceedings of World Academy of Science, Engineering and Technology, 36, 30–35.

    Google Scholar 

  • Chakeres, I. D., & Perkins, C. E. (2008). Dynamic MANET on-demand (DYMO) routing. Internet Engineering Task Force. Internet Draft.

  • Chen, S., & Nahrstedt, K. (1998). An overview of quality-of-service routing for the next generation high-speed networks: problems and solutions. IEEE Network Magazine, 12(6), 64–79. Special Issue on Transmission and Distribution of Digital Video.

    Google Scholar 

  • Clausen, T., & Jacquet, P. (2003). Optimized link state routing protocol (OLSR). Internet Engineering Task Force, RFC 3626 (Experimental).

  • Crespi, V., Galstyan, A., & Lerman, K. (2008). Top-down vs bottom-up methodologies in multi-agent system design. Autonomous Robots, 24(3), 303–313.

    Google Scholar 

  • Daneshtalab, M., Sobhani, A., Afzali-Kusha, A., Fatemi, O., & Navabi, Z. (2006). NoC hot spot minimization using AntNet dynamic routing algorithm. In Proceedings of the international conference on application-specific systems, architectures and processors (ASAP) (pp. 33–38). Washington: IEEE Computer Society.

    Google Scholar 

  • Demeyer, S., De Leenheer, M., Baert, J., Pickavet, M., & Demeester, P. (2008). Ant colony optimization for the routing of jobs in optical grid networks. Journal of Optical Networking, 7(2), 160–172.

    Google Scholar 

  • Dhillon, S., Arbona, X., & Van Mieghem, P. (2007). Ant routing in mobile ad hoc networks. In Proceedings of the 3rd international conference on networking and services (INCS) (pp. 67–74). Washington: IEEE Computer Society.

    Google Scholar 

  • Di Caro, G. A. (2004). Ant colony optimization and its application to adaptive routing in telecommunication networks. PhD thesis, Faculté des Sciences Appliquées, Université Libre de Bruxelles, Brussels, Belgium.

  • Di Caro, G. A., & Dorigo, M. (1998a). AntNet: Distributed stigmergetic control for communications networks. Journal of Artificial Intelligence Research (JAIR), 9, 317–365.

    MATH  Google Scholar 

  • Di Caro, G. A., & Dorigo, M. (1998b). Two ant colony algorithms for best-effort routing in datagram networks. In Y. Pan, S. G. Akl, & K. Li (Eds.), Parallel and distributed computing and systems (pp. 541–546). Proceedings of the tenth IASTED international conference (PDCS’98) Anaheim: IASTED/ACTA Press.

    Google Scholar 

  • Di Caro, G. A., & Vasilakos, T. (2000). Ant-SELA: Ant-agents and stochastic automata learn adaptive routing tables for QoS routing in ATM networks. In ANTS’2000—From ant colonies to artificial ants: Second international workshop on ant colony optimization. Brussels, Belgium.

  • Di Caro, G. A., Ducatelle, F., & Gambardella, L. M. (2008). Theory and practice of ant colony optimization for routing in dynamic telecommunications networks. In N. Sala, & F. Orsucci (Eds.), Reflecting interfaces: the complex coevolution of information technology ecosystems (pp. 185–216). Hershey: Idea Group.

    Google Scholar 

  • Doi, S., & Yamamura, M. (2002). BntNetL and its evaluation on a situation of congestion. Electronics and Communications in Japan (Part I: Communications), 85(9), 31–41.

    Google Scholar 

  • Dorigo, M., & Stützle, T. (2004). Ant colony optimization. Cambridge: MIT Press.

    MATH  Google Scholar 

  • Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant System: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics—Part B, 26(1), 29–41.

    Google Scholar 

  • Dorigo, M., Di Caro, G. A., & Gambardella, L. M. (1999). Ant algorithms for discrete optimization. Artificial Life, 5(2), 137–172.

    Google Scholar 

  • Dorigo, M., Bonabeau, E., & Theraulaz, G. (2000). Ant algorithms and stigmergy. Future Generation Computer Systems, 16(8), 851–871.

    Google Scholar 

  • Dressler, F. (2007). Self-organization in sensor and actor networks. Hoboken: Wiley.

    Google Scholar 

  • Ducatelle, F. (2007). Adaptive routing in ad hoc wireless multi-hop networks. PhD thesis, Università della Svizzera Italiana (USI), Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA), Lugano, Switzerland.

  • Ducatelle, F., Di Caro, G. A., & Gambardella, L. M. (2005). Using ant agents to combine reactive and proactive strategies for routing in mobile ad hoc networks. International Journal of Computational Intelligence and Applications, 5(2), 169–184. Special Issue on Nature-Inspired Approaches to Networks and Telecommunications.

    MATH  Google Scholar 

  • Engelbrecht, A. P. (2006). Fundamentals of computational swarm intelligence. Hoboken: Wiley.

    Google Scholar 

  • Eugster, P., Guerraoui, R., Kermarrec, A.-M., & Massoulie, L. (2004). From epidemics to distributed computing. IEEE Computer, 37(5), 60–67.

    Google Scholar 

  • Farooq, M. (2009). Bee-inspired protocol engineering: from nature to networks. Natural computing series. Berlin: Springer.

    Google Scholar 

  • Farooq, M. & Di Caro, G. A. (2008). Routing protocols for next-generation intelligent networks inspired by collective behaviors of insect societies. In C. Blum, & D. Merkle (Eds.), Natural computing series. Swarm intelligence: introduction and applications. Berlin: Springer.

    Google Scholar 

  • F. H. Fitzek, & M. D. Katz (Eds.) (2007). Cognitive wireless networks: concepts, methodologies and visions inspiring the age of enlightenment of wireless communications. Berlin: Springer.

    Google Scholar 

  • Gao, Z.-H., Guo, Q., & Wang, P. (2007). An adaptive routing based on an improved ant colony optimization in LEO satellite networks. In Proceedings of the international conference on machine learning and cybernetics (pp. 1041–1044). Washington: IEEE Computer Society.

    Google Scholar 

  • Goss, S., Aron, S., Deneubourg, J. L., & Pasteels, J. M. (1989). Self-organized shortcuts in the Argentine ant. Naturwissenschaften, 76, 579–581.

    Google Scholar 

  • Grassé, P. P. (1959). La reconstruction du nid et les coordinations interindividuelles chez Bellicositermes natalensis et Cubitermes sp. La théorie de la stigmergie: essai d’interprétation du comportement des termites constructeurs. Insectes Sociaux, 6, 41–81.

    Google Scholar 

  • Guéret, C., Monmarché, N., & Slimane, M. (2006). Autonomous gossiping of information in a p2p network with artificial ants. In M. Dorigo, L. Gambardella, M. Birattari, A. Martinoli, R. Poli, & T. Stützle (Eds.), Lecture notes in artificial intelligence : Vol. 4150. Proceedings of the 5th international workshop on ant colony optimization and swarm intelligence (ANTS) (pp. 388–395). Berlin: Springer.

    Google Scholar 

  • Günes, M., Sorges, U., & Bouazizi, I. (2002). ARA—the ant-colony based routing algorithm for MANETs. In Proceedings of the 2002 ICPP international workshop on ad hoc networks IWAHN 2002 (pp. 79–85). Washington: IEEE Computer Society.

    Google Scholar 

  • Heissenbüttel, M., Braun, T., Jörg, D., & Huber, T. (2006). A framework for routing in large ad-hoc networks with irregular topologies. International Journal of Ad Hoc & Sensor Wireless Networks, 2(2), 119–128.

    Google Scholar 

  • Heusse, M., Snyers, D., Guérin, S., & Kuntz, P. (1998). Adaptive agent-driven routing and load balancing in communication networks. Advances in Complex Systems, 1(2), 237–254.

    Google Scholar 

  • Hoffmann-Wellenhof, B., Lichtenegger, H., & Collins, J. (2001). GPS: Theory and practice. Berlin: Springer.

    Google Scholar 

  • Huang, C.-J., Chuang, Y.-T., Yang, D.-X., Chen, I.-F., Chen, Y.-J., & Hu, K.-W. (2008). A mobility-aware link enhancement mechanism for vehicular ad hoc networks. EURASIP Journal on Wireless Communications and Networking, 8(3).

  • Hussein, O., Saadawi, T., & Jong Lee, M. (2005). Probability routing algorithm for mobile ad hoc networks’ resources management. IEEE Journal on Selected Areas in Communications, 23(12), 2248–2259.

    Google Scholar 

  • Johnson, D. B., & Maltz, D. A. (1996). Dynamic source routing in ad hoc wireless networks. In Mobile Computing (pp. 153–181). Dordrecht: Kluwer Academic.

    Google Scholar 

  • Kalaavathi, B., & Duraiswamy, K. (2008). Ant colony based node disjoint hybrid multi-path routing for mobile ad hoc networks. Journal of Computer Science, 4(2), 80–86.

    Google Scholar 

  • Kassabalidis, I., El-Sharkawi, M. A., Marks II, R. J., Arabshahi, P., & Gray, A. A. (2001). Swarm intelligence for routing in communication networks. In Proceedings of the IEEE global telecommunications conference (Globecom) (pp. 3613–3617). Washington: IEEE Computer Society.

    Google Scholar 

  • Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of the IEEE international conference on neural networks (pp. 1942–1948). Piscataway: IEEE Press.

    Google Scholar 

  • Kennedy, J., & Eberhart, R. C. (1999). The particle swarm: social adaptation in information processing systems. In D. Corne, M. Dorigo, & F. Glover (Eds.), New ideas in optimization. London: McGraw-Hill.

    Google Scholar 

  • Kennedy, J., Eberhart, R. C., & Shi, Y. (2001). Swarm intelligence. San Francisco: Morgan Kaufmann.

    Google Scholar 

  • Kephart, J., & Chess, D. (2003). The vision of autonomic computing. IEEE Computer Magazine, 36(1), 41–50.

    Google Scholar 

  • Kilkki, K. (1999). Differentiated services for the Internet. Indianapolis: Macmillan Publishing.

    Google Scholar 

  • Kudelski, M., & Pacut, A. (2009). Ant routing with distributed geographical localization of knowledge in ad-hoc networks. In Lecture notes in computer science : Vol. 5484. Applications of evolutionary computing—proceedings of EvoWorkshops 2009 (EvoCOMNET) (pp. 111–116). Berlin: Springer.

    Google Scholar 

  • Liang, S., Zincir-Heywood, A. N., & Heywood, M. I. (2002). Intelligent packets for dynamic network routing using distributed genetic algorithm. In Proceedings of the genetic and evolutionary computation conference (GECCO) (pp. 88–96). San Francisco: Morgan Kaufmann.

    Google Scholar 

  • Liu, L., & Feng, G. (2005). A novel ant colony based QoS-aware routing algorithm for MANETs. In Lecture notes in computer science : Vol. 3612. Proceedings of the first international conference on advances in natural computation (ICNC) (pp. 457–466). Berlin: Springer.

    Google Scholar 

  • Liu, Z., Kwiatkowska, M., & Constantinou, C. (2005). A biologically inspired QoS routing algorithm for mobile ad hoc networks. In Proceedings of the international conference on advanced information networking and applications (pp. 426–431). Washington: IEEE Computer Society.

    Google Scholar 

  • Mahmoud, Q. (Ed.) (2007). Cognitive networks: towards self-aware networks. Chichester: Wiley.

    Google Scholar 

  • Malkin, G. S. (1999). RIP: An intra-domain routing protocol. Reading: Addison-Wesley.

    Google Scholar 

  • Marwaha, S., Tham, C. K., & Srinivasan, D. (2002). Mobile agents based routing protocol for mobile ad hoc networks. In Proceedings of the IEEE global telecommunications conference (Globecom) (pp. 163–167). Washington: IEEE Computer Society.

    Google Scholar 

  • Matsuo, H., & Mori, K. (2001). Accelerated ants routing in dynamic networks. In Proceedings of the 2nd ACIS international conference on software engineering, artificial intelligence, networking and parallel/distributed computing (pp. 333–339). Mt. Pleasant: ACIS.

    Google Scholar 

  • Michalareas, T., & Sacks, L. (2001). Stigmergic techniques for solving multi-constraint routing for packet networks. In P. Lorenz (Ed.), Lecture notes in computer science : Vol. 2094. Proceedings of the first international conference on networking (ICN), Part II, Networking (pp. 687–697). Berlin: Springer.

    Google Scholar 

  • Minar, N., Kramer, K. H., & Maes, P. (1999). Cooperating mobile agents for dynamic network routing. In A. Hayzelden, & J. Bigham (Eds.), Software agents for future communication systems (pp. 287–304). Secaucus: Springer. Chapter 12.

    Google Scholar 

  • Moy, J. (1998). OSPF: Anatomy of an Internet routing protocol. Reading: Addison-Wesley.

    Google Scholar 

  • Muraleedharan, R., & Osadciw, L. A. (2004). A predictive sensor network using ant system. In R. M. Rao, S. A. Dianat, & M. D. Zoltowski (Eds.), Proceedings of the SPIE : Vol. 5440. Digital wireless communications VI (pp. 181–192). Bellingham: SPIE.

    Google Scholar 

  • Navarro Varela, G., & Sinclair, M. C. (1999). Ant colony optimisation for virtual-wavelength-path routing and wavelength allocation. In Proceedings of the congress on evolutionary computation (pp. 1809–1816). Piscataway: IEEE Press.

    Google Scholar 

  • Ngo, S., Jiang, X., Le, V., & Horiguchi, S. (2006). Ant-based survivable routing in dynamic WDM networks with shared backup paths. Journal of Supercomputing, 36(3), 297–307.

    Google Scholar 

  • Oida, K., & Kataoka, A. (1999). Lock-free AntNet and its evaluation for adaptiveness. Journal of IEICE B, J82-B(7), 1309–1319. (In Japanese).

    Google Scholar 

  • Oida, K., & Sekido, M. (2000). ARS: An efficient agent-based routing system for QoS guarantees. Computer Communications, 23(14), 1437–1447.

    Google Scholar 

  • Okdem, S., & Karaboga, D. (2006). Routing in wireless sensor networks using ant colony optimization. In Proceedings of the first NASA/ESA conference on adaptive hardware and systems (AHS) (pp. 401–404). Washington: IEEE Computer Society.

    Google Scholar 

  • Papadimitriou, C. H., & Steiglitz, K. (1982). Combinatorial optimization. Upper Saddle River: Prentice-Hall.

    MATH  Google Scholar 

  • Perkins, C., & Bhagwat, P. (1994). Highly dynamic destination-sequenced distance-vector routing (DSDV) for mobile computers. In ACM SIGCOMM conference on communications architectures, protocols and applications (pp. 234–244). New York: ACM.

    Google Scholar 

  • Perkins, C. E., & Royer, E. M. (1999). Ad-hoc on-demand distance vector routing. In Proceedings of the second IEEE workshop on mobile computing systems and applications (pp. 90–100). Washington: IEEE Computer Society.

    Google Scholar 

  • Peshkin, L. (2002). Reinforcement learning by policy search. PhD thesis, Department of Computer Science, Brown University, Providence, Rhode Island, April 2002.

  • Peshkin, L., & Savova, V. (2002). Reinforcement learning for adaptive routing. In International joint conference on neural networks (IJCNN) (Vol. 2, pp. 1825–1830). Piscataway: IEEE Press.

    Google Scholar 

  • Poli, R., Kennedy, J., & Blackwell, T. (2007). Particle swarm optimization. An overview. Swarm intelligence, 1(1), 33–57.

    Google Scholar 

  • Rajagopalan, S., & Shen, C. (2005). ANSI: A unicast routing protocol for mobile ad hoc networks using swarm intelligence. In Proceedings of the international conference on artificial intelligence (ICAI) (pp. 104–110). CSREA Press.

  • Roth, M., & Wicker, S. (2005). Termite: a swarm intelligence routing algorithm for mobile wireless ad-hoc networks. In Stigmergic optimization (pp. 155–184). Berlin: Springer. Chapter 7

    Google Scholar 

  • Rothkrantz, L., & van der Put, R. (1998). Routing in packet switched networks using agents. In First international workshop on ant colony optimization (ANTS). Université Libre de Bruxelles, Brussels, Belgium.

  • Royer, E. M., & Toh, C.-K. (1999). A review of current routing protocols for ad hoc mobile wireless networks. IEEE Personal Communications, 6(2), 46–55.

    Google Scholar 

  • Rubinstein, R. Y. (2000). Combinatorial optimization, cross-entropy, ants and rare events. In S. Uryasev, & P. M. Pardalos (Eds.), Stochastic optimization: algorithms and applications (pp. 303–364). Dordrecht: Kluwer Academic.

    Google Scholar 

  • Saleem, M., & Farooq, M. (2007a). BeeSensor: A bee-inspired power aware routing protocol for wireless sensor networks. In Lecture notes in computer science : Vol. 4449. Applications of evolutionary computing (pp. 81–90). Proceedings of EvoWorkshops 2007. Berlin: Springer.

    Google Scholar 

  • Saleem, M., & Farooq, M. (2007b). A framework for empirical evaluation of nature inspired routing protocols for wireless sensor networks. In Proceedings of the congress on evolutionary computing (CEC) (pp. 751–758). Washington: IEEE Computer Society.

    Google Scholar 

  • Sandalidis, H., Mavromoustakis, K., & Stavroulakis, P. (2004). Ant-based probabilistic routing with pheromone and antipheromone mechanisms. International Journal of Communication Systems (IJCS), 17(1), 55–62.

    Google Scholar 

  • Schoonderwoerd, R., Holland, O., Bruten, J., & Rothkrantz, L. (1996). Ant-based load balancing in telecommunications networks. Adaptive Behavior, 5(2), 169–207.

    Google Scholar 

  • Shen, C.-C., & Rajagopalan, S. (2007). Protocol-independent multicast packet delivery improvement service for mobile ad hoc networks. Journal of Computer Science, 5(2), 210–227.

    Google Scholar 

  • Sigel, E., Denby, B., & Le Heárat-Mascle, S. (2002). Application of ant colony optimization to adaptive routing in a LEO telecommunications satellite network. Annals of Telecommunications, 57(5–6), 520–539.

    Google Scholar 

  • Sim, K. M., & Sun, W. H. (2003). Ant colony optimization for routing and load-balancing: survey and new directions. IEEE Transactions on Systems, Man, and Cybernetics—Part A, 33(5), 560–572.

    Google Scholar 

  • Subramanian, D., Druschel, P., & Chen, J. (1997). Ants and reinforcement learning: a case study in routing in dynamic networks. In Proceedings of the international joint conference on artificial intelligence (IJCAI) (pp. 832–838). San Francisco: Morgan Kaufmann.

    Google Scholar 

  • Sum, J., Shen, H., Young, G., & Wu, J. (2003). Analysis on extended ant routing algorithms for network routing and management. Journal of Supercomputing, 24(3), 327–340.

    MATH  Google Scholar 

  • Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: an introduction. Cambridge: MIT Press.

    Google Scholar 

  • Tadrus, S., & Bai, L. (2003). A QoS network routing algorithm using multiple pheromone tables. In Proceedings of the IEEE/WIC international conference on Web intelligence (pp. 132–138). Washington: IEEE Computer Society.

    Google Scholar 

  • Tadrus, S., & Bai, L. (2005). QColony: A multi-pheromone best-fit QoS routing algorithm as an alternative to shortest-path routing algorithms. International Journal of Computational Intelligence and Applications (IJCIA), 5(2), 141–167.

    MATH  Google Scholar 

  • Tanenbaum, A. S. (2002). Computer networks (4th ed.). Upper Saddle River: Prentice-Hall.

    Google Scholar 

  • Tatomir, B., & Rothkrantz, L. (2004). Dynamic routing in mobile wireless networks using ABC-AdHoc. In Lecture notes in computer science : Vol. 3172. Ant colony optimization and swarm intelligence: Proceedings of the fourth international workshop on ant colony optimization and swarm intelligence (ANTS) (pp. 334–341). Berlin: Springer.

    Google Scholar 

  • Theraulaz, G., & Bonabeau, E. (1999). A brief history of stigmergy. Artificial Life, 5(2), 97–116. Special Issue on Stigmergy.

    Google Scholar 

  • Vasilakos, A. V., & Papadimitriou, G. A. (1995). A new approach to the design of reinforcement scheme for learning automata: stochastic estimator learning algorithms. Neurocomputing, 7(275), 649–654.

    Google Scholar 

  • Verstraete, V., Strobbe, M., Van Breusegem, E., Coppens, J., Pickavet, M., & Demeester, P. (2006). AntNet: ACO routing algorithm in practice. In Proceedings of the 8th INFORMS telecommunications conference. Hanover: INFORMS.

    Google Scholar 

  • Vrancx, P., Nowé, A., & Steenhaut, K. (2005). Multi-type ACO for light path protection. In Lecture notes in computer science : Vol. 3898. Proceedings of the first international workshop on learning and adaption in multi-agent systems (LAMAS) (pp. 207–215). Berlin: Springer.

    Google Scholar 

  • Wedde, H. F., & Farooq, M. (2005). BeeHive: New ideas for developing routing algorithms inspired by honey bee behavior. In Handbook of bioinspired algorithms and applications (pp. 321–339). Boca Raton: Chapman & Hall/CRC. Chapter 21.

    Google Scholar 

  • Wedde, H. F., Farooq, M., & Zhang, Y. (2004). BeeHive: An efficient fault tolerant routing algorithm under high loads inspired by honey bee behavior. In Lecture notes in computer science : Vol. 3172. Ants algorithms—Proceedings of ANTS 2004, the fourth international workshop on ant algorithms (pp. 83–94). Berlin: Springer.

    Google Scholar 

  • White, T., Pagurek, B., & Oppacher, F. (1998). Connection management using adaptive mobile agents. In Proceedings of the international conference on parallel and distributed processing techniques and applications (PDPTA) (pp. 802–809). CSREA Press.

  • Wittner, O., & Helvik, B. E. (2002). Cross entropy guided ant-like agents finding dependable primary/backup path patterns in networks. In Proceedings of the congress on evolutionary computation (CEC) (pp. 1528–1533). Washington: IEEE Computer Society.

    Google Scholar 

  • Wittner, O., & Helvik, B. E. (2006). CE-Ants: Ant-like agents for path management in the next-generation Internet. Ercim News, 64, 31–32.

    Google Scholar 

  • Wittner, O., Helvik, B. E., & Nicola, V. (2005). Internet failure protection using Hamiltonian p-cycles found by ant-like agents. In Proceedings of the fifth international workshop on design of reliable communication networks (DRCN) (pp. 437–444). Washington: IEEE Computer Society.

    Google Scholar 

  • Yang, Y., Zincir-Heywood, A. N., Heywood, M. I., & Srinivas, S. (2002). Agent-based routing algorithms on a LAN. In Proceedings of the IEEE Canadian conference on electrical and computer engineering (CCECE) (pp. 1442–1447). Washington: IEEE Computer Society.

    Google Scholar 

  • Yong, L., Guang-Zhou, Z., Fan-Jun, S., & Xiao-Run, L. (2004). Adaptive swarm-based routing in communication networks. Journal of Zhejiang University Science, 5(7), 867–872.

    Google Scholar 

  • Zhang, X.-H., & Xu, W.-B. (2006). QoS based routing in wireless sensor network with particle swarm optimization. In Lecture notes in artificial intelligence : Vol. 4088. Agent computing and multi-agent systems (pp. 602–607). Berlin: Springer.

    Google Scholar 

  • Zheng, X., Guo, W., & Liu, R. (2004). An ant-based distributed routing algorithm for ad-hoc networks. In Proceedings of the international conference on communications, circuits and systems (ICCCAS) (pp. 412–417). Washington: IEEE Computer Society.

    Google Scholar 

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  1. “Dalle Molle” Institute for Artificial Intelligence Studies (IDSIA), Galleria 2, 6928, Manno, Switzerland

    Frederick Ducatelle, Gianni A. Di Caro & Luca M. Gambardella

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  1. Frederick Ducatelle
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  2. Gianni A. Di Caro
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  3. Luca M. Gambardella
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Correspondence to Gianni A. Di Caro.

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Ducatelle, F., Di Caro, G.A. & Gambardella, L.M. Principles and applications of swarm intelligence for adaptive routing in telecommunications networks. Swarm Intell 4, 173–198 (2010). https://doi.org/10.1007/s11721-010-0040-x

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  • DOI: https://doi.org/10.1007/s11721-010-0040-x

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