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Erschienen in:
Enhanced Multiobjective Population-Based Incremental Learning with Applications in Risk Treaty Optimization Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

A Hybrid Discrete Artificial Bee Colony Algorithm for the Multicast Routing Problem

verfasst von : Yannis Marinakis, Magdalene Marinaki, Athanasios Migdalas

Erschienen in: Applications of Evolutionary Computation

Verlag: Springer International Publishing

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Abstract

In this paper, a new algorithm is proposed for the solution of the Multicast Routing Problem. The algorithm is based on the Artificial Bee Colony approach hybridized with Variable Neighborhood Search. The quality of the algorithm is evaluated with experiments conducted on suitably modified benchmark instances of the Euclidean Traveling Salesman Problem from the TSP library. The results of the algorithm are compared to results obtained by several versions of the Particle Swarm Optimization algorithm. The comparisons indicated the effectiveness of the new approach.

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Vorheriges Kapitel Application of Evolutionary Algorithms for the Optimization of Genetic Regulatory Networks
Nächstes Kapitel Evolving Coverage Optimisation Functions for Heterogeneous Networks Using Grammatical Genetic Programming
Literatur
1.
Baykasoglu, A., Ozbakir, L., Tapkan, P.: Artificial bee colony algorithm and its application to generalized assignment problem. In: Chan, F.T.S., Tiwari, M.K. (eds.) Swarm Intelligence, Focus on Ant and Particle Swarm Optimization, pp. 113–144. I-Tech Education and Publishing, Vienna (2007)
2.
Karaboga, D., Akay, B.: A survey: algorithms simulating bee swarm intelligence. Artif. Intell. Rev. 31, 61–85 (2009)CrossRef
3.
Xing, B., Gao, W.J.: Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms. Intelligent Systems Reference Library, vol. 62. Springer International Publishing, Cham (2014)CrossRefMATH
4.
Abbass, H.A.: A monogenous mbo approach to satisfiability. In: International Conference on Computational Intelligence for Modeling, Control and Automation, CIMCA 2001, Las Vegas, NV, USA (2001)
5.
Abbass, H.A.: Marriage in honey-bee optimization (MBO): a haplometrosis polygynous swarming approach. In: The Congress on Evolutionary Computation (CEC 2001), Seoul, Korea, pp. 207–214 (2001)
6.
Marinakis, Y., Marinaki, M., Dounias, G.: Honey bees mating optimization algorithm for the vehicle routing problem. In: Krasnogor, N., Nicosia, G., Pavone, M., Pelta, D. (eds.) NICSO 2007. SIC, vol. 129, pp. 139–148. Springer, Heidelberg (2008)CrossRef
7.
Marinakis, Y., Marinaki, M.: Bumble bees mating optimization algorithm for the vehicle routing problem. In: Panigrahi, B.K., Shi, Y., Lim, M.-H. (eds.) Handbook of Swarm Intelligence. ALO, vol. 8, pp. 347–369. Springer, Heidelberg (2011)CrossRef
8.
Bitam, S., Mellouk, A.: Bee life-based multi constraints multicast routing optimization for vehicular ad hoc networks. J. Netw. Comput. Appl. 36, 981–991 (2013)CrossRef
9.
Yang, X.-S.: Engineering optimizations via nature-inspired virtual bee algorithms. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2005. LNCS, vol. 3562, pp. 317–323. Springer, Heidelberg (2005)CrossRef
10.
Wedde, H.F., Farooq, M., Zhang, Y.: Beehive: an efficient fault-tolerant routing algorithm inspired by honey bee behavior. In: Dorigo, M., Birattari, M., Blum, C., Gambardella, L.M., Mondada, F., Stützle, T. (eds.) ANTS 2004. LNCS, vol. 3172, pp. 83–94. Springer, Heidelberg (2004)CrossRef
11.
Munoz, M.A., Lopez, J.A., Caicedo, E.: An artificial beehive algorithm for continuous optimization. Int. J. Intell. Syst. 24, 1080–1093 (2009)CrossRefMATH
12.
Pham, D.T., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S., Zaidi, M.: The bees algorithm - a novel tool for complex optimization problems. In: IPROMS 2006 Proceeding 2nd International Virtual Conference on Intelligent Production Machines and Systems. Elsevier, Oxford (2006)
13.
Drias, H., Sadeg, S., Yahi, S.: Cooperative bees swarm for solving the maximum weighted satisfiability problem. In: Cabestany, J., Prieto, A.G., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 318–325. Springer, Heidelberg (2005)CrossRef
14.
Comellas, F., Martinez-Navarro, J.: Bumblebees: a multiagent combinatorial optimization algorithm inspired by social insect behavior. In: Proceedings of First ACM/ SIGEVO Summit on Genetic and Evolutionary Computation (GECCO), pp. 811–814 (2009)
15.
Teodorovic, D., Dell’Orco, M.: Bee colony optimization - a cooperative learning approach to complex transportation problems. In: Advanced OR and AI Methods in Transportation, Proceedings of the 16th Mini - EURO Conference and 10th Meeting of EWGT, pp. 51–60 (2005)
16.
Hackel, S., Dippold, P.: The bee colony-inspired algorithm (BCiA)-a two stage approach for solving the vehicle routing problem with time windows. In: Proceedings of GECCO 2009, Montreal, Quebec, Canada, pp. 25–32 (2009)
17.
Sato, T., Hagiwara, M.: Bee system: finding solution by a concentrated search. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 3954–3959 (1997)
18.
Quijano, N., Passino, K.M.: Honey bee social foraging algorithms for resource allocation: theory and application. Eng. Appl. Artif. Intell. 23, 845–861 (2010)CrossRef
19.
Maia, R.D., Castro, L.N.D., Caminhas, W.M.: Bee colonies as model for multimodal continuous optimization: the optbees algorithm. In: IEEE World Congress on Computational Intelligence (WCCI), Brisbane, Australia, pp. 1–8 (2012)
20.
McCaffrey, J.D., Dierking, H.: An empirical study of unsupervised rule set extraction of clustered categorical data using a simulated bee colony algorithm. In: Governatori, G., Hall, J., Paschke, A. (eds.) RuleML 2009. LNCS, vol. 5858, pp. 182–192. Springer, Heidelberg (2009)CrossRef
21.
Fan, H., Zhong, Y.: A rough set approach to feature selection based on wasp swarm optimization. J. Comput. Inf. Syst. 8, 1037–1045 (2012)
22.
Theraulaz, G., Goss, S., Gervet, J., Deneubourg, J.L.: Task differentiation in polistes wasps colonies: a model for self-organizing groups of robots. In: First International Conference on Simulation of Adaptive Behavior, pp. 346–355. MIT Press, Cambridge (1991)
23.
Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Global Optim. 39, 459–471 (2007)MathSciNetCrossRefMATH
24.
Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. 8, 687–697 (2008)CrossRef
25.
Karaboga, D., Gorkemli, B., Ozturk, C., Karaboga, N.: A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif. Intell. Rev. 42, 21–57 (2014)CrossRef
26.
Marinakis, Y., Migdalas, A.: A particle swarm optimization algorithm for the multicast routing problem. In: Batsyn, M.V., Kalyagin, V.A., Pardalos, P.M. (eds.) Models, Algorithms and Technologies for Network Analysis: Third International Conference on Network Analysis. Springer Proceedings in Mathematics & Statistics, vol. 104, pp. 69–91. Springer International Publishing, Switzerland (2014)
27.
Oliveira, C.A.S., Pardalos, P.M.: A survey of combinatorial optimization problems in multicast routing. Comput. Oper. Res. 32, 1953–1981 (2005)CrossRefMATH
28.
Oliveira, C.A.S., Pardalos, P.M., Resende, M.G.C.: Optimization problems in multicast tree construction. In: Resende, M.G.C., Pardalos, P.M. (eds.) Handbook of Optimization in Telecommunications, pp. 701–731. Springer, New York (2006)CrossRef
29.
Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman and Company, New York (1979)MATH
30.
Chow, C.H.: On multicast path finding algorithms. In: IEEE INFOCOM 1991, pp. 1974–1283. IEEE (1991)
31.
Takahashi, H., Mutsuyama, A.: An approximate solution for the steiner problem in graphs. Math. Jpn. 6, 573–577 (1980)
32.
Waxman, B.M.: Routing of multipoint connections. IEEE J. Sel. Areas Commun. 1, 286–292 (1988)
33.
Kompleea, V.P., Pasquale, J.C., Polyzos, G.C.: Multicast routing for multimedia communication. IEEE/ACM Trans. Networking 1, 286–292 (1993)CrossRef
34.
Tode, H., Sakai, Y., Yamamoto, M., Okada, H., Tezuka, Y.: Multicast routing algorithm for nodal load balancing. In: IEEE INFOCOM 1992, pp. 2086–2095. IEEE (1992)
35.
Hwang, R.H., Do, W.Y., Yang, S.C.: Multicast routing based on genetic algorithms. In: WiOpt 2003: Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks. INRIA Sophia-Antipolis, France (2003)
36.
Wu, J.J., Hwang, R.H.: Multicast routing with multiple constraints. Inf. Sci. 124, 29–57 (2000)CrossRef
37.
D’Andreagiovanni, F., Nardin, A.: Towards the fast and robust optimal design of wireless body area networks. Appl. Soft Comput. 37, 971–982 (2015)CrossRef
38.
D’Andreagiovanni, F., Krolikowski, J., Pulaj, J.: A fast hybrid primal heuristic for multiband robust capacitated network design with multiple time periods. Appl. Soft Comput. 26, 497–507 (2015)CrossRef
39.
Doar, M., Leslie, I.: How bad is naive multicast routing. In: Twelfth Annual Joint Conference of the IEEE Computer and Communications Societies. Networking: Foundation for the Future, INFOCOM 1993, Proceedings, pp. 82–89. IEEE (1993)
40.
Oh, J., Pyo, I., Pedram, M.: Constructing minimal spanning/steiner trees with bounded path length. In: European Design and Test Conference, pp. 244–249 (1996)
41.
Marinakis, Y., Marinaki, M., Matsatsinis, M.: A hybrid discrete artificial bee colony - grasp algorithm for clustering. In: 39th International Conference on Computers and Industrial Engineering, Troyes, France (2009)
42.
43.
Hansen, P., Mladenovic, N.: Variable neighborhood search: principles and applications. Eu. J. Oper. Res. 130, 449–467 (2001)MathSciNetCrossRefMATH
44.
Blum, C., Puchinger, J., Raidl, G.R., Roli, A.: Hybrid metaheuristics in combinatorial optimization: a survey. Appl. Soft Comput. 11, 4135–4151 (2011)CrossRefMATH
45.
Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Proceedings of 1998 IEEE World Congress on Computational Intelligence, pp. 69–73 (1998)
46.
Hansen, P., Mladenović, N., Moreno-Pérez, J.A.: Variable neighborhood search: methods and applications. Ann. Oper. Res. 175, 367–407 (2010)MathSciNetCrossRefMATH
Metadaten
Titel
A Hybrid Discrete Artificial Bee Colony Algorithm for the Multicast Routing Problem
verfasst von
Yannis Marinakis
Magdalene Marinaki
Athanasios Migdalas
Copyright-Jahr
2016
Buch
Applications of Evolutionary Computation

Print ISBN: 978-3-319-31203-3

Electronic ISBN: 978-3-319-31204-0

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-31204-0_14

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