Abstract
In this work, we tackle a real-world telecommunication problem by using Evolutionary Computation and Multiobjective Optimization jointly. This problem is known in the literature as the Traffic Grooming problem and consists on multiplexing or grooming a set of low-speed traffic requests (Mbps) onto high-speed channels (Gbps) over an optical network with wavelength division multiplexing facility. We propose a multiobjective version of an algorithm based on the laws of motions and mass interactions (Gravitational Search Algorithm, GSA) for solving this NP-hard optimization problem. After carrying out several comparisons with other approaches published in the literature for this optical problem, we can conclude that the multiobjective GSA (MO-GSA) is able to obtain very promising results.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Arroyo, J., Vieira, P., Vianna, D.: A grasp algorithm for the multi-criteria minimum spanning tree problem. Annals of Operations Research 159, 125–133 (2008)
De, T., Pal, A., Sengupta, I.: Traffic Grooming, Routing, and Wavelength Assignment in an Optical WDM Mesh Networks Based on Clique Partitioning. Photonic Network Communications 20, 101–112 (2010)
Deb, K.: Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Inc., New York (2001)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast Elitist Multi-Objective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6, 182–197 (2000)
Gagnaire, M., Koubaa, M., Puech, N.: Network Dimensioning under Scheduled and Random Lightpath Demands in All-Optical WDM Networks. IEEE Journal on Selected Areas in Communications 25(S-9), 58–67 (2007)
Gong, Y.J., Zhang, J., Liu, O., Huang, R.Z., Chung, H.H., Shi, Y.H.: Optimizing the Vehicle Routing Problem With Time Windows: A Discrete Particle Swarm Optimization Approach. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 42(2), 254–267 (2012)
Prathombutr, P., Stach, J., Park, E.K.: An Algorithm for Traffic Grooming in WDM Optical Mesh Networks with Multiple Objectives. Telecommunication Systems 28, 369–386 (2005)
Rashedi, E., Nezamabadi-pour, H., Saryazdi, S.: GSA: A Gravitational Search Algorithm. Information Sciences 179(13), 2232–2248 (2009), Special Section on High Order Fuzzy Sets
Rubio-Largo, A., Vega-Rodríguez, M.A., Gomez-Pulido, J.A., Sanchez-Perez, J.M.: Multiobjective Metaheuristics for Traffic Grooming in Optical Networks. IEEE Transactions on Evolutionary Computation (available online since June 2012), 1–17 (2012)
Xue, F., Sanderson, A., Graves, R.: Multiobjective Evolutionary Decision Support for Design Supplier Manufacturing Planning. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans 39(2), 309–320 (2009)
Zhu, H., Zang, H., Zhu, K., Mukherjee, B.: A Novel Generic Graph Model for Traffic Grooming in Heterogeneous WDM Mesh Networks. IEEE/ACM Transaction on Networking 11, 285–299 (2003)
Zhu, K., Mukherjee, B.: A Review of Traffic Grooming in WDM Optical Networks: Architectures and Challenges. Optical Networks Magazine 4(2), 55–64 (2003)
Zhu, K., Mukherjee, B.: Traffic Grooming in an Optical WDM Mesh Network. IEEE Journal on Selected Areas in Communications 20(1), 122–133 (2002)
Zitzler, E., Deb, K., Thiele, L.: Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation 8, 173–195 (2000)
Zitzler, E., Thiele, L.: Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Transactions on Evolutionary Computation 3(4), 257–271 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rubio-Largo, Á., Vega-Rodríguez, M.A. (2013). A Multiobjective Approach Based on the Law of Gravity and Mass Interactions for Optimizing Networks. In: Middendorf, M., Blum, C. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2013. Lecture Notes in Computer Science, vol 7832. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37198-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-37198-1_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37197-4
Online ISBN: 978-3-642-37198-1
eBook Packages: Computer ScienceComputer Science (R0)