Abstract
Over the past few years, several local search algorithms have been proposed for various problems related to multicast routing in the off-line mode. We describe a population-based search algorithm for cost minimization of multicast routing. The algorithm utilizes the partially mixed crossover operation (PMX) under the elitist model: for each element of the current population, the local search is based upon the results of a landscape analysis that is executed only once in a pre-processing step; the best solution found so far is always part of the population. The aim of the landscape analysis is to estimate the depth of the deepest local minima in the landscape generated by the routing tasks and the objective function. The local search then performs alternating sequences of descending and ascending steps for each individual of the population, where the length of a sequence with uniform direction is controlled by the estimated value of the maximum depth of local minima. We present results from computational experiments on two different routing tasks, and we provide experimental evidence that our genetic local search procedure performs better than algorithms using either Simulated Annealing or PMX only.
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References
E. Aarts. Local Search in Combinatorial Optimization. Wiley & Sons, New York, 1998.
J. Beasley. OR library: http://people.brunel.ac.uk/~ mastjjb/jeb/info.html.
V. Cerny. A thermodynamical approach to the travelling salesman problem: An efficient simulation algorithm. Journal of Optimization Theory and Applications, 45:41–51, 1985.
D. Chakraborty, G. Chakraborty, and N. Shiratori. A dynamic multicast routing satisfying multiple QoS constraints. International Journal of Network Management, 13:321–335, 2003.
C. Diot, W. Dabbous, and J. Crowcroft. Multipoint communication: A survey of protocols, functions, and mechanisms. IEEE Journal on Selected Areas in Communication, 15:277–290, 1997.
M. Doar and I. Leslie. How bad is naïve multicast routing? In Proc. of IEEE INFOCOM’93, pp. 82–89, 1993.
P. Galiasso and R. Wainwright. A hybrid genetic algorithm for the point to multipoint routing problem with single split paths. In Proc. of ACM Symposium on Applied Computing (2001), pages 327–332, 2001.
D. Goldberg. Genetic Algorithms in Search. Addison-Wesley Publishing Co., Reading, MA, 1989.
B. Hajek. Cooling schedules for optimal annealing. Mathematics of Operations Research, 13:311–329, 1988.
S. Hakimi. Steiner’s problem in graphs and its implications. Networks, 1:113–133, 1971.
T. Harrison and C. Williamson. A performance study of multicast routing algorithms for ATM networks. In Proc. of 21 st Annual IEEE Conference on Local Computer Networks, p. 191, 1996.
J. Holland. Genetic algorithms. Scientific American, 267(1):66–72, 1992.
P. Kampstra. Evolutionary computing in telecommu-nications. In BMI paper (Vrije U. Amsterdam), 2005.
R. Karp. Reducibility among combinatorial problems. In Complexity of Computer Computations, pp. 85–103. Plenum Press, 1972.
S. Kirkpatrick, C. G. Jr., and M. Vecchi. Optimization by simulated annealing. Science, 220:671–680, 1983.
L. Kou, G. Markowsky, L. Berman. A fast algorithm for Steiner trees. Acta Informatica, 15:141–145, 1981.
P. Merz and B. Freisleben. Fitness landscapes, même-tic algorithms, and greedy operators for graph bipar-titioning. Evolutionary Computation, 8(1):61–81, 2000.
C. Oliveira and P. Pardalos. A survey of combinatorial optimization problems in multicast routing. Comput. & Operations Research, 32:1953–1981, 2005.
H. Prömel, A. Steger. A new approximation algorithm for the Steiner tree problem with performance ratio 5/3. Journal of Algorithms, 36:89–101, 2000.
G. Rudolph. Convergence analysis of canonical genetic algorithms. IEEE Transactions on Neural Networks, 5:96–101, 1994.
H. Salama, D. Reeves, and Y. Viniotis. Evaluation of multicast routing algorithms for real-time communi-cation on high-speed networks. IEEE Journal on Selected Areas in Communications, 13:332–345, 1997.
L. Schmitt. Theory of genetic algorithms. Theoretical Computer Science, 259:1–61, 2001.
M. Wolfinger, W. Svrcek-Seiler, C. Flamm, I. Hofacker, and P. Stadler. Exactfolding dynamics of RNA secondary structures. Journal of Physics A: Mathematics and General, 37:4731–4741, 2004.
C. Yeo, B. Lee, and M. Er. A framework for multicast video streaming over IP networks. Journal of Network and Computer Applications, 26:273–289, 2003.
L. Zhu, R. Wainwright, and D. Schoenefeld. A genetic algorithm for the point to multipoint routing problem with varying number of requests. In Proc. of IEEE International Conference on Evolutionary Computation, pp. 171–176, 1998.
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Zahrani, M.S., Malcolm, J.A., Loomes, M.J., Albrecht, A.A. (2007). LSA-based Landscape Analysis for Multicast Routing. In: Bramer, M., Coenen, F., Tuson, A. (eds) Research and Development in Intelligent Systems XXIII. SGAI 2006. Springer, London. https://doi.org/10.1007/978-1-84628-663-6_14
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DOI: https://doi.org/10.1007/978-1-84628-663-6_14
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