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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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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