Genetic local search for multicast routing with pre-processing by logarithmic simulated annealing

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 minimisation of multicast routing. The algorithm utilises 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 analysis employs simulated annealing with a logarithmic cooling schedule (logarithmic simulated annealing-LSA). 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 three different routing tasks, and we provide experimental evidence that our genetic local search procedure that combines LSA and PMX performs better than algorithms using either LSA or PMX only. ᭧

A simulated annealing based genetic local search algorithm for multi-objective multicast routing problems

“…Over the past decade, the problem has attracted increasing attention from the meta-heuristic research community in both computer communications and operational research (Diot et al 1997;Yeo et al 2004;Oliveira et al 2005). A large amount of investigations on meta-heuristic algorithms exist in the literature (Haghighat et al 2004;Kun et al 2005;Skorin-Kapov and Kos 2006;Zahrani et al 2008;Qu et al 2009). However, at the early stage, the MRPs have been mainly defined and solved as a singleobjective optimisation problem subject to certain QoS constraints, i.e.…”

An iterative local search approach based on fitness landscapes analysis for the delay-constrained multicast routing problem

“…The results show that the difference depends mainly on the cost function and the capacity constraint, while only slightly on the particular network structure. Zahrani et al have further extended their work in [10], by introducing a LSA based genetic local search (GLS) algorithm for the landscape analysis on the group multicast routing problem. The GLS algorithm applies the partial mixed crossover (PMX) operation to pairs of individuals.…”

“…As far as we know, there are only three related work [10,21,22] on the landscape analysis for a different type of MRP, namely the group MRP, where a sequence of multicast requests is scheduled. In [21], a logarithmic simulated annealing (LSA) is used to perform detailed landscape analysis on three instances.…”

“…Experimental results on two benchmark instances show that the proposed LSA-based GLS together with the PMX operation outperforms two variants of GLS algorithms with either LSA or PMX only. The same GLA algorithm with a LSA as the pre-processing step is applied by Zahrani et al in [10] on more instances for the group multicast routing problem with the capacity constraint. Similarly, the LSA is also used to estimate the value of maximum depth of local optima in the landscape.…”

“…the Kompella-Pasquale-Polyzos (KPP) heuristic [4], has been developed in the early 1990"s, various heuristic and meta-heuristic algorithms have been proposed for solving a wide range of MRPs with different constraints. The majority of state-of-art algorithms for the DCLC-MRP are based on meta-heuristics such as simulated annealing [5,6], tabu search [7,8], genetic algorithms [9,10], greedy random adaptive search procedure (GRASP) [11,12], variable neighborhood search [13], and Scatter Search [14] etc. The rich literature of optimization algorithms for various MRPs can be found in several surveys [15][16][17][18][19].…”