QoS-driven multicast tree generation using Tabu search

Youssef, Habib; Al-Mulhem, Abdulaziz; Sait, Sadiq M.; Tahir, Muhammad Atif

doi:10.1016/s0140-3664(02)00029-4

Cited by 40 publications

(20 citation statements)

References 7 publications

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“…Several tabu search algorithms have been developed, where Dijkstra's algorithm has been widely used as the initialization method (Youssef et al, 2002;SkorinKapov and Kos, 2006;Ghaboosi and Haghighat, 2007a). Wang et al (2004) find that the main disadvantage in the tabu mechanism is that the randomly selected paths often lead to a disjointed multicast tree.…”

Section: Heuristics and Meta-heuristics For Dclc Multicast Routing Prmentioning

confidence: 99%

Particle swarm optimization for the Steiner tree in graph and delay-constrained multicast routing problems

Castro

et al. 2012

J Heuristics

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This paper presents the first investigation on applying a Particle Swarm Optimization (PSO) algorithm to both the Steiner tree problem and the delay constrained multicast routing problem. Steiner tree problems, being the underlining models of many applications, have received significant research attention within the meta-heuristics community. The literature on the application of meta-heuristics to multicast routing problems is less extensive but includes several promising approaches. Many interesting research issues still remain to be investigated, for example, the inclusion of different constraints, such as delay bounds, when finding multicast trees with minimum cost. In this paper, we develop a novel PSO algorithm based on the jumping PSO (JPSO) algorithm recently developed by Moreno-Perez et al. (2007), and also propose two novel local search heuristics within our PSO framework. A path replacement operator has been used in particle moves to improve the positions of the particle with regard to the structure of the tree. We test the performance of our PSO algorithm, and the effect of the integrated local search heuristics by an extensive set of experiments on multicast routing benchmark problems and Steiner tree problems from the OR library. The experimental results show the superior performance of the proposed PSO algorithm over a number of other state-of-the-art approaches.

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Section: Heuristics and Meta-heuristics For Dclc Multicast Routing Prmentioning

confidence: 99%

Particle swarm optimization for the Steiner tree in graph and delay-constrained multicast routing problems

Castro

et al. 2012

J Heuristics

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“…In recent years, meta-heuristic algorithms such as simulated annealing [21,22], genetic algorithm [23,24], tabu search [25][26][27][28], GRASP [9,10], path relinking [29], VNS [12], scatter search [30] and particle swarm optimization [31] have been investigated for various multicast routing problems. A GRASP meta-heuristic hybridized with VNDMR [12], namely GRASP-VND, is proposed in our previous work in [10], which has showed good performance for the solving the DCLC multicast routing problem.…”

Section: B Related Workmentioning

confidence: 99%

A new hybrid GRASP with the pilot method for the delay-constrained multicast routing problem

Xu¹,

Zheng²,

Li³

2012

Proceedings of the 2012 2nd International Conference on Computer and Information Applications (ICCIA 2012)

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Abstract-Multicast routing problem is a well know optimization problem for transmitting real-time multimedia applications in telecommunication networks. As the underpinning mathematical model, the constrained minimum Steiner tree problem in graphs is a well-known NP-complete problem. In this paper we investigate a new hybrid GRASP (Greedy Randomized Adaptive Search Procedure) approach where a pilot method is applied to further enhance the search for the Delay-Constrained Least-Cost (DCLC) multicast routing problem. Experimental results demonstrate the efficiency of the hybrid GRASP algorithm and the contributions of the post-processing pilot method to better solutions in most cases. The proposed GRASP approach is a competitive approach in solving the DCLC multicast routing problem.

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“…Since the constrained multicast routing problem is known to be NP-complete [37], many heuristic techniques, such as tabu search [38][39][40], ant colony optimization [41,42], genetic algorithms [43][44][45], and fuzzy-based algorithms [46,47], have been also devised for solving this problem. The heuristic methods have been found to perform well for solving the multicast routing problem in Ad-hoc environments with multiple constraints.…”

Section: Overview Of Multicast Protocolsmentioning

confidence: 99%

Mobility-based multicast routing algorithm for wireless mobile Ad-hoc networks: A learning automata approach

Torkestani

Meybodi

2010

Computer Communications

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In this paper, we propose a distributed learning automata-based algorithm to solve the multicast routing problem in wireless mobile Ad-hoc networks. The proposed algorithm called MMR-LA estimates the expected relative mobility of each host, by sampling its movement parameters in various epochs, to realistically predict its motion behavior, and takes advantage of the Steiner connected dominating set to form the virtual multicast backbone. To do this, a stochastic version of the minimum Steiner connected dominating set problem in weighted network graphs, where the relative mobility of each host is considered as its weight is introduced. Then, a distributed learning automata-based algorithm is designed to solve this problem. The designed algorithm is proposed for multicast routing in wireless mobile Ad-hoc networks. The experiments show the superiority of the proposed multicast routing algorithm over the existing methods in terms of the packet delivery ratio, multicast route lifetime, and end-to-end delay. We present a strong convergence theorem in which the convergence of the proposed distributed learning automata-based algorithm to the optimal solution is proved. It is shown that the most stable multicast route is found with a probability as close as to unity by the proper choice of the parameters of the distributed learning automata.

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QoS-driven multicast tree generation using Tabu search

Cited by 40 publications

References 7 publications

Particle swarm optimization for the Steiner tree in graph and delay-constrained multicast routing problems

Particle swarm optimization for the Steiner tree in graph and delay-constrained multicast routing problems

A new hybrid GRASP with the pilot method for the delay-constrained multicast routing problem

Mobility-based multicast routing algorithm for wireless mobile Ad-hoc networks: A learning automata approach

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