A tree-based particle swarm optimization for multicast routing

Wang, Hua; Meng, Xiangxu; Li, Shuai; Hong, Xu

doi:10.1016/j.comnet.2010.05.006

Cited by 41 publications

(20 citation statements)

References 16 publications

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“…Inappropriate parameter settings may lead to the unexpected way. Figure 5 suggests that the Steiner tree cost by employing new-EDA is close to those of PSOTREE algorithm [42] on all the test topologies, and it is much better than those of ACO algorithm. On the other hand, the convergence time of adopting new-EDA is faster than the other two algorithms shown in Figure 6 especially with the increment of network size.…”

Section: The Influence Of the Initial Population Sizementioning

confidence: 49%

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An improved EDA for solving Steiner tree problem

Liu

Wang

Kong

2015

Concurrency and Computation

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SUMMARYSteiner tree problem and its derivations are widely employed to optimize the design of transportation, communication networks, biological engineering and the QoS multicast routing problem. It is one of well-defined open issues which have attracted many research efforts. Different from the existing works, this paper develops a new method of solving Steiner tree problem by using the improved estimation of distribution algorithms (EDA). Further, the performance of developed method is validated by applying on multicast routing optimization. The developed method randomly initializes n trees which contain the source node and the destination nodes. And some individuals select the crossover operation randomly to add the population diversity and avoid the algorithm premature convergence. The algorithm constructs a probabilistic model according to the selected elites, which is capable of estimating the probability distribution of the solution. The probabilistic model is updated according to the new population. New trees are generated based on the probabilistic model. This process iterated until designated termination criteria are met. The improved EDA algorithm gradually evolves trees to obtain a better solution. Simulation validations suggest that the developed method leads to better performance. In particular, the complexity in terms of the converging speed improves significantly compared to other algorithms.

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Section: The Influence Of the Initial Population Sizementioning

confidence: 49%

“…T temp is the result of the merging operation. It needs elimination and prune operation [42]. In the elimination operation, we select a simple method eliminating the maximum cost value in the circle.…”

Section: Crossover Operationmentioning

confidence: 99%

An improved EDA for solving Steiner tree problem

Liu

Wang

Kong

2015

Concurrency and Computation

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“…In these studies, both uni-rate and multi-rate techniques have been employed for data transmission. The focus of the uni-rate multicasting algorithms in Liu and Liao (2010), Ruiz and GomezSkarmeta (2005), Wang et al (2010) and Zhang et al (2009) is on minimizing the amount of bandwidth usage. Some studies, such as Wang et al (2010) and Zhang et al (2009), have also guaranteed QoS requirements of applications.…”

Section: Related Workmentioning

confidence: 99%

“…The focus of the uni-rate multicasting algorithms in Liu and Liao (2010), Ruiz and GomezSkarmeta (2005), Wang et al (2010) and Zhang et al (2009) is on minimizing the amount of bandwidth usage. Some studies, such as Wang et al (2010) and Zhang et al (2009), have also guaranteed QoS requirements of applications. Roy et al (2008), Zhao et al (2007), Koutsonikolas et al (2012) and Zhao et al (2011) considered the quality of wireless links to provide high throughput multicast routing metrics.…”

Section: Related Workmentioning

confidence: 99%

Multi-rate multicast routing in multi-gateway multi-channel multi-radio wireless mesh networks

Farzinvash

Dehghan

2014

Journal of Network and Computer Applications

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“…Several articles have been presented in this area. [1]The presented algorithm optimizes multicast tree directly. This algorithm uses PSO as a solution to control the tree frame optimization position.…”

Section: Introductionmentioning

confidence: 99%

Multicast Routing in Computer Networks considering Quality of Service (QoS) based on Honey Bee Algorithm

TayebTaher¹,

Masoudrahmani²

2012

IJCA

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The main purpose of multicast routing is minimizing the sources relationship by using multicast structure and the purpose will prepare the routing by minimizing the tree's cost. The Steiner tree's issue is to find a tree the least cost in the network in a way that total weights of the tree should be minimized and connect one or more sources to one or more destinations. The weights which are applied to the edge of the tree could include one or more limitations of quality of service such as bandwidth, delay and cost. It is improved that finding such a tree in the network is a NP-COMPLETE problem. So, a real answer can't be find for that and approximate and discovering solutions are proposed for that. In this paper a suitable answer for mentioned NP-COMPLETE problem is found by presenting a new algorithm according to bee algorithm. Bee algorithm is a collective smart searching algorithm for optimizing the solution of problem. The applied results show that the proposed algorithm with regard to genetic algorithm and practice swarm optimization of Steiner tree with more suitable application percent computers the relation among bandwidth, delay and cost. General Terms multicast routing algorithmKeywords multicast routing, multicast tree, quality of service, Bee algorithm

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A tree-based particle swarm optimization for multicast routing

Cited by 41 publications

References 16 publications

An improved EDA for solving Steiner tree problem

An improved EDA for solving Steiner tree problem

Multi-rate multicast routing in multi-gateway multi-channel multi-radio wireless mesh networks

Multicast Routing in Computer Networks considering Quality of Service (QoS) based on Honey Bee Algorithm

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