A tree-growth based ant colony algorithm for QoS multicast routing problem

Wang, Hua; Hong, Xu; Shen, Yi; Zhao, Shengzhi

doi:10.1016/j.eswa.2011.03.065

Cited by 41 publications

(15 citation statements)

References 22 publications

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“…In our algorithm, ants walk through logical links of the network topology graph and build data distribution trees for individual data streams as its subgraphs. A similar approach was adopted in the TGBACA algorithm [22] for multicast QoS routing of a single group. We use the ants not only to build the tree as the TGBACA does, but also to decide formats for data transfer via each logical link.…”

Section: Algorithm and Implementationmentioning

confidence: 99%

“…Some authors used problem specific heuristic, typically based on merging of least-cost paths into a tree [20]. Most of the approaches rely on computational intelligence algorithms, namely genetic algorithms [21], ACO [22], or quantum particle swarm optimization [23]. The MMDOM algorithm [24] minimizes latency of multicast tree considering both link latencies and delays in queues on the network nodes.…”

Section: B Related Problemsmentioning

confidence: 99%

“…The pheromone update (Ln. [17][18][19][20][21][22], running in O( L L · S ), can be run concurrently for several streams. Because of strict requirements on interactive response, we leave the main loop after exceeding a given time limit.…”

Section: B Main Algorithm Loopmentioning

confidence: 99%

See 2 more Smart Citations

Media Streams Planning with Uncertain Link Capacities

Troubil

Rudová

Holub

2014

2014 IEEE 13th International Symposium on Network Computing and Applications

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Interactive collaborative environments built upon high quality video applications require minimum capture-todisplay latency to maintain feeling of natural communication. In practice, end users often encounter interruptions due to network congestion, which they could not avoid without knowledge of physical network infrastructure. We solve the problem of application level multipoint routing without assuming accurate knowledge of network link capacities, but rather relying on their estimates by network inference and bandwidth measurement tools. Our approach can handle uncertainties caused by both unreliable bandwidth measurements and unexpected capacity sharing. Support for on-the-fly transcoding of data streams on multicast agents allows per-recipient adaptation of video quality and bandwidth. We have implemented an ant colony optimization algorithm in the CoUniverse middleware for orchestration of collaborative environments. Experiments show very good quality of results on most practical use cases and ability to solve significantly larger instances than optimal approaches based on mixed integer programming.

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Section: Algorithm and Implementationmentioning

confidence: 99%

Section: B Related Problemsmentioning

confidence: 99%

See 1 more Smart Citation

Media Streams Planning with Uncertain Link Capacities

Troubil

Rudová

Holub

2014

2014 IEEE 13th International Symposium on Network Computing and Applications

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“…Objective function f(e) need to minimize in order to provide good QoS (Quality of Service) [13] [14] within the network proposed. QoS ensures the resource reservation control mechanisms and service quality by maintaining high bit rate, low latency and low bit error probability.…”

Section: Bit Error Rate (Ber)mentioning

confidence: 99%

Application of Modified Ant Colony Optimization (MACO) for Multicast Routing Problem

Sahana¹,

Al-Fayoumi²,

Mahanti

2016

IJISA

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Abstract-It is well known that multicast routing is combinatorial problem finds the optimal path between source destination pairs. Traditional approaches solve this problem by establishment of the spanning tree for the network which is mapped as an undirected weighted graph. This paper proposes a Modified Ant Colony Optimization (MACO) algorithm which is based on Ant Colony System (ACS) with some modification in the configuration of starting movement and in local updation technique to overcome the basic limitations of ACS such as poor initialization and slow convergence rate. It is shown that the proposed Modified Ant Colony Optimization (MACO) shows better convergence speed and consumes less time than the conventional ACS to achieve the desired solution.

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“…For the advantages of not depending on mathematics description of definite problems, excellent capacity on global optimization, better performance on reliability than early genetic algorithm and annealing simulation algorithm, little workload, and easy to realization, it has been paid more attentions to solve discrete problems, such as combination optimization and modification consistency of judgment matrix [2,3,4].…”

Section: Introductionmentioning

confidence: 99%

Cultural Ant Algorithm for Continuous Optimization Problems

Xu¹,

Zhang²,

Cai³

2013

Appl. Math. Inf. Sci.

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In order to overcome prematurity of ant colony algorithm, the conception of belief space originated in cultural algorithm is introduced, and a new cultural ant algorithm is proposed for continuous optimization problems. Firstly, the coding scheme for ant colony algorithm to solve continuous optimization problems is discussed. Then belief space is brought in, and designed as the form of two parts: individual belief space and population belief space. The former is used to conduct individuals' deep search for better solutions, and the other to help worse individuals drop their current bad solution space for broad search. The update rules of both population space and belief space are given subsequently. Eight common standard functions are used to test the new algorithm, which is compared with four other algorithms at the same time. The results show effectiveness and superiority of the new algorithm. Finally the effect of the parameter used in the algorithm is discussed, and so does the both two belief space.

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A tree-growth based ant colony algorithm for QoS multicast routing problem

Cited by 41 publications

References 22 publications

Media Streams Planning with Uncertain Link Capacities

Media Streams Planning with Uncertain Link Capacities

Application of Modified Ant Colony Optimization (MACO) for Multicast Routing Problem

Cultural Ant Algorithm for Continuous Optimization Problems

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