An orthogonal genetic algorithm for multimedia multicast routing

Many multimedia communication applications require a source to send multimedia information to multiple destinations through a communication network. To support these applications, it is necessary to determine a multicast tree of minimal cost to connect the source node to the destination nodes subjec...

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Vydáno v:IEEE transactions on evolutionary computation Ročník 3; číslo 1; s. 53 - 62
Hlavní autoři: Zhang, Qingfu, Leung, Yiu-Wing
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, NY IEEE 01.04.1999
Institute of Electrical and Electronics Engineers
Témata:
Algorithmics. Computability. Computer arithmetics
Applied sciences
Communication networks
Computer networks
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Costs
Delay
Exact sciences and technology
Genetic algorithms
Heuristic algorithms
Information retrieval. Graph
Multicast
Multicast algorithms
Multimedia
Multimedia communication
Optimization
Routing
Routing (telecommunications)
Software
Streaming media
Theoretical computing
Videoconference
Multimedia
System performance
Computer network
Genetic algorithm
Routing
Graph theory
Distributed system
ISSN:1089-778X
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Shrnutí:Many multimedia communication applications require a source to send multimedia information to multiple destinations through a communication network. To support these applications, it is necessary to determine a multicast tree of minimal cost to connect the source node to the destination nodes subject to delay constraints on multimedia communication. This problem is known as multimedia multicast routing and has been proved to be NP-complete. The paper proposes an orthogonal genetic algorithm for multimedia multicast routing. Its salient feature is to incorporate an experimental design method called orthogonal design into the crossover operation. As a result, it can search the solution space in a statistically sound manner and it is well suited for parallel implementation and execution. We execute the orthogonal genetic algorithm to solve two sets of benchmark test problems. The results indicate that for practical problem sizes, the orthogonal genetic algorithm can find near optimal solutions within moderate numbers of generations.
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ISSN:1089-778X
DOI:10.1109/4235.752920