Genetic algorithm based approach for designing computer network topology

Kumar, Anup; Pathak, Rakesh M.; Gupta, Mahesh

doi:10.1145/170791.170871

Cited by 14 publications

(8 citation statements)

References 13 publications

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“…There are three steps to calculate the availability: 1) Availability of the links, A l : The value is calculated using Formulas (1) and (2).…”

Section: A Limitation Parametersmentioning

confidence: 99%

“…Once the number of nodes increases (n > 20), optimization of the network characteristics (total length, maximum link length, availability, budget, etc) for, even, a known topology can be challenging for any human or dedicated algorithms except perhaps for generalized heuristics algorithms such as EAs/GAs where the objective is optimization of the characteristics of these topologies even thought that there is no guarantee that the best solution can be found, nevertheless the guarantee is that after many runs better solutions can be found [1] and [2]. …”

Section: A Genetic Algorithm Introductionmentioning

confidence: 99%

“…The GA had to calculate all the paths for every pair of nodes to calculate the diameter, which makes the GA´s convergence to an optimized solution more difficult and slower [2].…”

mentioning

confidence: 99%

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Network Planning Using GA For Regular Topologies

Lopez

Imine

Madsen

2008

2008 IEEE International Conference on Communications

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Abstract-Evolutionary Algorithms (EAs) and Genetic Algorithms (GAs) have successfully been applied to solve constrained problems in network design. Network systems are being designed with fiber optic, as the users' requirements are growing all the time. Selecting a suitable depiction of candidate solutions to the problem at hand is the crucial issue for applying GAs. Well defined network topologies, such as Single Ring, Double Ring and Grid, have been intensively scrutinized. As the number of nodes increases, optimization of the network for a given topology can be challenging for any human or specialized algorithm. For EAs/GAs where the objective is optimization of some characteristics even thought that there is no guaranty that the best optimized solution, better solutions after many runs can be found. These solutions are getting near the optimal one at each time until reaching some satisfaction.

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“…There are three steps to calculate the availability: 1) Availability of the links, A l : The value is calculated using Formulas (1) and (2).…”

Section: A Limitation Parametersmentioning

confidence: 99%

Section: A Genetic Algorithm Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Network Planning Using GA For Regular Topologies

Lopez

Imine

Madsen

2008

2008 IEEE International Conference on Communications

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“…In the area of network systems, many literatures about GAs' applications appeared to prove their feasibility about optimization problem. As we all know, the topological design of networks [6] is a very complicated combinatorial optimization problem, which can be classified as NP-complete. Reuven Elbaum and Moshe Sidi [2] successfully solve this problem of Local-Area networks by using GAs.…”

Section: Introductionmentioning

confidence: 99%

Genetic algorithm-based dynamic reconfiguration for networked control system

Zhou

Xiang

Chen

et al. 2007

Neural Comput & Applic

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This paper represents a genetic algorithm (GA) based dynamic reconfiguration for networked control systems (NCS) with the objective of minimizing network time-delay. With the development of NCS, it is become more and more important for them to have the minimum time-delay and the ability of dynamic reconfiguration, which can accommodate the changes rapidly, smartly and flexibly. And it is important to find a routing algorithm, which is quicker to reduce the time to update the router and decrease the reconfiguration time as much as possible. In this paper, based on NCS, we discuss the process of GA with specialized encoding, initialization, selection, crossover and mutation. A specialized repair function is used to improve performance. In addition, experiment results are given to illuminate that GA can improve the performance of the NCS.

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“…Previous research has addressed topology design for macro networks [3,5,9,6] and bottom-up communication synthesis from a fine-grained IP library for a given block placement [8]. We describe the methodologies used by a design automation tool for effective network-on-chip topology optimization.…”

Section: Introductionmentioning

confidence: 99%

Topology optimization for application-specific networks-on-chip

Ahonen

Sigüenza-Tortosa

Hong

et al. 2004

Proceedings of the 2004 International Workshop on System Level Interconnect Prediction

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Compared to the well understood macro networks, networks-onchip introduce novel design challenges. The characteristics of the system data flows and the knowledge of the required wire lengths can be exploited to optimize for speed and power consumption. A component library for flexible construction of interconnection architectures is being developed at the Tampere University of Technology to enable the creation of application development platforms. The overall design flow of these development platforms is reviewed in this paper. Network-on-chip topology optimization is addressed by describing the methodologies used by an effective design automation tool. The detailed cost functions of the tool capture the factors contributing to the speed and power consumption of asynchronous interconnections, while different abstraction level input information is supported. A case study into the application domain of industrial process control and monitoring is presented in order to evaluate the result quality.

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Genetic algorithm based approach for designing computer network topology

Cited by 14 publications

References 13 publications

Network Planning Using GA For Regular Topologies

Network Planning Using GA For Regular Topologies

Genetic algorithm-based dynamic reconfiguration for networked control system

Topology optimization for application-specific networks-on-chip

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