Efficient optimization of all-terminal reliable networks, using an evolutionary approach

Dengız, Berna; Altıparmak, Fulya; Smith, Alice E.

doi:10.1109/24.589921

Cited by 125 publications

(70 citation statements)

References 27 publications

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“…Evolutionary Algorithms (EAs) and in particular Genetic Algorithm (GA) have successfully been applied to solve constrained problems with multi-objectives, such as transportation problems [7], production process planning problems [8] and network topology design problems [1]- [3], [9] and [10]. EAs/GAs were investigated for several kinds of encoding methods [8] where most of them can not effectively encode/decode without getting some infeasible solutions that require some repair before being considered.…”

Section: A Genetic Algorithm Introductionmentioning

confidence: 99%

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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Section: A Genetic Algorithm Introductionmentioning

confidence: 99%

Network Planning Using GA For Regular Topologies

Lopez

Imine

Madsen

2008

2008 IEEE International Conference on Communications

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“…An introduction to GA may be found in [9]. In [10] it is proposed how GA may be used for finding (near) optimal all-terminal network topology considering cost and reliability. A variablelength chromosome and population sizing are presented in [11] for shortest path routing.…”

Section: Introductionmentioning

confidence: 99%

“…A variablelength chromosome and population sizing are presented in [11] for shortest path routing. Both [10] and [11] pinpoint the challenges for efficient fitness function and control parameters. It is possible to identify the optimal GA control parameters for the desired optimization, but this may itself be a time demanding task [12].…”

Section: Introductionmentioning

confidence: 99%

Using genetic algorithms to improve the reliability of dual homed wireless critical services

Folstad

Helvik

2014

2014 6th International Workshop on Reliable Networks Design and Modeling (RNDM)

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Abstract-The wireless access to any service in different contexts is nowadays taken for granted. However, the dependability requirements are different for various services and contexts. Critical services put high requirement on the service reliability, i.e., the probability of no service interruption should be close to one. Dual homing may be used to increase the service reliability in a multi technology, multi operator wireless environment, where the user's mobility necessitates access point selections and handovers. To allow the user to assess the risk of the service session, a prediction of the service reliability is necessary. This prediction must fulfil the need for the optimal sequence of access point selections and handovers with regard to service reliability and being computation efficient to accomplish the need for the real-time operation. We demonstrate how genetic algorithms (GA) may be used to predict and to improve the (near) optimal service reliability by fast and simple heuristics, far more computationally efficient than an Integer Linear Programming (ILP) optimization.

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“…The approach in which there is a need to maintain connections between all network nodes was analysed in [2,3]. Networks where only k nodes must be connected where considered in [4,5,6,7].…”

Section: Introductionmentioning

confidence: 99%

Reliability and cost optimisation of complex electric power networks using ant colony algorithm

Piątek

2017

ITM Web Conf.

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Abstract. The article presents a new approach towards reducing an overall cost of electric power network with maintaining its reliability. Goals are achieved by implementing an ant colony algorithm with a cut-set method as a method for reliability evaluation. The algorithm solves the problem of multi-objective optimisation, where both the network cost and network reliability index, known as unavailability, should be minimalised. The network cost is considered as a linear function of overall length of network's connections. For reliability evaluation in the cut-set method, real empiric data of hazard rate for overhead power lines are used. Parallel-series network structure, equivalent by means of reliability to analysed network, is generated through the cut-set method to compute unavailability of trial solutions. Sections of the structure are generated on the basis of minimum cut set, found by the algorithm for finding one-and two-minimum cuts. As used algorithm for finding minimum cuts has linear complexity, the evaluation of trial solutions is computationally effective. An example, presented in this article, provides figure of optimal network configurations found by the algorithm.

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Efficient optimization of all-terminal reliable networks, using an evolutionary approach

Cited by 125 publications

References 27 publications

Network Planning Using GA For Regular Topologies

Network Planning Using GA For Regular Topologies

Using genetic algorithms to improve the reliability of dual homed wireless critical services

Reliability and cost optimisation of complex electric power networks using ant colony algorithm

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