Applying an evolutionary algorithm to telecommunication network design

Arabas, Jarosław; Kozdrowski, Stanisław

doi:10.1109/4235.942526

Cited by 39 publications

(16 citation statements)

References 9 publications

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“…Selection rules are used to choose parents, crossover rules combine two parents to form children and mutation rules to apply random changes to parents to form children [11]. GAs have been applied at different levels in telecommunication networks, including topological design [12] and traffic routing [13]. In this paper, a GA is employed to design DWDM survivable optical mesh networks by solving RWA problems.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Survivable wavelength‐routed optical network design using genetic algorithms

Leeson

Kavian

Ren

et al. 2007

Trans Emerging Tel Tech

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SUMMARYThe provision of acceptable service in the presence of failures and attacks is a major issue in the design of next generation dense wavelength division multiplexing (DWDM) networks. Survivability is provided by the establishment of spare lightpaths for each connection request to protect the working lightpaths. This paper presents a genetic algorithm (GA) solver for the routing and wavelength assignment problem with working and spare lightpaths to design survivable optical networks in the presence of a single link failure. Lightpaths are encoded into chromosomes made up of a fixed number of genes equal to the number of entries in the traffic demand matrix. Each gene represents one valid path and is thus coded as a variable length binary string. After crossover and mutation, each member of the population represents a set of valid but possibly incompatible paths and those that do not satisfy the problem constraints are discarded. The best paths are then found by use of a fitness function and these are assigned the minimum number of wavelengths according to the problem constraints. The proposed approach has been evaluated on dedicated path protection and shared path protection. Simulation results show that the GA method is efficient and able to design DWDM survivable real-world optical mesh networks.

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Section: Genetic Algorithmsmentioning

confidence: 99%

Survivable wavelength‐routed optical network design using genetic algorithms

Leeson

Kavian

Ren

et al. 2007

Trans Emerging Tel Tech

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“…By repeatedly utilizing selection and reproduction principles to the population of individuals representing solutions to the problem, the evolutionary techniques evolve a satisfactory solution quickly and efficiently. Therefore, EC tools find applications in many problems ranging from telecommunication networks (Arabas and Kozdrowski, 2001), to fuzzy learning (Russo, 2000), to modeling (Fujiwara and Sawai, 1999;Chen et al, 2002), and data mining (Brameier and Banzhaf, 2001;Parpinelli et al, 2002), as well as image processing problems mostly related to gray-scale restoration (Hamid et al, 2003), feature extraction (Liu et al, 1997), and coding (Dasgupta et al, 2000). In this study, we intend to use genetic algorithm (GA) in color image filtering and enhancement applications (Lukac et al, 2005a).…”

Section: Introductionmentioning

confidence: 99%

Color image denoising using evolutionary computation

Lukáč

Plataniotis

Venetsanopoulos

2005

Int. J. Imaging Syst. Technol.

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Noise suppression in multichannel data sets, such as color images, has drawn much attention in the last few years. An issue of paramount importance in designing color image filters is the determination of the coefficients that should be used to weight the inputs to the filter. In this study, we propose an evolutionary computation-based approach to select and optimize the coefficients in the class of weighted vector directional filters. Using a genetic algorithm, we were able to adapt the filter weights to match varying image and noise characteristics. Extended experimentation with realistic image processing applications, including television image enhancement and virtual restoration of artworks, indicates that the proposed filters are capable of removing noise while preserving chromaticity information, edges, and fine details, as well as structural image content.

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“…Linear and integer programming have been used to a limited extent for network optimization since the number of variables grows exponentially with the number of nodes, e.g., [1,2]. There are several randomized search techniques that were used for design of network topologies.…”

Section: Communication Network Design -A Reviewmentioning

confidence: 99%

Multiobjective network design for realistic traffic models

Banerjee

Kumar

2007

Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation

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Network topology design problems find application in several real life scenarios. However, most designs in the past either optimize for a single criterion like delay or assume simplistic traffic models like Poisson. Such assumptions make the solutions inapplicable in the practical world.In this paper, we formulate and solve a multiobjective network topology design problem for a realistic Internet traffic model which is assumed to be self similar. We optimize for the average packet delivery delay and network layout cost to construct realistic network topologies. We present a multiobjective evolutionary algorithm (MOEA) to obtain the diverse near-optimal network topologies. For fair comparison, we design a multiobjective deterministic heuristic based on branch exchange -we call the heuristic Pareto Branch Exchange (PBE). We empirically show that the MOEA used performs well for real networks of various sizes, and generated topologies are quite different with significantly larger delays for the self similar traffic model.

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Applying an evolutionary algorithm to telecommunication network design

Cited by 39 publications

References 9 publications

Survivable wavelength‐routed optical network design using genetic algorithms

Survivable wavelength‐routed optical network design using genetic algorithms

Color image denoising using evolutionary computation

Multiobjective network design for realistic traffic models

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