Genetic optimization methods for Traffic Engineering problems in multi-service high speed optical networks

Vasilios Pasias; Dimitrios A. Karras; Rallis C. Papademetriou; Bhanu Prasad

doi:10.1515/JISYS.2007.16.4.339

Genetic optimization methods for Traffic Engineering problems in multi-service high speed optical networks

Vasilios Pasias, Dimitrios A. Karras, Rallis C. Papademetriou, Bhanu Prasad^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

This paper presents two new methods for solving the offline Traffic Engineering (TE) problem in multi-service high speed optical networks. The methods are based on genetic optimization techniques. In the first method, the offline TE problem is formulated as an optimization model with linear constraints and then it is solved using a modified version of the Genetic Algorithm for Numerical Optimization for Constraint Problems (GENOCOP). In the second method, a hybrid method based on GENOCOP and a heuristic TE algorithm is presented to solve the above problem. The performance results of these methods are compared with that of a standard linear programming optimization method. Two different optical network topologies are considered for the comparison purposes.

Original language	English
Pages (from-to)	339-357
Number of pages	19
Journal	Journal of Intelligent Systems
Volume	16
Issue number	4
DOIs	https://doi.org/10.1515/JISYS.2007.16.4.339
Publication status	Published - 1 Dec 2007

Keywords

Genetic algorithms
High speed networks
Optimization

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10.1515/JISYS.2007.16.4.339

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title = "Genetic optimization methods for Traffic Engineering problems in multi-service high speed optical networks",

abstract = "This paper presents two new methods for solving the offline Traffic Engineering (TE) problem in multi-service high speed optical networks. The methods are based on genetic optimization techniques. In the first method, the offline TE problem is formulated as an optimization model with linear constraints and then it is solved using a modified version of the Genetic Algorithm for Numerical Optimization for Constraint Problems (GENOCOP). In the second method, a hybrid method based on GENOCOP and a heuristic TE algorithm is presented to solve the above problem. The performance results of these methods are compared with that of a standard linear programming optimization method. Two different optical network topologies are considered for the comparison purposes.",

keywords = "Genetic algorithms, High speed networks, Optimization",

author = "Vasilios Pasias and Karras, {Dimitrios A.} and Papademetriou, {Rallis C.} and Bhanu Prasad",

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AU - Pasias, Vasilios

AU - Karras, Dimitrios A.

AU - Papademetriou, Rallis C.

AU - Prasad, Bhanu

PY - 2007/12/1

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N2 - This paper presents two new methods for solving the offline Traffic Engineering (TE) problem in multi-service high speed optical networks. The methods are based on genetic optimization techniques. In the first method, the offline TE problem is formulated as an optimization model with linear constraints and then it is solved using a modified version of the Genetic Algorithm for Numerical Optimization for Constraint Problems (GENOCOP). In the second method, a hybrid method based on GENOCOP and a heuristic TE algorithm is presented to solve the above problem. The performance results of these methods are compared with that of a standard linear programming optimization method. Two different optical network topologies are considered for the comparison purposes.

AB - This paper presents two new methods for solving the offline Traffic Engineering (TE) problem in multi-service high speed optical networks. The methods are based on genetic optimization techniques. In the first method, the offline TE problem is formulated as an optimization model with linear constraints and then it is solved using a modified version of the Genetic Algorithm for Numerical Optimization for Constraint Problems (GENOCOP). In the second method, a hybrid method based on GENOCOP and a heuristic TE algorithm is presented to solve the above problem. The performance results of these methods are compared with that of a standard linear programming optimization method. Two different optical network topologies are considered for the comparison purposes.

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KW - Optimization

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JF - Journal of Intelligent Systems

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Genetic optimization methods for Traffic Engineering problems in multi-service high speed optical networks

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Keywords

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