NDLI: Solving Multicommodity Capacitated Network Design Problems using a Multiobjective Evolutionary Algorithm

Content Provider	IEEE Xplore Digital Library
Author	Kleeman, M.P. Lamont, G.B. Hopkinson, K.M. Graham, S.R.
Copyright Year	2007
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Air Force Inst. of Technol., Dayton, OH (Kleeman, M.P.; Lamont, G.B.; Hopkinson, K.M.; Graham, S.R.)
Abstract	Evolutionary algorithms have been applied to a variety of network flow problems with acceptable results. In this research, a multiobjective evolutionary algorithm (MOEA) is used to solve a variation of the multicommodity capacitated network design problem (MCNDP). This variation represents a hybrid communication network as found in network centric models with multiple objectives including costs, delays, robustness, vulnerability, and reliability. Nodes in such centric systems can have multiple and varying link capacities, rates and information (commodity) quantities to be delivered and received. Each commodity can have an independent prioritized bandwidth requirement as well. Insight to the MCNDP problem domain and Pareto structure is developed. The nondominated sorting genetic algorithm (NSGA-II) is modified and extended to solve such a MCNDP. Since the MCNDP is highly constrained, a novel initialization procedure and mutation method are also integrated into this MOEA. Empirical results and analysis indicate that effective solutions are generated very efficiently
Starting Page	33
Ending Page	41
File Size	10132110
Page Count	9
File Format	PDF
ISBN	1424407001
DOI	10.1109/CISDA.2007.368132
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-04-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Costs Genetic mutations Evolutionary computation Bandwidth Robustness Communication networks Telecommunication network reliability Sorting Genetic algorithms
Content Type	Text
Resource Type	Article

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