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With the growth of the Internet, Internet Service Providers (ISPs) try to meet the increasing traffic demand with new technology and improved utilization of existing resources. Routing of data packets can affect network utilization. Packets are sent along network paths from source to destination following a protocol. Open Shortest Path First (OSPF) is the most commonly used intra-domain Internet routing protocol (IRP). Traffic flow is routed along shortest paths, splitting flow at nodes with several outgoing links on a shortest path to the destination IP address. Link weights are assigned by the network operator. A path length is the sum of the weights of the links in the path. The OSPF weight setting (OSPFWS) problem seeks a set of weights that optimizes network performance. We study the problem of optimizing OSPF weights, given a set of projected demands, with the objective of minimizing network congestion. The weight assignment problem is NP-hard. We present a genetic algorithm (GA) to solve the OSPFWS problem. We compare our results with the best known and commonly used heuristics for OSPF weight setting, as well as with a lower bound of the optimal multi-commodity flow routing, which is a linear programming relaxation of the OSPFWS problem. Computational experiments are made on the AT&T Worldnet backbone with projected demands, and on twelve instances of synthetic networks. 相似文献
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Clustering of Microarray data via Clique Partitioning 总被引:2,自引:2,他引:0
Gary?KochenbergerEmail author Fred?Glover Bahram?Alidaee Haibo?Wang 《Journal of Combinatorial Optimization》2005,10(1):77-92
Microarrays are repositories of gene expression data that hold tremendous potential for new understanding, leading to advances in functional genomics and molecular biology. Cluster analysis (CA) is an early step in the exploration of such data that is useful for purposes of data reduction, exposing hidden patterns, and the generation of hypotheses regarding the relationship between genes and phenotypes. In this paper we present a new model for the clique partitioning problem and illustrate how it can be used to perform cluster analysis in this setting. 相似文献
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Yannis?MarinakisEmail author Athanasios?Migdalas Panos?M.?Pardalos 《Journal of Combinatorial Optimization》2005,10(4):311-326
Hybridization techniques are very effective for the solution of combinatorial optimization problems. This paper presents a
genetic algorithm based on Expanding Neighborhood Search technique (Marinakis, Migdalas, and Pardalos, Computational Optimization and Applications, 2004) for the solution of the traveling salesman problem: The initial population of the algorithm is created not entirely
at random but rather using a modified version of the Greedy Randomized Adaptive Search Procedure. Farther more a stopping
criterion based on Lagrangean Relaxation is proposed. The combination of these different techniques produces high quality
solutions. The proposed algorithm was tested on numerous benchmark problems from TSPLIB with very satisfactory results. Comparisons
with the algorithms of the DIMACS Implementation Challenge are also presented. 相似文献
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