Optimizing the system of virtual paths
IEEE/ACM Transactions on Networking (TON)
Computational experience with a difficult mixed-integer multicommodity flow problem
Mathematical Programming: Series A and B
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Improved bounds for the unsplittable flow problem
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
On path selection for traffic with bandwidth guarantees
ICNP '97 Proceedings of the 1997 International Conference on Network Protocols (ICNP '97)
Single-source unsplittable flow
FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
Computational Optimization and Applications
Routing, Flow, and Capacity Design in Communication and Computer Networks
Routing, Flow, and Capacity Design in Communication and Computer Networks
Mesh-based Survivable Transport Networks: Options and Strategies for Optical, MPLS, SONET and ATM Networking
ICCS'03 Proceedings of the 2003 international conference on Computational science
Lagrangean heuristic for primary routes assignment in survivable connection-oriented networks
Computational Optimization and Applications
Evolutionary Algorithm for Solving Congestion Problem in Computer Networks
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
Quasi-hierarchical evolutionary algorithm for flow optimization in survivable MPLS networks
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part III
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The major objective of this paper is to deploy an effective evolutionary algorithm (EA) for the congestion problem in connection-oriented networks. The network flow is modeled as non-bifurcated multicommodity flow. The main novelty of this work is that the proposed evolutionary algorithm consists of two levels. The high level applies typical EA operators. The low level idea is based on the hierarchical algorithm idea. However, the presented approach is not a classical hierarchical algorithm. Therefore, we call the algorithm quasi-hierarchical. We present a brief description of the algorithm and results of simulations run over various networks.