An iterated greedy algorithm for the node placement problem in bidirectional Manhattan street networks

Authors:
Fubito Toyama;Kenji Shoji;Juichi Miyamichi
Affiliations:
Faculty of engineering, Utsunomiya University, Utsunomiya, Japan;Faculty of engineering, Utsunomiya University, Utsunomiya, Japan;Faculty of engineering, Utsunomiya University, Utsunomiya, Japan
Venue:
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Year:
2008

Citing 3
Cited 3

Performance Comparison of Wavelength Routing Optical Networks with Chordal Ring and Mesh-Torus Topologies

ICN '01 Proceedings of the First International Conference on Networking-Part 1
An effective local search for the maximum clique problem

Information Processing Letters
Routing in multihop packet switching networks: Gb/s challenge

IEEE Network: The Magazine of Global Internetworking

Multi-colony ant colony optimization for the node placement problem

Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
MC-ANT: a multi-colony ant algorithm

EA'09 Proceedings of the 9th international conference on Artificial evolution
Restarted Iterated Pareto Greedy algorithm for multi-objective flowshop scheduling problems

Computers and Operations Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

Wavelength Division Multiplexing (WDM) is a technology which multiplexes optical carrier signals on a single optical fiber by using different wavelengths. Lightwave networks based on WDM are promising ones for high-speed communication. If network nodes are equipped with tunable transmitters and receivers, a logical topology can be changed by reassigning wavelengths to tunable transceivers of nodes. Network performance is influenced by the logical node placements. Therefore, an efficient algorithm to obtain the optimal node placement to achieve the best network performance is necessary. In this paper, an iterated greedy algorithm is proposed for this node placement problem. The proposed iterated greedy algorithm consists of two phases, construction and destruction phases. As a local search algorithm, variable depth search is applied after the construction phase. The computational results showed that this iterated greedy algorithm outperformed the best metaheuristic algorithm for this problem.