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Tabu Search For Graph Coloring, TColorings And Set TColorings
, 1998
"... In this paper, a generic tabu search is presented for three coloring problems: graph coloring, Tcolorings and set Tcolorings. This algorithm integrates important features such as greedy initialization, solution regeneration, dynamic tabu tenure, incremental evaluation of solutions and constraint ..."
Abstract

Cited by 27 (8 self)
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In this paper, a generic tabu search is presented for three coloring problems: graph coloring, Tcolorings and set Tcolorings. This algorithm integrates important features such as greedy initialization, solution regeneration, dynamic tabu tenure, incremental evaluation of solutions and constraint handling techniques. Empirical comparisons show that this algorithm approaches the best coloring algorithms and outperforms some hybrid algorithms on a wide range of benchmarks. Experiments on large random instances of Tcolorings and set Tcolorings show encouraging results.
An ANT Heuristic for the Frequency Assignment Problem
 Future Generation Computer Systems
, 1999
"... The problem considered in this paper consists in defining an assignment of frequencies to radio links, to be established between base stations and mobile transmitters, which minimizes the global interference over a given region. This problem is NPhard and few results have been reported on techni ..."
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Cited by 18 (2 self)
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The problem considered in this paper consists in defining an assignment of frequencies to radio links, to be established between base stations and mobile transmitters, which minimizes the global interference over a given region. This problem is NPhard and few results have been reported on techniques for solving it to optimality. We have applied to this version of the frequency assignment problem an ANTS metaheuristic, that is an approach following the ACO optimization paradigm. Computational results, obtained on a number of standard problem instances, testify the effectiveness of the proposed approach. 1. Introduction The introduction of mobile communication, such as portable phones, has a tremendous impact on everyday life. Mobility raises a number of research questions: for many of them discrete models and algorithms are required in order to solve the underlying mathematical problem. The Ant Colony Optimization paradigm (ACO) [Dorigo and Di Caro, 1999], [Maniezzo and Carbo...
Experiments with a randomized algorithm for a frequency assignment problem
 ECOLE NORMALE SUP'ERIEURE DE LYON
, 1997
"... The problems of assigning frequencies to transmitters can be naturally modelled by generalizations of graph coloring problems. We start with a randomized graph coloring algorithm of Petford and Welsh and propose a randomized algorithm for minimizing the number of constraints violated when a set of f ..."
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Cited by 6 (0 self)
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The problems of assigning frequencies to transmitters can be naturally modelled by generalizations of graph coloring problems. We start with a randomized graph coloring algorithm of Petford and Welsh and propose a randomized algorithm for minimizing the number of constraints violated when a set of frequencies available is xed. Experiments on instances of various types relevant to mobile communication networks are reported.
Upper Bounds for the Span in Triangular Lattice Graphs: Application to Frequency Planning for Cellular Network
, 1997
"... We study a problem coming from the design of wireless cellular radiocommunication network. Frequency planning constraints are modelled in terms of graph theory. For each planning function f let us call sp(f)  or the span of the frequency planning f  the difference between the largest and the s ..."
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Cited by 3 (0 self)
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We study a problem coming from the design of wireless cellular radiocommunication network. Frequency planning constraints are modelled in terms of graph theory. For each planning function f let us call sp(f)  or the span of the frequency planning f  the difference between the largest and the smallest frequency used. Let the Order of the graph be Or(G) = sp(G)+1 and the maximal local order of the graph the maximum order of a clique of G, i.e. M lo(G) = max X clique of G sp(X). We show: M lo(G) sp(G) 8d Mlo(G) 6 e.