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Guided Local Search
, 2010
"... Combinatorial explosion problem is a well known phenomenon that prevents complete algorithms from solving many reallife combinatorial optimization problems. In many situations, heuristic search methods are needed. This chapter describes the principles of Guided Local Search (GLS) and Fast Local Sea ..."
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Cited by 64 (5 self)
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Combinatorial explosion problem is a well known phenomenon that prevents complete algorithms from solving many reallife combinatorial optimization problems. In many situations, heuristic search methods are needed. This chapter describes the principles of Guided Local Search (GLS) and Fast Local Search (FLS) and surveys their applications. GLS is a penaltybased metaheuristic algorithm that sits on top of other local search algorithms, with the aim to improve their efficiency and robustness. FLS is a way of reducing the size of the neighbourhood to improve the efficiency of local search. The chapter also provides guidance for implementing and using GLS and FLS. Four problems, representative of general application categories, are examined with detailed information provided on how to build a GLSbased method in each case.
A greedy genetic algorithm for the quadratic assignment problem
 COMPUTERS & OPERATIONS RESEARCH
, 2000
"... ..."
The L(h, k)Labelling Problem: A Survey and Annotated Bibliography
, 2006
"... Given any fixed nonnegative integer values h and k, the L(h, k)labelling problem consists in an assignment of nonnegative integers to the nodes of a graph such that adjacent nodes receive values which differ by at least h, and nodes connected by a 2 length path receive values which differ by at l ..."
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Cited by 29 (3 self)
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Given any fixed nonnegative integer values h and k, the L(h, k)labelling problem consists in an assignment of nonnegative integers to the nodes of a graph such that adjacent nodes receive values which differ by at least h, and nodes connected by a 2 length path receive values which differ by at least k. The span of an L(h, k)labelling is the difference between the largest and the smallest assigned frequency. The goal of the problem is to find out an L(h, k)labelling with minimum span. The L(h, k)labelling problem has been intensively studied following many approaches and restricted to many special cases, concerning both the values of h and k and the considered classes of graphs. This paper reviews the results from previous by published literature, looking at the problem with a graph algorithmic approach.
Solving the Radio Link Frequency Assignment Problem using Guided Local Search
, 1998
"... this paper, we examine the application of the combinatorial optimisation technique of Guided Local Search to the Radio Link Frequency Assignment Problem (RLFAP). RLFAP stems from real world situations in military telecommunications and it is known to be an NPhard problem. Guided Local Search is a m ..."
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Cited by 14 (7 self)
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this paper, we examine the application of the combinatorial optimisation technique of Guided Local Search to the Radio Link Frequency Assignment Problem (RLFAP). RLFAP stems from real world situations in military telecommunications and it is known to be an NPhard problem. Guided Local Search is a metaheuristic that sits on top of local search procedures allowing them to escape from local minima. GLS is shown to be superior to other methods proposed in the literature for the problem, making it the best choice for solving RLFAPs. 2. INTRODUCTION
Exact solution of a class of frequency assignment problems in cellular networks and other regular grids
 in: 8th Italian Conf. Theor. Comp. Sci. (ICTCS’03), LNCS
, 2003
"... For any non negative real values h and k, an L(h, k)labeling of a graph G = (V, E) is a function L: V → IR such that L(u) − L(v)  ≥ h if (u, v) ∈ E and L(u) − L(v)  ≥ k if there exists w ∈ V such that (u, w) ∈ E and (w, v) ∈ E. The span of an L(h, k)labeling is the difference between th ..."
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Cited by 13 (6 self)
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For any non negative real values h and k, an L(h, k)labeling of a graph G = (V, E) is a function L: V → IR such that L(u) − L(v)  ≥ h if (u, v) ∈ E and L(u) − L(v)  ≥ k if there exists w ∈ V such that (u, w) ∈ E and (w, v) ∈ E. The span of an L(h, k)labeling is the difference between the largest and the smallest value of L, so it is not restrictive to assume 0 as the smallest value of L. We denote by λh,k(G) the smallest real λ such that graph G has an L(h, k)labeling of span λ. The aim of the L(h, k)problem is to satisfy the distance constraints using the minimum span. In this paper, we study L(h, k)labeling problem on regular grids of degree 3, 4, 6, and 8 solving several open problems left in the literature. Keywords: L(h,k)labeling, triangular grids, hexagonal grids, squared grids, octagonal grids. 1
Evolution of planning for wireless communication systems
 In Proc. of HICSS’03, Big Island
, 2003
"... In this paper we provide a detailed and comprehensive survey of proposed approaches for network design, charting the evolution of models and techniques for the automatic planning of cellular wireless services. These problems present themselves as a tradeoff between commitment to infrastructure and ..."
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Cited by 9 (0 self)
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In this paper we provide a detailed and comprehensive survey of proposed approaches for network design, charting the evolution of models and techniques for the automatic planning of cellular wireless services. These problems present themselves as a tradeoff between commitment to infrastructure and quality of service, and have become increasingly complex with the advent of more sophisticated protocols and wireless architectures. Consequently these problems are receiving increased attention from researchers in a variety of fields who adopt a wide range of models, assumptions and methodologies for problem solution. We seek to unify this dispersed and fragmented literature by charting the evolution of centralised planning for cellular systems. 1
Graph domination, coloring and cliques in telecommunications
 Handbook of Optimization in Telecommunications, pages 865–890. Spinger Science + Business
, 2006
"... This paper aims to provide a detailed survey of existing graph models and algorithms for important problems that arise in different areas of wireless telecommunication. In particular, applications of graph optimization problems such as minimum dominating set, minimum vertex coloring and maximum cliq ..."
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Cited by 9 (3 self)
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This paper aims to provide a detailed survey of existing graph models and algorithms for important problems that arise in different areas of wireless telecommunication. In particular, applications of graph optimization problems such as minimum dominating set, minimum vertex coloring and maximum clique in multihop wireless networks are discussed. Different forms of graph domination have been used extensively to model clustering in wireless ad hoc networks. Graph coloring problems and their variants have been used to model channel assignment and scheduling type problems in wireless networks. Cliques are used to derive bounds on chromatic number, and are used in models of traffic flow, resource allocation, interference, etc. In this paper we survey the solution methods proposed in the literature for these problems and some recent theoretical results that are relevant to this area of research in wireless networks.
On distance constrained labeling of disk graphs
, 2004
"... A disk graph is the intersection graph of a set of disks in the plane. For a ktuple (p1,..., pk) of positive integers, a distance constrained labeling of a graph G is an assignment of labels to the vertices of G such that the labels of any pair of vertices at graph distance i in G differ by at leas ..."
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Cited by 8 (2 self)
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A disk graph is the intersection graph of a set of disks in the plane. For a ktuple (p1,..., pk) of positive integers, a distance constrained labeling of a graph G is an assignment of labels to the vertices of G such that the labels of any pair of vertices at graph distance i in G differ by at least pi, for i = 1,..., k. In the case when k = 1 and p1 = 1, this gives a traditional coloring of G. We propose and analyze several online and offline labeling algorithms for the class of disk graphs.
Hierarchical cellular network design with channel allocation
 European Journal of Operational Research
"... The design of a cellular network is a complex process that encompasses the selection and configuration of cell sites and the supporting network infrastructure. This investigation presents a net revenue maximizing model that can assist network designers in the design and configuration of a cellular ..."
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Cited by 8 (0 self)
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The design of a cellular network is a complex process that encompasses the selection and configuration of cell sites and the supporting network infrastructure. This investigation presents a net revenue maximizing model that can assist network designers in the design and configuration of a cellular system. The integer programming model takes as given a set of candidate cell locations with corresponding costs, the amount of available bandwidth, the maximum demand for service in each geographical area, and the revenue potential in each customer area. Based on these data, the model determines the size and location of cells, and the specific channels to be allocated to each cell. To solve problem instances, a maximal clique cut procedure is developed in order to efficiently generate tight upper bounds. A lower bound is constructed by solving the discrete optimization model with some of the discrete variables fixed. Computational experiments on seventytwo problem instances demonstrate the computational viability of our new procedure. 1