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Very LargeScale Neighborhood Search for the Quadratic Assignment Problem
 DISCRETE APPLIED MATHEMATICS
, 2002
"... The Quadratic Assignment Problem (QAP) consists of assigning n facilities to n locations so as to minimize the total weighted cost of interactions between facilities. The QAP arises in many diverse settings, is known to be NPhard, and can be solved to optimality only for fairly small size instances ..."
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Cited by 108 (11 self)
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The Quadratic Assignment Problem (QAP) consists of assigning n facilities to n locations so as to minimize the total weighted cost of interactions between facilities. The QAP arises in many diverse settings, is known to be NPhard, and can be solved to optimality only for fairly small size instances (typically, n < 25). Neighborhood search algorithms are the most popular heuristic algorithms to solve larger size instances of the QAP. The most extensively used neighborhood structure for the QAP is the 2exchange neighborhood. This neighborhood is obtained by swapping the locations of two facilities and thus has size O(n²). Previous efforts to explore larger size neighborhoods (such as 3exchange or 4exchange neighborhoods) were not very successful, as it took too long to evaluate the larger set of neighbors. In this paper, we propose very largescale neighborhood (VLSN) search algorithms where the size of the neighborhood is very large and we propose a novel search procedure to heuristically enumerate good neighbors. Our search procedure relies on the concept of improvement graph which allows us to evaluate neighbors much faster than the existing methods. We present extensive computational results of our algorithms on standard benchmark instances. These investigations reveal that very largescale neighborhood search algorithms give consistently better solutions compared the popular 2exchange neighborhood algorithms considering both the solution time and solution accuracy.
MatrixExplorer: a DualRepresentation System to Explore Social Networks
 IEEE Transactions on Visualization and Computer Graphics
, 2006
"... Abstract — MatrixExplorer is a network visualization system that uses two representations: nodelink diagrams and matrices. Its design comes from a list of requirements formalized after several interviews and a participatory design session conducted with social science researchers. Although matrices ..."
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Cited by 55 (12 self)
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Abstract — MatrixExplorer is a network visualization system that uses two representations: nodelink diagrams and matrices. Its design comes from a list of requirements formalized after several interviews and a participatory design session conducted with social science researchers. Although matrices are commonly used in social networks analysis, very few systems support the matrixbased representations to visualize and analyze networks. MatrixExplorer provides several novel features to support the exploration of social networks with a matrixbased representation, in addition to the standard interactive filtering and clustering functions. It provides tools to reorder (layout) matrices, to annotate and compare findings across different layouts and find consensus among several clusterings. MatrixExplorer also supports Nodelink diagram views which are familiar to most users and remain a convenient way to publish or communicate exploration results. Matrix and nodelink representations are kept synchronized at all stages of the exploration process. Index Terms — social networks visualization, nodelink diagrams, matrixbased representations, exploratory process, matrix ordering, interactive clustering, consensus. Fig. 1. MatrixExplorer showing two synchronized representations of the same network: matrix on the left and nodelink on the right. 1
Experimental Analysis of Heuristics for the STSP
 Local Search in Combinatorial Optimization
, 2001
"... In this and the following chapter, we consider what approaches one should take when one is confronted with a realworld application of the TSP. What algorithms should be used under which circumstances? We ..."
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Cited by 53 (1 self)
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In this and the following chapter, we consider what approaches one should take when one is confronted with a realworld application of the TSP. What algorithms should be used under which circumstances? We
Memetic Algorithms for the Traveling Salesman Problem
 Complex Systems
, 1997
"... this paper, the tness landscapes of several instances of the traveling salesman problem (TSP) are investigated to illustrate why MAs are wellsuited for nding nearoptimum tours for the TSP. It is shown that recombination{based MAs can exploit the correlation structure of the landscape. A comparis ..."
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Cited by 25 (7 self)
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this paper, the tness landscapes of several instances of the traveling salesman problem (TSP) are investigated to illustrate why MAs are wellsuited for nding nearoptimum tours for the TSP. It is shown that recombination{based MAs can exploit the correlation structure of the landscape. A comparison of several recombination operators { including a new generic recombination operator { reveals that when using the sophisticated Lin{Kernighan local search, the performance dierence of the MAs is small. However, the most important property of eective recombination operators is shown to be respectfulness. In experiments it is shown that our MAs with generic recombination are among the best evolutionary algorithms for the TSP. In particular, optimum solutions could be found up to a problem size of 3795, and for large instances up to 85,900 cities, nearoptimum solutions could be found in a reasonable amount of time
A very large scale neighborhood search algorithm for the quadratic assignment problem
 JOURNAL ON COMPUTING
, 2002
"... Many optimization problems of practical interest are computationally intractable. Therefore, a practical approach for solving such problems is to employ heuristic (approximation) algorithms that can find nearly optimal solutions within a reasonable amount of computation time. An improvement algorith ..."
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Cited by 20 (3 self)
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Many optimization problems of practical interest are computationally intractable. Therefore, a practical approach for solving such problems is to employ heuristic (approximation) algorithms that can find nearly optimal solutions within a reasonable amount of computation time. An improvement algorithm generally starts with a feasible solution and iteratively tries to obtain a better solution. Neighborhood search algorithms (alternatively called local search algorithms) are a wide class of improvement heuristics where at each iteration an improving solution is found by searching the “neighborhood” of the current solution. A critical issue in the design of a neighborhood search approach is the choice of the neighborhood structure, that is, the manner in which the neighborhood is defined. As a rule of thumb, the larger the neighborhood, the better is the quality of the locally optimal solutions, and the greater is the accuracy of the final solution that is obtained. At the same time, the larger the neighborhood, the longer it takes to search the neighborhood at each iteration. For this reason a larger neighborhood does not necessarily produce a more effective heuristic unless one can search the larger neighborhood in a very efficient manner. This paper concentrates on neighborhood search algorithms where the size of the neighborhood is “very large” with respect to the size of the input data and in which the neighborhood is searched in an efficient manner. We survey three broad classes of very large scale neighborhood search (VLSN) algorithms: (1) variable depth
Generating similaritybased playlists using traveling salesman algorithms
 In DAFx
, 2005
"... When using a mobile music player enroute, usually only little attention can be paid to its handling. Nonetheless it is desirable that all music stored in the device can be accessed quickly, and that tracks played in a sequence should match up. In this paper, we present an approach to satisfy these ..."
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Cited by 19 (8 self)
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When using a mobile music player enroute, usually only little attention can be paid to its handling. Nonetheless it is desirable that all music stored in the device can be accessed quickly, and that tracks played in a sequence should match up. In this paper, we present an approach to satisfy these constraints: a playlist containing all tracks stored in the music player is generated such that in average, consecutive pieces are maximally similar. This is achieved by applying a Traveling Salesman algorithm to the pieces, using timbral similarities as the distances. The generated playlist is linear and circular, thus the whole collection can easily be browsed with only one input wheel. When a chosen track finishes playing, the player advances to the consecutive tracks in the playlist, generally playing tracks similar to the chosen track. This behavior could be a favorable alternative to the wellknown shuffle function that most current devices – such as the iPod shuffle, for example – have. We evaluate the fitness of four different Traveling Salesman algorithms for this purpose. Evaluated aspects were runtime, the length of the resulting route, and the genre distribution entropy. We implemented a Java applet to demonstrate the application and its usability. 1.
An Effective Implementation of Kopt Moves for the LinKernighan TSP
 Roskilde University, 2007. Case
, 2006
"... Local search with kchange neighborhoods, kopt, is the most widely used heuristic method for the traveling salesman problem (TSP). This report presents an effective implementation of kopt for the LinKernighan TSP heuristic. The effectiveness of the implementation is demonstrated with extensive ex ..."
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Cited by 12 (1 self)
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Local search with kchange neighborhoods, kopt, is the most widely used heuristic method for the traveling salesman problem (TSP). This report presents an effective implementation of kopt for the LinKernighan TSP heuristic. The effectiveness of the implementation is demonstrated with extensive experiments on instances ranging from 10,000 to 10,000,000 cities. 1.
CARTHAGENE: multipopulation integrated genetic and radiated hybrid mapping
 Bioinformatics
, 2004
"... Summary: CART HAGENE is an integrated genetic and radiated hybrid mapping tool which can deal with multiple populations, including mixtures of genetic and RH data. CART HAGENE performs multipoint maximum likelihood estimations with accelerated EM algorithms for some pedigrees and has sophisticated a ..."
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Cited by 11 (3 self)
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Summary: CART HAGENE is an integrated genetic and radiated hybrid mapping tool which can deal with multiple populations, including mixtures of genetic and RH data. CART HAGENE performs multipoint maximum likelihood estimations with accelerated EM algorithms for some pedigrees and has sophisticated algorithms for marker ordering. Dedicated heuristics for framework mapping are also included. CART HAGENE can be used as a C++ library, through a shell command and through a graphical interface. XML output for companion tools is integrated.
Samplingbased planning for discrete spaces
 In IEEE/RSJ International Conference on Intelligent Robots and Systems
, 2004
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