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QAPLIB  A Quadratic Assignment Problem Library
, 1996
"... This report, the data and also most of the best feasible solutions are available via World Wide Web. The URLs of the QAPLIB Home Page are http://www.opt.math.tugraz.ac.at/qaplib/ ..."
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Cited by 163 (6 self)
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This report, the data and also most of the best feasible solutions are available via World Wide Web. The URLs of the QAPLIB Home Page are http://www.opt.math.tugraz.ac.at/qaplib/
The Quadratic Assignment Problem
 HANDBOOK OF COMBINATORIAL OPTIMIZATION, P. PARDALOS AND D.Z. DU, EDS.
, 1998
"... This paper aims at describing the state of the art on quadratic assignment problems (QAPs). It discusses the most important developments in all aspects of the QAP such as linearizations, QAP polyhedra, algorithms to solve the problem to optimality, heuristics, polynomially solvable special cases, an ..."
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Cited by 110 (3 self)
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This paper aims at describing the state of the art on quadratic assignment problems (QAPs). It discusses the most important developments in all aspects of the QAP such as linearizations, QAP polyhedra, algorithms to solve the problem to optimality, heuristics, polynomially solvable special cases, and asymptotic behavior. Moreover, it also considers problems related to the QAP, e.g. the biquadratic assignment problem, and discusses the relationship between the QAP and other well known combinatorial optimization problems, e.g. the traveling salesman problem, the graph partitioning problem, etc. The paper will appear in the Handbook of Combinatorial Optimization to be published by Kluwer Academic Publishers, P. Pardalos and D.Z. Du, eds.
The Quadratic Assignment Problem: A Survey and Recent Developments
 In Proceedings of the DIMACS Workshop on Quadratic Assignment Problems, volume 16 of DIMACS Series in Discrete Mathematics and Theoretical Computer Science
, 1994
"... . Quadratic Assignment Problems model many applications in diverse areas such as operations research, parallel and distributed computing, and combinatorial data analysis. In this paper we survey some of the most important techniques, applications, and methods regarding the quadratic assignment probl ..."
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Cited by 91 (16 self)
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. Quadratic Assignment Problems model many applications in diverse areas such as operations research, parallel and distributed computing, and combinatorial data analysis. In this paper we survey some of the most important techniques, applications, and methods regarding the quadratic assignment problem. We focus our attention on recent developments. 1. Introduction Given a set N = f1; 2; : : : ; ng and n \Theta n matrices F = (f ij ) and D = (d kl ), the quadratic assignment problem (QAP) can be stated as follows: min p2\Pi N n X i=1 n X j=1 f ij d p(i)p(j) + n X i=1 c ip(i) ; where \Pi N is the set of all permutations of N . One of the major applications of the QAP is in location theory where the matrix F = (f ij ) is the flow matrix, i.e. f ij is the flow of materials from facility i to facility j, and D = (d kl ) is the distance matrix, i.e. d kl represents the distance from location k to location l [62, 67, 137]. The cost of simultaneously assigning facility i to locat...
A Genetic Approach to the Quadratic Assignment Problem
, 1995
"... The Quadratic Assignment Problem (QAP) is a wellknown combinatorial optimization problem with a wide variety of practical applications. Although many heuristics and semienumerative procedures for QAP have been proposed, no dominant algorithm has emerged. In this paper, we describe a Genetic Algori ..."
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Cited by 54 (7 self)
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The Quadratic Assignment Problem (QAP) is a wellknown combinatorial optimization problem with a wide variety of practical applications. Although many heuristics and semienumerative procedures for QAP have been proposed, no dominant algorithm has emerged. In this paper, we describe a Genetic Algorithm (GA) approach to QAP. Genetic algorithms are a class of randomized parallel search heuristics which emulate biological natural selection on a population of feasible solutions. We present computational results which show that this GA approach finds solutions competitive with those of the best previouslyknown heuristics, and argue that genetic algorithms provide a particularly robust method for QAP and its more complex extensions. 5 A Genetic Approach to the Quadratic Assignment Problem David M. Tate and Alice E. Smith Department of Industrial Engineering 1048 Benedum Hall University of Pittsburgh Pittsburgh, PA 15261 4126249837 4126249831 (Fax) 1. Introduction The Quadrat...
Selected Topics on Assignment Problems
, 1999
"... We survey recent developments in the fields of bipartite matchings, linear sum assignment and bottleneck assignment problems and applications, multidimensional assignment problems, quadratic assignment problems, in particular lower bounds, special cases and asymptotic results, biquadratic and co ..."
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Cited by 22 (1 self)
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We survey recent developments in the fields of bipartite matchings, linear sum assignment and bottleneck assignment problems and applications, multidimensional assignment problems, quadratic assignment problems, in particular lower bounds, special cases and asymptotic results, biquadratic and communication assignment problems.
Parallel Hybrid MetaHeuristics: Application to the Quadratic Assignment Problem
 IN PROCEEDINGS OF THE PARALLEL OPTIMIZATION COLLOQUIUM
, 1996
"... Metaheuristics are search techniques that can be applied to a broad range of combinatorial optimization problems. Each metaheuristic explores and exploits the search space in its own way. No heuristic can be better than any heuristic on a wide spectrum of problems. To make the search more efficie ..."
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Cited by 8 (1 self)
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Metaheuristics are search techniques that can be applied to a broad range of combinatorial optimization problems. Each metaheuristic explores and exploits the search space in its own way. No heuristic can be better than any heuristic on a wide spectrum of problems. To make the search more efficient and robust, hybridization of heuristics should be used. In this paper, we present an ongoing research on parallel hybrid heuristics. The Quadratic Assignement Problem is used as a testbed problem. We present the performance of different metaheuristics and their hybridization on standard problems taken from the QAPLIB library.
Evolving Design Genes in Space Layout Planning Problems
 Artificial Intelligence in Engineering
, 1997
"... The space layout planning problem is one of the most difficult in architectural design. It is practically important in architectural design because it is the basis of the development of most designs. It is important in a wider context because it maps onto a large class of locationallocation problem ..."
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Cited by 7 (0 self)
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The space layout planning problem is one of the most difficult in architectural design. It is practically important in architectural design because it is the basis of the development of most designs. It is important in a wider context because it maps onto a large class of locationallocation problems including VLSI floorplanning, process layouts and facilities layout problems. We will use the formalization of the space layout problem as a particular case of a combinatorial optimization problem  a quadratic assignment problem [9]. As such it is NPcomplete and presents all the difficulties associated with this class of problems. Over the years a number of approximate algorithms based on combinations of global and local search techniques and heuristics have been developed specifically for this class of problems. Although they are reasonably efficient for smallscale problems, the computational cost is still too high for largescale problems. Another shortcoming of the majo...
Constrained Neural Approaches to Quadratic Assignment Problems
, 1998
"... In this paper, we discuss analog neural approaches to the Quadratic Assignment Problem (QAP). These approaches employ a hard constraints scheme to restrict the domain space, and are able to obtain much improved solutions over conventional neural approaches. Since only a few strong heuristics for QAP ..."
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Cited by 7 (1 self)
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In this paper, we discuss analog neural approaches to the Quadratic Assignment Problem (QAP). These approaches employ a hard constraints scheme to restrict the domain space, and are able to obtain much improved solutions over conventional neural approaches. Since only a few strong heuristics for QAP have been known to date, our approaches are good alternatives capable of obtaining fairly good solutions in a short period of time. Some of them can also be applied to largescale problems, say of size N 300. Acknowledgment We thank Professor Tomomi Matsui of the University of Tokyo for his valuable comments and suggestions in this research, and for introducing QAP to us. We also thank Professor Kazuyuki Aihara of the University of Tokyo for his valuable suggestions and encouragement. Neural approaches to QAP 2 1 Introduction As parallel implementations of the continuation methods (Wasserstrom, 1973), analog neural approaches to combinatorial optimization problems have been widely st...
A Modified Simulated Annealing Algorithm for the Quadratic Assignment Problem
, 2003
"... Abstract. The quadratic assignment problem (QAP) is one of the wellknown combinatorial optimization problems and is known for its various applications. In this paper, we propose a modified simulated annealing algorithm for the QAP – MSAQAP. The novelty of the proposed algorithm is an advanced for ..."
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Cited by 3 (0 self)
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Abstract. The quadratic assignment problem (QAP) is one of the wellknown combinatorial optimization problems and is known for its various applications. In this paper, we propose a modified simulated annealing algorithm for the QAP – MSAQAP. The novelty of the proposed algorithm is an advanced formula of calculation of the initial and final temperatures, as well as an original cooling schedule with oscillation, i.e., periodical decreasing and increasing of the temperature. In addition, in order to improve the results obtained, the simulated annealing algorithm is combined with a tabu search approach based algorithm. We tested our algorithm on a number of instances from the library of the QAP instances – QAPLIB. The results obtained from the experiments show that the proposed algorithm appears to be superior to earlier versions of the simulated annealing for the QAP. The power of MSAQAP is also corroborated by the fact that the new best known solution was found for the one of the largest QAP instances – THO150. Key words: heuristics, local search, simulated annealing, quadratic assignment problem. 1.
PARALLEL MEMETIC ALGORITHM WITH SELECTIVE LOCAL SEARCH FOR LARGE SCALE QUADRATIC ASSIGNMENT PROBLEMS
, 2005
"... Abstract. The extent of the application of local searches in canonical memetic algorithm is typically based on the principle of “more is better”. In the same spirit, the parallel memetic algorithm (PMA) is an important extension of the canonical memetic algorithm which applies local searches to ever ..."
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Cited by 2 (1 self)
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Abstract. The extent of the application of local searches in canonical memetic algorithm is typically based on the principle of “more is better”. In the same spirit, the parallel memetic algorithm (PMA) is an important extension of the canonical memetic algorithm which applies local searches to every transitional solutions being considered. For PMA which applies a complete local search, we termed it as PMACLS. We show in this paper that instead of a complete local search, the island model PMA with selective application of local search (PMASLS) is effective in solving complex combinatorial optimization problems, in particular largescale quadratic assignment problems (QAPs). A distinct feature of the PMASLS to be noted in our study is the sampling size. We make use of a normal distribution scheme to determine the sampling ratio. Empirical study on large scale QAPs with PMASLS and PMACLS are presented. It is shown that PMASLS arrives at solutions that are competitive to the PMACLS at significantly lower computation efforts on the diverse large scale QAPs considered. This we concluded is due mainly to the ability of the PMASLS to manage a more desirable diversity profile as the search progresses.