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60
GREEDY RANDOMIZED ADAPTIVE SEARCH PROCEDURES
, 2002
"... GRASP is a multistart metaheuristic for combinatorial problems, in which each iteration consists basically of two phases: construction and local search. The construction phase builds a feasible solution, whose neighborhood is investigated until a local minimum is found during the local search phas ..."
Abstract

Cited by 637 (79 self)
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GRASP is a multistart metaheuristic for combinatorial problems, in which each iteration consists basically of two phases: construction and local search. The construction phase builds a feasible solution, whose neighborhood is investigated until a local minimum is found during the local search phase. The best overall solution is kept as the result. In this chapter, we first describe the basic components of GRASP. Successful implementation techniques and parameter tuning strategies are discussed and illustrated by numerical results obtained for different applications. Enhanced or alternative solution construction mechanisms and techniques to speed up the search are also described: Reactive GRASP, cost perturbations, bias functions, memory and learning, local search on partially constructed solutions, hashing, and filtering. We also discuss in detail implementation strategies of memorybased intensification and postoptimization techniques using pathrelinking. Hybridizations with other metaheuristics, parallelization strategies, and applications are also reviewed.
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 ..."
Abstract

Cited by 148 (13 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.
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 34 (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.
Breakout local search for the quadratic assignment problem
 Applied Mathematics and Computation
"... The quadratic assignment problem (QAP) is one of the most studied combinatorial optimization problems with various practical applications. In this paper, we present Breakout Local Search (BLS) for solving QAP. BLS explores the search space by a joint use of local search and adaptive perturbation str ..."
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Cited by 8 (3 self)
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The quadratic assignment problem (QAP) is one of the most studied combinatorial optimization problems with various practical applications. In this paper, we present Breakout Local Search (BLS) for solving QAP. BLS explores the search space by a joint use of local search and adaptive perturbation strategies. Experimental evaluations on the set of QAPLIB benchmark instances show that the proposed approach is able to attain current bestknown results for all but two instances with an average computing time of less than 4.5 hours. Comparisons are also provided to show the competitiveness of the proposed approach with respect to the bestperforming QAP algorithms from the literature.
Very LargeScale Neighborhood Search Techniques In Timetabling Problems
 IN PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON THE PRACTICE AND THEORY OF AUTOMATED
, 2006
"... We describe the use of very largescale neighborhood search (VLSN) techniques in examination timetabling problems. We detail three applications of VLSN algorithms that illustrate the versatility and potential of such algorithms in timetabling. The first of these uses cyclic exchange neighborhood ..."
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Cited by 7 (0 self)
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We describe the use of very largescale neighborhood search (VLSN) techniques in examination timetabling problems. We detail three applications of VLSN algorithms that illustrate the versatility and potential of such algorithms in timetabling. The first of these uses cyclic exchange neighborhoods, in which an ordered subset of exams in disjoint time slots are swapped cyclically such that each exam moves to the time slot of the exam following it in the order. The neighborhood of all such cyclic exchanges may be searched e#ectively for an improving set of moves, making this technique computationally reasonable in practice. We next describe
Management
"... Abstract: The Vehicle Routing Problem with Time Windows (VRPTW) is an important problem occurring in many logistics systems. The objective of VRPTW is to serve a set of customers within their predefined time windows at minimum cost. Ant Colony System algorithm (ACS) that is capable of searching mult ..."
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Cited by 5 (0 self)
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Abstract: The Vehicle Routing Problem with Time Windows (VRPTW) is an important problem occurring in many logistics systems. The objective of VRPTW is to serve a set of customers within their predefined time windows at minimum cost. Ant Colony System algorithm (ACS) that is capable of searching multiple search areas simultaneously in the solution space is good in diversification. On the other hand, Simulated Annealing algorithm (SA) is a local search technique that has been successfully applied to many NPhard problems. A hybrid algorithm (IACSSA) that combines an improved ACS with SA is proposed in this paper. The algorithm has been tested on 56 Solomon benchmark problems. The results show that our IACSSA is competitive with other metaheuristic approaches in the literature. The results also indicate that such a hybrid algorithm outperforms the individual heuristic alone. Key Words: logistics, ant colony system, simulated annealing, hybrid algorithm 1.
Combining (integer) linear programming techniques and metaheuristics for combinatorial optimization
 of Studies in Computational Intelligence
, 2008
"... Summary. Several different ways exist for approaching hard optimization problems. Mathematical programming techniques, including (integer) linear programming based methods, and metaheuristic approaches are two highly successful streams for combinatorial problems. These two have been established by d ..."
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Cited by 5 (2 self)
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Summary. Several different ways exist for approaching hard optimization problems. Mathematical programming techniques, including (integer) linear programming based methods, and metaheuristic approaches are two highly successful streams for combinatorial problems. These two have been established by different communities more or less in isolation from each other. Only over the last years a larger number of researchers recognized the advantages and huge potentials of building hybrids of mathematical programming methods and metaheuristics. In fact, many problems can be practically solved much better by exploiting synergies between these different approaches than by “pure ” traditional algorithms. The crucial issue is how mathematical programming methods and metaheuristics should be combined for achieving those benefits. Many approaches have been proposed in the last few years. After giving a brief introduction to the basics of integer linear programming, this chapter surveys existing techniques for such combinations and classifies them into ten methodological categories. 1
Experiments with hybrid genetic algorithm for the grey pattern problem
 Informatica
"... Abstract. Recently, genetic algorithms (GAs) and their hybrids have achieved great success in solving difficult combinatorial optimization problems. In this paper, the issues related to the performance of the genetic search in the context of the grey pattern problem (GPP) are discussed. The main att ..."
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Cited by 4 (0 self)
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Abstract. Recently, genetic algorithms (GAs) and their hybrids have achieved great success in solving difficult combinatorial optimization problems. In this paper, the issues related to the performance of the genetic search in the context of the grey pattern problem (GPP) are discussed. The main attention is paid to the investigation of the solution recombination, i.e., crossover operators which play an important role by developing robust genetic algorithms. We implemented seven crossover operators within the hybrid genetic algorithm (HGA) framework, and carried out the computational experiments in order to test the influence of the recombination operators to the genetic search process. We examined the one point crossover, the uniform like crossover, the cycle crossover, the swap path crossover, and others. A socalled multiple parent crossover based on a special type of recombination of several solutions was tried, too. The results obtained from the experiments on the GPP test instances demonstrate promising efficiency of the swap path and multiple parent crossovers. Key words: combinatorial optimization, heuristic algorithms, genetic algorithms, crossover operators, grey pattern problem, quadratic assignment problem.