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Metaheuristics in combinatorial optimization: Overview and conceptual comparison
 ACM COMPUTING SURVEYS
, 2003
"... The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important meta ..."
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Cited by 314 (17 self)
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The field of metaheuristics for the application to combinatorial optimization problems is a rapidly growing field of research. This is due to the importance of combinatorial optimization problems for the scientific as well as the industrial world. We give a survey of the nowadays most important metaheuristics from a conceptual point of view. We outline the different components and concepts that are used in the different metaheuristics in order to analyze their similarities and differences. Two very important concepts in metaheuristics are intensification and diversification. These are the two forces that largely determine the behaviour of a metaheuristic. They are in some way contrary but also complementary to each other. We introduce a framework, that we call the I&D frame, in order to put different intensification and diversification components into relation with each other. Outlining the advantages and disadvantages of different metaheuristic approaches we conclude by pointing out the importance of hybridization of metaheuristics as well as the integration of metaheuristics and other methods for optimization.
Iterated local search
 Handbook of Metaheuristics, volume 57 of International Series in Operations Research and Management Science
, 2002
"... Iterated Local Search has many of the desirable features of a metaheuristic: it is simple, easy to implement, robust, and highly effective. The essential idea of Iterated Local Search lies in focusing the search not on the full space of solutions but on a smaller subspace defined by the solutions th ..."
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Cited by 172 (15 self)
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Iterated Local Search has many of the desirable features of a metaheuristic: it is simple, easy to implement, robust, and highly effective. The essential idea of Iterated Local Search lies in focusing the search not on the full space of solutions but on a smaller subspace defined by the solutions that are locally optimal for a given optimization engine. The success of Iterated Local Search lies in the biased sampling of this set of local optima. How effective this approach turns out to be depends mainly on the choice of the local search, the perturbations, and the acceptance criterion. So far, in spite of its conceptual simplicity, it has lead to a number of stateoftheart results without the use of too much problemspecific knowledge. But with further work so that the different modules are well adapted to the problem at hand, Iterated Local Search can often become a competitive or even state of the art algorithm. The purpose of this review is both to give a detailed description of this metaheuristic and to show where it stands in terms of performance. O.M. acknowledges support from the Institut Universitaire de France. This work was partially supported by the “Metaheuristics Network”, a Research Training Network funded by the Improving Human Potential programme of the CEC, grant HPRNCT199900106. The information provided is the sole responsibility of the authors and does not reflect the Community’s opinion. The Community is not responsible for any use that might be made of data appearing in this publication. 1 1
A Tutorial for Competent Memetic Algorithms: Model, Taxonomy, and Design Issues
 IEEE Transactions on Evolutionary Computation
, 2005
"... We recommend you cite the published version. ..."
Fast Local Search and Guided Local Search and Their Application to British Telecom’s Workforce Scheduling Problem
, 1997
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Solving Vehicle Routing Problems using Constraint Programming and Metaheuristics
 Journal of Heuristics
, 1997
"... . Constraint Programming typically uses the technique of depthfirst branch and bound as the method of solving optimisation problems. Although this method can give the optimal solution, for large problems, the time needed to find the optimal can be prohibitive. This paper introduces a method for usi ..."
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Cited by 50 (4 self)
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. Constraint Programming typically uses the technique of depthfirst branch and bound as the method of solving optimisation problems. Although this method can give the optimal solution, for large problems, the time needed to find the optimal can be prohibitive. This paper introduces a method for using iterative improvement techniques within a Constraint Programming framework, and applies this technique to vehicle routing problems. We introduce a Constraint Programming model for vehicle routing, after which we describe a system for integrating Constraint Programming and iterative improvement techniques. We then describe how the method can be greatly accelerated by handling core constraints using fast local checks, while other more complex constraints are left to the constraint propagation system. We have coupled our iterative improvement technique with a metaheuristic to avoid the search being trapped in local minima. Two metaheuristics are investigated: a simple Tabu Search procedur...
Guided Local Search for the Vehicle Routing Problem
, 1997
"... This paper applies GLS to vehicle routing problems with time windows and capacity constraints. Results indicate that GLS can provide excellent results. This paper is organised as follows. In section 2, we introduce a local search algorithm for the vehicle routing problem. We begin first by describin ..."
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Cited by 48 (6 self)
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This paper applies GLS to vehicle routing problems with time windows and capacity constraints. Results indicate that GLS can provide excellent results. This paper is organised as follows. In section 2, we introduce a local search algorithm for the vehicle routing problem. We begin first by describing the move operators and model, and then go on to describe the objective function and search itself. The GLS metaheuristic is then presented in section 3. The application of the metaheuristic to a vehicle routing framework is discussed. Experiments are then performed using GLS on some standard benchmark problems from the literature that involve both capacity constraints on vehicles, and time windows at customers. Conclusions are drawn on the quality of the results in comparison to other methods.
Choosing Search Heuristics by NonStationary Reinforcement Learning
"... Search decisions are often made using heuristic methods because realworld applications can rarely be tackled without any heuristics. In many cases, multiple heuristics can potentially be chosen, and it is not clear a priori which would perform best. In this article, we propose a procedure that learn ..."
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Cited by 41 (1 self)
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Search decisions are often made using heuristic methods because realworld applications can rarely be tackled without any heuristics. In many cases, multiple heuristics can potentially be chosen, and it is not clear a priori which would perform best. In this article, we propose a procedure that learns, during the search process, how to select promising heuristics. The learning is based on weight adaptation and can even switch between di#erent heuristics during search. Di#erent variants of the approach are evaluated within a constraintprogramming environment.
Adaptive Constraint Satisfaction: The Quickest First Principle
 EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE
, 1996
"... The choice of a particular algorithm for solving a given class of constraint satisfaction problems is often confused by exceptional behaviour of algorithms. One method of reducing the impact of this exceptional behaviour is to adopt an adaptive philosophy to constraint satisfaction problem solving. ..."
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Cited by 36 (3 self)
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The choice of a particular algorithm for solving a given class of constraint satisfaction problems is often confused by exceptional behaviour of algorithms. One method of reducing the impact of this exceptional behaviour is to adopt an adaptive philosophy to constraint satisfaction problem solving. In this report we describe one such adaptive algorithm, based on the principle of chaining. It is designed to avoid the phenomenon of exceptionally hard problem instances. Our algorithm shows how the speed of more naïve algorithms can be utilised safe in the knowledge that the exceptional behaviour can be bounded. Our work clearly demonstrates the potential benefits of the adaptive approach and opens a new front of research for the constraint satisfaction community.
Partial constraint satisfaction problems and guided local search
 Proc., Practical Application of Constraint Technology (PACT'96
, 1996
"... A largely unexplored aspect of Constraint Satisfaction Problem (CSP) is that of overconstrained instances for which no solution exists that satisfies all the constraints. In these problems, mentioned in the literature as Partial Constraint Satisfaction Problems (PCSPs), we are often looking for sol ..."
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Cited by 34 (12 self)
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A largely unexplored aspect of Constraint Satisfaction Problem (CSP) is that of overconstrained instances for which no solution exists that satisfies all the constraints. In these problems, mentioned in the literature as Partial Constraint Satisfaction Problems (PCSPs), we are often looking for solutions which violate the minimum number of constraints. In more realistic settings, constraints violations incur different costs and solutions are sought that minimize the total cost from constraint violations and possibly other criteria. Problems in this category present enormous difficulty to complete search algorithms. In practical terms, complete search has more or less to resemble the traditional Branch and Bound taking no advantage of the efficient pruning techniques recently developed for CSPs. In this report, we examine how the stochastic search method of Guided Local Search (GLS) can be applied to these problems. The effectiveness of the method is demonstrated on instances of the Radio Link Frequency Assignment Problem (RLFAP), which is a realworld Partial CSP.
A Comparison of Traditional and Constraintbased Heuristic Methods on Vehicle Routing Problems with Side Constraints
 CONSTRAINTS
, 1998
"... The vehicle routing problem (VRP) is a variant of the familiar travelling salesman problem (TSP). In the VRP we are to perform a number of visits, using a limited number of vehicles, while minimizing the distance travelled. The VRP can be further complicated by associating time windows on visits, c ..."
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Cited by 27 (4 self)
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The vehicle routing problem (VRP) is a variant of the familiar travelling salesman problem (TSP). In the VRP we are to perform a number of visits, using a limited number of vehicles, while minimizing the distance travelled. The VRP can be further complicated by associating time windows on visits, capacity constraints on vehicles, sequencing constraints between visits, and so on. In this paper we introduce a constraintbased model of the capacitated VRP with time windows and side constraints. The model is implemented using a constraint programming toolkit. We investigate the performance of a number of construction and improvement techniques, and show that as problems become richer and more constrained conventional techniques fail while constraint directed techniques continue to perform acceptably. This suggests that constraint programming is an appropriate technology for real world VRP's.