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49
Adaptive Constraint Satisfaction
 WORKSHOP OF THE UK PLANNING AND SCHEDULING
, 1996
"... Many different approaches have been applied to constraint satisfaction. These range from complete backtracking algorithms to sophisticated distributed configurations. However, most research effort in the field of constraint satisfaction algorithms has concentrated on the use of a single algorithm fo ..."
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Cited by 916 (43 self)
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Many different approaches have been applied to constraint satisfaction. These range from complete backtracking algorithms to sophisticated distributed configurations. However, most research effort in the field of constraint satisfaction algorithms has concentrated on the use of a single algorithm for solving all problems. At the same time, a consensus appears to have developed to the effect that it is unlikely that any single algorithm is always the best choice for all classes of problem. In this paper we argue that an adaptive approach should play an important part in constraint satisfaction. This approach relaxes the commitment to using a single algorithm once search commences. As a result, we claim that it is possible to undertake a more focused approach to problem solving, allowing for the correction of bad algorithm choices and for capitalising on opportunities for gain by dynamically changing to more suitable candidates.
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 63 (6 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.
Guided local search and its application to the traveling salesman problem
, 1998
"... The Traveling Salesman Problem (TSP) is one of the most famous problems in combinatorial optimization. In this paper, we are going to examine how the techniques of Guided Local Search (GLS) and Fast Local Search (FLS) can be applied to the problem. Guided Local Search sits on top of local search heu ..."
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Cited by 57 (15 self)
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The Traveling Salesman Problem (TSP) is one of the most famous problems in combinatorial optimization. In this paper, we are going to examine how the techniques of Guided Local Search (GLS) and Fast Local Search (FLS) can be applied to the problem. Guided Local Search sits on top of local search heuristics and has as a main aim to guide these procedures in exploring efficiently and effectively the vast search spaces of combinatorial optimization problems. Guided Local Search can be combined with the neighborhood reduction scheme of Fast Local Search which significantly speeds up the operations of the algorithm. The combination of GLS and FLS with TSP local search heuristics of different efficiency and effectiveness is studied in an effort to determine the dependence of GLS on the underlying local search heuristic used. Comparisons are made with some of the best TSP heuristic algorithms and general optimization techniques which demonstrate the advantages of GLS over alternative heuristic approaches suggested for the problem.
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 49 (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 44 (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.
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 33 (13 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.
Guided Local Search  An Illustrative Example in Function Optimisation
 In BT Technology Journal, Vol.16, No.3
, 1998
"... The Guided Local Search method has been successfully applied to a number of hard combinatorial optimisation problems from the wellknown TSP and QAP to real world problems such as Frequency Assignment and Workforce Scheduling. In this paper, we are demonstrating that the potential applications of GL ..."
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Cited by 18 (5 self)
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The Guided Local Search method has been successfully applied to a number of hard combinatorial optimisation problems from the wellknown TSP and QAP to real world problems such as Frequency Assignment and Workforce Scheduling. In this paper, we are demonstrating that the potential applications of GLS are not limited to optimisation problems of discrete nature but also to difficult continuous optimisation problems. Continuous optimisation problems arise in many engineering disciplines (such as electrical and mechanical engineering) in the context of analysis, design or simulation tasks. The problem examined gives an illustrative example of the behaviour of GLS, providing insights on the mechanisms of the algorithm. 1.
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 15 (8 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
Function Optimization using Guided Local Search
, 1995
"... In this report, we examine the potential use of Guided Local Search (GLS) for function optimization. In order to apply GLS, the function to be minimized is augmented with a set of penalty terms that enable local search to escape from local minima. The function F6 is used to demonstrate the proposed ..."
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Cited by 13 (3 self)
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In this report, we examine the potential use of Guided Local Search (GLS) for function optimization. In order to apply GLS, the function to be minimized is augmented with a set of penalty terms that enable local search to escape from local minima. The function F6 is used to demonstrate the proposed technique. 1. Introduction In this report, we present preliminary findings on the potential use of Guided Local Search (GLS) for function optimization. GLS is a metaheuristic for guiding local search [3] to escape local minima and visit promising solutions. GLS has been used to tackle difficult combinatorial optimization problems [7,5] and derives itself from the GENET network for constraint satisfaction problems [6]. Function optimization can be seen as a combinatorial problem by encoding real variables as binary strings [2]. In the simple case of binary encoding, binary string values are converted to integers which then are scaled by the appropriate coefficient to give real values in the...