Results 1  10
of
23
Semiringbased CSPs and Valued CSPs: Frameworks, Properties, and Comparison
 Constraints
, 1999
"... In this paper we describe and compare two frameworks for constraint solving where classical CSPs, fuzzy CSPs, weighted CSPs, partial constraint satisfaction, and others can be easily cast. One is based on a semiring, and the other one on a totally ordered commutative monoid. While comparing the two ..."
Abstract

Cited by 114 (28 self)
 Add to MetaCart
In this paper we describe and compare two frameworks for constraint solving where classical CSPs, fuzzy CSPs, weighted CSPs, partial constraint satisfaction, and others can be easily cast. One is based on a semiring, and the other one on a totally ordered commutative monoid. While comparing the two approaches, we show how to pass from one to the other one, and we discuss when this is possible. The two frameworks have been independently introduced in [2], [3] and [35].
Radio Link Frequency Assignment
 Constraints
, 1999
"... The problem of radio frequency assignment is to provide communication channels from limited spectral resources whilst keeping to a minimum the interference suffered by those whishing to communicate in a given radio communication network. This problem is a combinatorial (NPhard) optimization problem ..."
Abstract

Cited by 70 (11 self)
 Add to MetaCart
The problem of radio frequency assignment is to provide communication channels from limited spectral resources whilst keeping to a minimum the interference suffered by those whishing to communicate in a given radio communication network. This problem is a combinatorial (NPhard) optimization problem. In 1993, the CELAR (the French "Centre d'Electronique de l'Armement") built a suite of simplified versions of Radio Link Frequency Assignment Problems (RLFAP) starting from data on a real network (Roisnel 93). Initially designed for assessing the performances of several Constraint Logic Programming languages, these benchmarks have been made available to the public in the framework of the European EUCLID project CALMA (Combinatorial Algorithms for Military Applications).
Abstracting Soft Constraints: Framework, Properties, Examples
 Artificial Intelligence
, 2002
"... Soft constraints are very exible and expressive. However, they also are very complex to handle. For this reason, it may be reasonable in several cases to pass to an abstract version of a given soft constraint problem, and then to bring some useful information from the abstract problem to the concret ..."
Abstract

Cited by 19 (9 self)
 Add to MetaCart
Soft constraints are very exible and expressive. However, they also are very complex to handle. For this reason, it may be reasonable in several cases to pass to an abstract version of a given soft constraint problem, and then to bring some useful information from the abstract problem to the concrete one. This will hopefully make the search for a solution, or for an optimal solution, of the concrete problem, faster.
Stochastic local search algorithms for the graph set Tcolouring . . .
 APPROXIMATION ALGORITHMS AND METAHEURISTICS; COMPUTER AND INFORMATION SCIENCE SERIES
, 2005
"... The graph set Tcolouring problem (GSTCP) generalises the classical graph colouring problem; it asks for the assignment of sets of integers to the vertices of a graph such that constraints on the separation of any two numbers assigned to a single vertex or to adjacent vertices are satisfied and some ..."
Abstract

Cited by 16 (3 self)
 Add to MetaCart
The graph set Tcolouring problem (GSTCP) generalises the classical graph colouring problem; it asks for the assignment of sets of integers to the vertices of a graph such that constraints on the separation of any two numbers assigned to a single vertex or to adjacent vertices are satisfied and some objective function is optimised. Among the objective functions of interest is the minimisation of the difference between the largest and the smallest integers used (the span). In this article, we present an experimental study of local search algorithms for solving general and large size instances of the GSTCP. We compare the performance of previously known as well as new algorithms covering both simple construction heuristics and elaborated stochastic local search algorithms. We investigate systematically different models and search strategies in the algorithms and determine the best choices for different types of instance. The study is an example of design of effective local search for constraint optimisation problems.
Bucket Elimination for Multiobjective Optimization Problems
 JOURNAL OF HEURISTICS
, 2006
"... Multiobjective optimization deals with problems involving multiple measures of performance that should be optimized simultaneously. In this paper we extend bucket elimination (BE), a well known dynamic programming generic algorithm, from monoobjective to multiobjective optimization. We show that t ..."
Abstract

Cited by 15 (2 self)
 Add to MetaCart
Multiobjective optimization deals with problems involving multiple measures of performance that should be optimized simultaneously. In this paper we extend bucket elimination (BE), a well known dynamic programming generic algorithm, from monoobjective to multiobjective optimization. We show that the resulting algorithm, MOBE, can be applied to true multiobjective problems as well as monoobjective problems with knapsack (or related) global constraints. We also extend minibucket elimination (MBE), the approximation form of BE, to multiobjective optimization. The new algorithm MOMBE can be used to obtain good quality multiobjective lower bounds or it can be integrated into multiobjective branch and bound in order to increase its pruning efficiency. Its accuracy is empirically evaluated in real scheduling problems, as well as in MaxSATONE and biobjective weighted minimum vertex cover problems.
Specializing Russian Doll Search
 In Proceedings of CP
, 2001
"... Russian Doll Search (RDS) is a clever procedure to solve overconstrained problems. RDS solves a sequence of nested subproblems, where two consecutive subproblems differ in one variable only. We present the Specialized RDS (SRDS) algorithm, which solves the current subproblem for each value of th ..."
Abstract

Cited by 12 (0 self)
 Add to MetaCart
Russian Doll Search (RDS) is a clever procedure to solve overconstrained problems. RDS solves a sequence of nested subproblems, where two consecutive subproblems differ in one variable only. We present the Specialized RDS (SRDS) algorithm, which solves the current subproblem for each value of the new variable with respect to the previous subproblem. The SRDS lower bound is superior to the RDS lower bound, which allows for a higher level of value pruning, although more work per node is required. Experimental results on random and real problems show that this extra work is often beneficial, providing substantial savings in the global computational effort.
Up and Down MiniBuckets: A Scheme for Approximating Combinatorial Optimization Tasks
, 2001
"... The paper addresses the problem of computing lower bounds on the optimal costs associated with each unary assignment of a value to a variable in combinatorial optimization problems. This task is instrumental in probabilistic reasoning and is also important for the development of admissible heu ..."
Abstract

Cited by 11 (5 self)
 Add to MetaCart
The paper addresses the problem of computing lower bounds on the optimal costs associated with each unary assignment of a value to a variable in combinatorial optimization problems. This task is instrumental in probabilistic reasoning and is also important for the development of admissible heuristic functions that can guide search algorithms for optimal solutions. The paper presents UDMB, a new algorithm that applies the minibucket elimination idea [Dechter and Rish, 1997] to accomplish this task. We show empirically that UDMB may achieve a substantial speed up over a bruteforce approximation method via minibuckets. 1 Introduction A Combinatorial optimization problem (COP) is defined by a finite set of variables, a set of finite domains and a set of cost functions. A solution is the optimal cost over the set of complete assignments of values to variables. It is well known that solving COP is NPhard. COP arise in a large variety of domains including artificial intelli...
Abstracting soft constraints
 PROC. 1999 ERCIM/COMPULOG NET WORKSHOP ON CONSTRAINTS, SPRINGER LNAI 1865
, 2000
"... We propose an abstraction scheme for soft constraint problems and we study its main properties. Processing the abstracted version of a soft constraint problem can help us in many ways: for example, to nd good approximations of the optimal solutions, or also to provide us with information that can m ..."
Abstract

Cited by 10 (6 self)
 Add to MetaCart
(Show Context)
We propose an abstraction scheme for soft constraint problems and we study its main properties. Processing the abstracted version of a soft constraint problem can help us in many ways: for example, to nd good approximations of the optimal solutions, or also to provide us with information that can make the subsequent search for the best solution easier. The results of this paper show that the proposed scheme is promising; thus they can be used as a stable formal base for any experimental work specific to a particular class of soft constraint problems.
An Abstraction Framework for Soft Constraints, And Its Relationship with Constraint Propagation
 In
, 2000
"... . Soft constraints are very flexible and expressive. However, they also are very complex to handle. For this reason, it may reasonable in several cases to pass to an abstract version of a given soft problem, and then to bring some useful information from the abstract problem to the concrete one. Thi ..."
Abstract

Cited by 10 (4 self)
 Add to MetaCart
(Show Context)
. Soft constraints are very flexible and expressive. However, they also are very complex to handle. For this reason, it may reasonable in several cases to pass to an abstract version of a given soft problem, and then to bring some useful information from the abstract problem to the concrete one. This will hopefully make the search for a solution, or for an optimal solution, of the concrete problem, faster. In this paper we review the main concepts and properties of our abstraction framework for soft constraints, and we show how it can be used to import constraint propagation algorithms from the abstract scenario to the concrete one. This may be useful when we don't have any (or any e#cient) propagation algorithm in the concrete setting. 1
Idwalk : A candidate list strategy with a simple diversification device
 CP 2004: Lecture Notes in Computer Science
, 2004
"... Abstract. This paper presents a new optimization metaheuristic called ID Walk (Intensification/Diversification Walk) that offers advantages for combining simplicity with effectiveness. In addition to the number S of moves, ID Walk uses only one parameter Max which is the maximum number of candidate ..."
Abstract

Cited by 9 (4 self)
 Add to MetaCart
(Show Context)
Abstract. This paper presents a new optimization metaheuristic called ID Walk (Intensification/Diversification Walk) that offers advantages for combining simplicity with effectiveness. In addition to the number S of moves, ID Walk uses only one parameter Max which is the maximum number of candidate neighbors studied in every move. This candidate list strategy manages the Max candidates so as to obtain a good tradeoff between intensification and diversification. A procedure has also been designed to tune the parameters automatically. We made experiments on several hard combinatorial optimization problems, and ID Walk compares favorably with correspondingly simple instances of leading metaheuristics, notably tabu search, simulated annealing and Metropolis. Thus, among algorithmic variants that are designed to be easy to program and implement, ID Walk has the potential to become an interesting alternative to such recognized approaches. Our automatic tuning tool has also allowed us to compare several variants of ID Walk and tabu search to analyze which devices (parameters) have the greatest impact on the computation time. A surprising result shows that the specific diversification mechanism embedded in ID Walk is very significant, which motivates examination of additional instances in this new class of “dynamic ” candidate list strategies. 1