SOLVING THE RADIO LINK FREQUENCY ASSIGNMENT PROBLEM WITH BOLTZMANN MACHINE

Neural network models that became well known and popular in the 80's have been successfully applied to solve tasks in several domains. These systems seem to offer fast and robust solutions for several difficult problems. The comparison of the results often shows similar achievements for neural networks and conventional methods. There is nothing surprising in it, if we consider that the different types of artificial neural systems accomplish the same or similar procedures as the different search algorithms and other methods. Most of the neural networks realize a modification of previously known algorithms on an intrinsic parallel system. Although the underlying methods are similar. the parallel structure and the nonlinear processing elements offer us a new, more efficient method. In this paper we present how to map a constraint satisfaction problem to achieve fast, optimal or near optimal solution. The application task, which has been solved, is the Radio Link Frequency Assignment Problem (RLFAP). In this problem we have to assign frequencies from a finite domain to several radio connections in such a way that the result should meet numerous constraints. The first section briefly describes the neural network model we have used to solve the problem. The second part introduces the RFLAP task in more detail. In the following two sections first we show a possible method to map the problem to the neural network and after this we present and evaluate the achieved results. In the fifth part we finish the paper with some conclusions

A genetic algorithm for the partial binary constraint satisfaction problem: an application to a frequency assignment problem

We describe a genetic algorithm for the partial constraint satisfaction problem. The typical elements of a genetic algorithm, selection, mutation and cross-over, are filled in with combinatorial ideas. For instance, cross-over of two solutions is performed by taking the one or two domain elements in the solutions of each of the variables as the complete domain of the variable. Then a branch-and-bound method is used for solving this small instance. When tested on a class of frequency assignment problems this genetic algorithm produced the best known solutions for all test problems. This feeds the idea that combinatorial ideas may well be useful in genetic algorithms.Economics ;

Maintaining Soft Arc Consistency in BnB-ADOPT+ During Search

Gutierrez and Meseguer show how to enforce consistency in BnB-ADOPT + for distributed constraint optimization, but they consider unconditional deletions only. However, during search, more values can be pruned conditionally according to variable instantiations that define subproblems. Enforcing consistency in these subproblems can cause further search space reduction. We introduce efficient methods to maintain soft arc consistencies in every subproblem during search, a non trivial task due to asynchronicity and induced overheads. Experimental results show substantial benefits on three different benchmarks. © 2013 Springer-Verlag.The work of Gutierrez and Meseguer was partially supported by the Spanish project TIN2009-13591-C02-02 and Generalitat de Catalunya 2009-SGR-1434.Peer Reviewe

Acyclic orientations with path constraints

Many well-known combinatorial optimization problems can be stated over the set of acyclic orientations of an undirected graph. For example, acyclic orientations with certain diameter constraints are closely related to the optimal solutions of the vertex coloring and frequency assignment problems. In this paper we introduce a linear programming formulation of acyclic orientations with path constraints, and discuss its use in the solution of the vertex coloring problem and some versions of the frequency assignment problem. A study of the polytope associated with the formulation is presented, including proofs of which constraints of the formulation are facet-defining and the introduction of new classes of valid inequalities