Abstract. The mallba project tackles the resolution of combinatorial optimization problems using algorithmic skeletons implemented in C ++ . mallba offers three families of generic resolution methods: exact, heuristic and hybrid. Moreover, for each resolution method, mallba provides three different implementations: sequential, parallel for local area networks, and parallel for wide area networks (currently under development). This paper shows the architecture of the mallba library, presents some of its skeletons and offers several computational results to show the viability of the approach.

No abstract

The mallba project tackles the resolution of combinatorial optimization problems using algorithmic skeletons implemented in C ++ . mallba offers three families of generic optimization techniques: exact, heuristic and hybrid. Moreover, for each technique, mallba provides three different implementations: sequential, parallel for local area networks, and parallel for wide area networks. This paper explains the architecture of the mallba library, presents some of the implemented skeletons, and offers several computational results to show the viability of the approach. In our conclusions we claim that the design used to develop the optimization techniques is general and efficient at the same time, and also that the resulting skeletons can outperforms existing algorithms on a plethora of problems.

No abstract

The Frequency Assignment Problem (FAP) is one of the key issues in the design of GSM networks (Global System for Mobile communications), and will remain important in the foreseeable future. There are many versions of FAP, most of them benchmarking-like problems. We use a formulation of FAP, developed in published work, that focuses on aspects which are relevant for real-world GSM networks. In this paper, we have designed, adapted, and evaluated several types of metaheuristic for different time ranges. After a detailed statistical study, results indicate that these metaheuristics are very appropriate for this FAP. New interference results have been obtained, that significantly improve those published in previous research.

Abstract-The radio network design (RND) is an NP-hard optimization problem which consists of the maximization of the coverage of a given area while minimizing the base station deployment. Solving RND problems efficiently is relevant to many fields of application and has a direct impact in the engineering, telecommunication, scientific, and industrial areas. Numerous works can be found in the literature dealing with the RND problem, although they all suffer from the same shortfall: a noncomparable efficiency. Therefore, the aim of this paper is twofold: first, to offer a reliable RND comparison base reference in order to cover a wide algorithmic spectrum, and, second, to offer a comprehensible insight into accurate comparisons of efficiency, reliability, and swiftness of the different techniques applied to solve the RND problem. In order to achieve the first aim we propose a canonical RND problem formulation driven by two main directives: technology independence and a normalized comparison criterion. Following this, we have included an exhaustive behavior comparison between 14 different techniques. Finally, this paper indicates algorithmic trends and different patterns that can be observed through this analysis.

This paper presents a parallel framework for the solution of multi-objective optimization problems. The framework implements some of the best known multi-objective evolutionary algorithms. The plugin-based architecture of the framework minimizes the end user effort required to incorporate their own problems and evolutionary algorithms, and facilitates tool maintenance. A wide variety of configuration options can be specified to adapt the software behavior to many different parallel models. An innovation of the framework is that it provides a self-adaptive parallel model that is based on the cooperation of a set of evolutionary algorithms. The aim of the new model is to raise the level of generality at which most current evolutionary algorithms operate. This way, a wider range of problems can be tackled since the strengths of one algorithm can compensate for the weaknesses of another. The model proposed is a hybrid algorithm that combines a parallel island-based scheme with a hyperheuristic approach. The model grants more computational resources to those algorithms that show a more promising behavior. The flexibility and efficiency of the framework were tested and demonstrated by configuring standard and self-adaptive models for test problems and real-world applications.

No abstract