Coromoto León

MALLBA: a library of skeletons for combinatorial optimisation

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 explains the architecture of the MALLBA library, presents some of its skeletons, and offers several computational results to show the viability of the approach.

Efficient parallel LAN/WAN algorithms for optimization: the MALLBA project

The MALLBA project tackles the resolution of combinatorial optimization problems using generic algorithmic skeletons implemented in C++. A skeleton in the MALLBA library implements an optimization method in one of the three families of generic optimization techniques offered: exact, heuristic and hybrid. Moreover, for each of those methods, MALLBA provides three different implementations: sequential, parallel for Local Area Networks, and parallel for Wide Area Networks. This paper introduces the architecture of the MALLBA library, details some of the implemented skeletons, and offers computational results for some classical optimization problems to show the viability of our library. Among other conclusions, we claim that the design used to develop the optimization techniques included in the library is generic and efficient at the same time.

Using multi-objective evolutionary algorithms for single-objective optimization

In recent decades, several multi-objective evolutionary algorithms have been successfully applied to a wide variety of multi-objective optimization problems. Along the way, several new concepts, paradigms and methods have emerged. Additionally, some authors have claimed that the application of multi-objective approaches might be useful even in single-objective optimization. Thus, several guidelines for solving single-objective optimization problems using multi-objective methods have been proposed. This paper offers a survey of the main methods that allow the use of multi-objective schemes for single-objective optimization. In addition, several open topics and some possible paths of future work in this area are identified.

A Parallel Plugin-Based Framework for Multi-objective Optimization

This work 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 framework architecture makes usage of configuration files to provide a more extensive and simple customization environment than other similar tools. A wide variety of configuration options can be specified to adapt the software behaviour to many different parallel models, including a new adaptive model which dynamically grants more computational resources to the most promising algorithms. The plugin-based architecture of the framework minimizes the final user effort required to incorporate their own problems and evolutionary algorithms, and facilitates the tool maintenance. The flexibility of the approach has been tested by configuring a standard homogeneous island-based model and a self-adaptive model. The computational results obtained for problems with different granularity demonstrate the efficiency of the provided parallel implementation.

Optimization Techniques for Solving Complex Problems

Solving Complex Problems addresses real problems and the modern optimization techniques used to solve them. Thorough examples illustrate the applications themselves, as well as the actual performance of the algorithms. Application areas include computer science, engineering, transportation, telecommunications, and bioinformatics, making the book especially useful to practitioners in those areas.

Benchmarking a Wide Spectrum of Metaheuristic Techniques for the Radio Network Design Problem

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.

Metaheuristics for solving a real-world frequency assignment problem in GSM networks

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.

Optimization algorithms for large-scale real-world instances of the frequency assignment problem

Nowadays, mobile communications are experiencing a strong growth, being more and more indispensable. One of the key issues in the design of mobile networks is the frequency assignment problem (FAP). This problem is crucial at present and will remain important in the foreseeable future. Real-world instances of FAP typically involve very large networks, which can be handled only by heuristic methods. In the present work, we are interested in optimizing frequency assignments for problems described in a mathematical formalism that incorporates actual interference information, measured directly on the field, as is done in current GSM networks. To achieve this goal, a range of metaheuristics have been designed, adapted, and rigourously compared on two actual GSM networks modeled according to the latter formalism. To generate quickly and reliably high-quality solutions, all metaheuristics combine their global search capabilities with a local-search method specially tailored for this domain. The experiments and statistical tests show that in general, all metaheuristics are able to improve upon results published in previous studies, but two of the metaheuristics emerge as the best performers: a population-based algorithm (Scatter Search) and a trajectory based (1+1) Evolutionary Algorithm. Finally, the analysis of the frequency plans obtained offers insight about how the interference cost is reduced in the optimal plans.

Parallel island-based multiobjectivised memetic algorithms for a 2D packing problem

Bin Packing problems are NP-hard problems with many practical applications. A variant of a Bin Packing Problem was proposed in the GECCO 2008 competition session. The best results were achieved by a mono-objective Memetic Algorithm (MA). In order to reduce the execution time, it was parallelised using an island-based model. High quality results were obtained for the proposed instance. However, subsequent studies concluded that stagnation may occur for other instances. The term multiobjectivisation refers to the transformation of originally mono-objective problems as multi-objective ones. Its main aim is to avoid local optima. In this work, a multiobjectivised MA has been applied to the gecco 2008 Bin Packing Problem. Several multiobjectivisation schemes, which use problem-dependent and problem-independent information have been tested. Also, a parallelisation of the multiobjectivised MA has been developed. Results have been compared with the best up to date mono-objective approaches. Computational results have demonstrated the validity of the proposals. They have provided benefits in terms of solution quality, and in terms of time saving.

Using multi-objective evolutionary algorithms for single-objective constrained and unconstrained optimization

In recent decades, several multi-objective evolutionary algorithms have been successfully applied to a wide variety of multi-objective optimization problems. Along the way, several new concepts, paradigms and methods have emerged. Additionally, some authors have claimed that the application of multi-objective approaches might be useful even in single-objective optimization. Thus, several guidelines for solving single-objective optimization problems using multi-objective methods have been proposed. This paper offers an updated survey of the main methods that allow the use of multi-objective schemes for single-objective optimization. In addition, several open topics and some possible paths of future work in this area are identified.