El-Ghazali Talbi

A Taxonomy of Hybrid Metaheuristics

Hybrid metaheuristics have received considerable interest these recent years in the field of combinatorial optimization. A wide variety of hybrid approaches have been proposed in the literature. In this paper, a taxonomy of hybrid metaheuristics is presented in an attempt to provide a common terminology and classification mechanisms. The taxonomy, while presented in terms of metaheuristics, is also applicable to most types of heuristics and exact optimization algorithms.As an illustration of the usefulness of the taxonomy an annoted bibliography is given which classifies a large number of hybrid approaches according to the taxonomy.

ParadisEO: A Framework for the Reusable Design of Parallel and Distributed Metaheuristics

In this paper, we present the ParadisEO white-box object-oriented framework dedicated to the reusable design of parallel and distributed metaheuristics (PDM). ParadisEO provides a broad range of features including evolutionary algorithms (EA), local searches (LS), the most common parallel and distributed models and hybridization mechanisms, etc. This high content and utility encourages its use at European level. ParadisEO is based on a clear conceptual separation of the solution methods from the problems they are intended to solve. This separation confers to the user a maximum code and design reuse. Furthermore, the fine-grained nature of the classes provided by the framework allow a higher flexibility compared to other frameworks. ParadisEO is of the rare frameworks that provide the most common parallel and distributed models. Their implementation is portable on distributed-memory machines as well as on shared-memory multiprocessors, as it uses standard libraries such as MPI, PVM and PThreads. The models can be exploited in a transparent way, one has just to instantiate their associated provided classes. Their experimentation on the radio network design real-world application demonstrate their efficiency.

Multi-objective vehicle routing problems

Routing problems, such as the traveling salesman problem and the vehicle routing problem, are widely studied both because of their classic academic appeal and their numerous real-life applications. Similarly, the field of multi-objective optimization is attracting more and more attention, notably because it offers new opportunities for defining problems. This article surveys the existing research related to multi-objective optimization in routing problems. It examines routing problems in terms of their definitions, their objectives, and the multi-objective algorithms proposed for solving them.

A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems

In this paper, we investigate the problem of scheduling precedence-constrained parallel applications on heterogeneous computing systems (HCSs) like cloud computing infrastructures. This kind of application was studied and used in many research works. Most of these works propose algorithms to minimize the completion time (makespan) without paying much attention to energy consumption. We propose a new parallel bi-objective hybrid genetic algorithm that takes into account, not only makespan, but also energy consumption. We particularly focus on the island parallel model and the multi-start parallel model. Our new method is based on dynamic voltage scaling (DVS) to minimize energy consumption. In terms of energy consumption, the obtained results show that our approach outperforms previous scheduling methods by a significant margin. In terms of completion time, the obtained schedules are also shorter than those of other algorithms. Furthermore, our study demonstrates the potential of DVS.

Parallel ant colonies for the quadratic assignment problem

Ant Colonies optimization take inspiration from the behavior of real ant colonies to solve optimization problems. This paper presents a parallel model for ant colonies to solve the quadratic assignment problem (QAP). The cooperation between simulated ants is provided by a pheromone matrix that plays the role of a global memory. The exploration of the search space is guided by the evolution of pheromones levels, while exploitation has been boosted by a tabu local search heuristic. Special care has also been taken in the design of a diversification phase, based on a frequency matrix. We give results that have been obtained on benchmarks from the QAP library. We show that they compare favorably with other algorithms dedicated for the QAP.

Hybridizing exact methods and metaheuristics: A taxonomy

The interest about hybrid optimization methods has grown for the last few years. Indeed, more and more papers about cooperation between heuristics and exact techniques are published. In this paper, we propose to extend an existing taxonomy for hybrid methods involving heuristic approaches in order to consider cooperative schemes between exact methods and metaheuristics. First, we propose some natural approaches for the different schemes of cooperation encountered, and we analyse, for each model, some examples taken from the literature. Then we recall and complement the proposed grammar and provide an annotated bibliography.

The "Ariadne's clew" algorithm: global planning with local methods

The goal of the work described is to build a path planner able to drive a robot in a dynamic environment where the obstacles are moving. In order to do so, the authors propose a method, called Ariadnes clew algorithm, to build a global path planner based on the combination of two local planning algorithms: an explore algorithm and a search algorithm. The purpose of the explore algorithm is to collect information about the environment with an increasingly fine resolution by placing landmarks in the searched space. The goal of the search algorithm is to opportunistically check if the target can be easily reached from any given placed landmark. The Ariadnes clew algorithm is shown to be very fast is most cases, allowing planning in dynamic environment. It is shown to be complete, which means that it is sure to find a path when one exists. A massively parallel implementation of this algorithm is described.

Gene selection in cancer classification using PSO/SVM and GA/SVM hybrid algorithms

In this work we compare the use of a particle swarm optimization (PSO) and a genetic algorithm (GA) (both augmented with support vector machines SVM) for the classification of high dimensional microarray data. Both algorithms are used for finding small samples of informative genes amongst thousands of them. A SVM classifier with 10- fold cross-validation is applied in order to validate and evaluate the provided solutions. A first contribution is to prove that PSOsvm is able to find interesting genes and to provide classification competitive performance. Specifically, a new version of PSO, called Geometric PSO, is empirically evaluated for the first time in this work using a binary representation in Hamming space. In this sense, a comparison of this approach with a new GAsvm and also with other existing methods of literature is provided. A second important contribution consists in the actual discovery of new and challenging results on six public datasets identifying significant in the development of a variety of cancers (leukemia, breast, colon, ovarian, prostate, and lung).

A multiobjective genetic algorithm for radio network optimization

Engineering of mobile telecommunication networks endures two major problems: the design of the network and the frequency assignment. We address the first problem in this paper, which has been formulated as a multiobjective constrained combinatorial optimisation problem. We propose a genetic algorithm (GA) that aims to approximate the Pareto frontier of the problem. Advanced techniques have been used, such as Pareto ranking, sharing and elitism. The GA has been implemented in parallel on a network of workstations to speed up the search. To evaluate the performance of the GA, we have introduced two new quantitative indicators: the entropy and the contribution. Encouraging results are obtained on real-life problems.

Parallel combinatorial optimization

Preface. Acknowledgments. Contributors. 1. Parallel Branch-and-Bound Algorithms (T. Crainic, B. Lecun, C. Roucairol). 2. Parallel Dynamic Programming (F. Almeida, D. Gonzalez, I. Pelaez). 3. Parallel Branch and Cut (T. Ralphs). 4. Parallel Semidefinite Programming and Combinatorial Optimization (S. J. Benson). 5. Parallel Resolution of the Satisfiability Problem: A Survey (D. Singer). 6. Parallel Metaheuristics: Algorithms and Frameworks (N. Melab, E-G. Talbi, S. Cahon, E. Alba, G. Luque). 7. Towards Parallel Design of Hybrids between Metaheuristics and Exact Methods (M. Basseur, L. Jourdan, E-G. Talbi). 8. Parallel Exact Methods for Multiobjective Combinatorial Optimization (C. Dhaenens, J. Lemesre, N. Melab, M. Mezmaz, E-G. Talbi). 9. Parallel Primal-Dual Interior Point Methods for Semidefinite Programs (M. Yamashita, K. Fujisawa, M. Fukuda, M. Kojima, K. Nakata). 10. MW: A Software Framework for Combinatorial Optimization on Computational Grids (W. Glankwamdee, T. Linderoth). 11. Constraint Logic Programming on Multiple Processors (I. Sakellariou, I. Vlahavas). 12. Application of Parallel Metaheuristics to Optimization Problems in Telecommunications and Bioinformatics (S. L. Martins, C. Ribeiro, I. Rosseti). Index.