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Enrique Alba

Bio: Enrique Alba is an academic researcher from University of Málaga. The author has contributed to research in topics: Metaheuristic & Evolutionary algorithm. The author has an hindex of 57, co-authored 530 publications receiving 14535 citations. Previous affiliations of Enrique Alba include ETSI & University of Waterloo.


Papers
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Journal ArticleDOI
TL;DR: A modern vision of the parallelization techniques used for evolutionary algorithms (EAs) and provides a highly structured background relating to PEAs to make researchers aware of the benefits of decentralizing and parallelizing an EA.
Abstract: This paper contains a modern vision of the parallelization techniques used for evolutionary algorithms (EAs). The work is motivated by two fundamental facts: 1) the different families of EAs have naturally converged in the last decade while parallel EAs (PEAs) are still lack of unified studies; and 2) there is a large number of improvements in these algorithms and in their parallelization that raise the need for a comprehensive survey. We stress the differences between the EA model and its parallel implementation throughout the paper. We discuss the advantages and drawbacks of PEAs. Also, successful applications are mentioned and open problems are identified. We propose potential solutions to these problems and classify the different ways in which recent results in theory and practice are helping to solve them. Finally, we provide a highly structured background relating to PEAs in order to make researchers aware of the benefits of decentralizing and parallelizing an EA.

810 citations

Book
01 Jan 2005
TL;DR: This chapter discusses Metaheuristics and Parallelism in Telecommunications, which has applications in Bioinformatics and Parallel Meta heuristics, and Theory of Parallel Genetic Algorithms, which focuses on the latter.
Abstract: Foreword. Preface Contributors. PART I: INTRODUCTION TO METAHEURISITICS AND PARALLELISM. 1. An Introduction to Metaheuristic Techniques (C. Blum, et al.). 2. Measuring the Performance of Parallel Metaheuristics (E. Alba & G. Luque). 3. New Technologies in Parallelism (E. Alba & A. Nebro). 4. Metaheuristics and Parallelism (E. Alba, et al.). PART II: PARALLEL METAHEURISTIC MODELS. 5. Parallel Genetic Algorithms (G. Luque, et al.). 6. Parallel Genetic Programming (F. Fernandez, et al.). 7. Parallel Evolution Strategies (G. Rudolph). 8. Parallel Ant Colony Algorithms (S. Janson, et al.). 9. Parallel Estimation of Distribution Algorithms (J. Madera, et al.). 10. Parallel Scatter Search (F. Garcia, et al.). 11. Parallel Variable Neighborhood Search (J. Moreno-Perez, et al.). 12. Parallel Simulated Annealing (M. Aydin, V. Yigit). 13. Parallel Tabu Search (T. Crainic, et al.). 14. Parallel Greedy Randomized Adaptive Search Procedures (M. Resende & C. Ribeiro). 15. Parallel Hybrid Metaheuristics (C. Cotta, et al.). 16. Parallel MultiObjective Optimization (A. Nebro, et al.). 17. Parallel Heterogeneous Metaheuristics (F. Luna, et al.). PART III: THEORY AND APPLICATIONS. 18. Theory of Parallel Genetic Algorithms (E. Cantu-Paz). 19. Parallel Metaheuristics Applications (T. Crainic & N. Hail). 20. Parallel Metaheuristics in Telecommunications (S. Nesmachnow, et al.). 21. Bioinformatics and Parallel Metaheuristics (O. Trelles, A. Rodriguez). Index.

592 citations

Proceedings ArticleDOI
15 May 2009
TL;DR: A new multi-objective particle swarm optimization algorithm characterized by the use of a strategy to limit the velocity of the particles, called Speed-constrained Multi-Objective PSO (SMPSO), which allows to produce new effective particle positions in those cases in which the velocity becomes too high.
Abstract: In this work, we present a new multi-objective particle swarm optimization algorithm (PSO) characterized by the use of a strategy to limit the velocity of the particles. The proposed approach, called Speed-constrained Multi-objective PSO (SMPSO) allows to produce new effective particle positions in those cases in which the velocity becomes too high. Other features of SMPSO include the use of polynomial mutation as a turbulence factor and an external archive to store the non-dominated solutions found during the search. Our proposed approach is compared with respect to five multi-objective metaheuristics representative of the state-of-the-art in the area. For the comparison, two different criteria are adopted: the quality of the resulting approximation sets and the convergence speed to the Pareto front. The experiments carried out indicate that SMPSO obtains remarkable results in terms of both, accuracy and speed.

563 citations

Journal ArticleDOI
TL;DR: This paper studies static and dynamic decentralized versions of the search model known as cellular genetic algorithm (cGA), in which individuals are located in a specific topology and interact only with their neighbors and concludes that dynamic cGAs have the most desirable behavior among all the evaluated ones in terms of efficiency and accuracy.
Abstract: This paper studies static and dynamic decentralized versions of the search model known as cellular genetic algorithm (cGA), in which individuals are located in a specific topology and interact only with their neighbors. Making changes in the shape of such topology or in the neighborhood may give birth to a high number of algorithmic variants. We perform these changes in a methodological way by tuning the concept of ratio. Since the relationship (ratio) between the topology and the neighborhood shape defines the search selection pressure, we propose to analyze in depth the influence of this ratio on the exploration/exploitation tradeoff. As we will see, it is difficult to decide which ratio is best suited for a given problem. Therefore, we introduce a preprogrammed change of this ratio during the evolution as a possible additional improvement that removes the need of specifying a single ratio. A later refinement will lead us to the first adaptive dynamic kind of cellular models to our knowledge. We conclude that these dynamic cGAs have the most desirable behavior among all the evaluated ones in terms of efficiency and accuracy; we validate our results on a set of seven different problems of considerable complexity in order to better sustain our conclusions.

408 citations

Journal ArticleDOI
TL;DR: This work makes a formalization of these algorithms, and a timely and topic survey of their most important traditional and recent technical issues, and presents a useful summaries on their main applications.
Abstract: In this work we review the most important existing developments and future trends in the class of Parallel Genetic Algorithms (PGAs) PGAs are mainly subdivided into coarse and fine grain PGAs, the coarse grain models being the most popular ones An exceptional characteristic of PGAs is that they are not just the parallel version of a sequential algorithm intended to provide speed gains Instead, they represent a new kind of meta-heuristics of higher efficiency and efficacy thanks to their structured population and parallel execution The good robustness of these algorithms on problems of high complexity has led to an increasing number of applications in the fields of artificial intelligence, numeric and combinatorial optimization, business, engineering, etc We make a formalization of these algorithms, and present a timely and topic survey of their most important traditional and recent technical issues Besides that, useful summaries on their main applications plus Internet pointers to important web sites are included in order to help new researchers to access this growing area

300 citations


Cited by
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Journal Article
TL;DR: This book by a teacher of statistics (as well as a consultant for "experimenters") is a comprehensive study of the philosophical background for the statistical design of experiment.
Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

13,333 citations

Journal ArticleDOI
TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.

10,082 citations

Journal ArticleDOI
TL;DR: Optimization results prove that the WOA algorithm is very competitive compared to the state-of-art meta-heuristic algorithms as well as conventional methods.

7,090 citations

Journal ArticleDOI
TL;DR: A detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far are presented.
Abstract: Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.

4,321 citations