Nature-inspired cooperative strategies for optimization

Natalio Krasnogor, Marı́a Belén Melián-Batista, José Andrés Moreno-Pérez, J. Marcos Moreno-Vega, and David Alejandro Pelta (Eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2008) Studies in Computational Intelligence, Volume 236 Editor-in-Chief Prof. Janusz Kacprzyk Systems Research Institute Polish Academy of Sciences ul. Newelska 6 01-447 Warsaw Poland E-mail: [email protected] Further volumes of this series can be found on our homepage: springer.com Vol. 215. Habib M. Ammari Opportunities and Challenges of Connected k-Covered Wireless Sensor Networks, 2009 ISBN 978-3-642-01876-3 Vol. 226. Ernesto Damiani, Jechang Jeong, Robert J. Howlett, and Lakhmi C. Jain (Eds.) New Directions in Intelligent Interactive Multimedia Systems and Services - 2, 2009 ISBN 978-3-642-02936-3 Vol. 216. Matthew Taylor Transfer in Reinforcement Learning Domains, 2009 ISBN 978-3-642-01881-7 Vol. 227. Jeng-Shyang Pan, Hsiang-Cheh Huang, and Lakhmi C. Jain (Eds.) 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Marcos Moreno-Vega, and David Alejandro Pelta (Eds.) Nature Inspired Cooperative Strategies for Optimization (NICSO 2008) 123 Natalio Krasnogor J. Marcos Moreno-Vega School of Computer Sciences and Information Technology Jubilee Campus University of Nottingham Nottingham, NG81BB, UK E-mail: Natalio.Krasnogor@ nottingham.ac.uk DEIOC, Facultad de Matemáticas Universidad de La Laguna 38271 La Laguna, Tenerife Spain E-mail: [email protected] Marı́a Belén Melián-Batista Dpto. Estadı́stica, I.O. y Computación Facultad de Matemáticas y Fı́sica 4a¯ Planta Universidad de La Laguna Campus de Anchieta, s/n 38206 La Laguna, Tenerife, Spain E-mail: [email protected] David Alejandro Pelta Department of Computer Science and Artificial Intelligence E.T.S.I Informática y de Telecomunicación C/ Periodista Daniel Saucedo Aranda s/n University of Granada 18071 Granada, Spain E-mail: [email protected] José Andrés Moreno Pérez DEIOC, Facultad de Matemáticas Universidad de La Laguna 38271 La Laguna, Tenerife, Spain E-mail: [email protected] ISBN 978-3-642-03210-3 e-ISBN 978-3-642-03211-0 DOI 10.1007/978-3-642-03211-0 Studies in Computational Intelligence ISSN 1860-949X Library of Congress Control Number: Applied for c 2009 Springer-Verlag Berlin Heidelberg  This work is subject to copyright. 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Printed in acid-free paper 987654321 springer.com Preface The inspiration from Biology and the Natural Evolution process has become a research area within computer science. For instance, the description of the artificial neuron given by McCulloch and Pitts was inspired from biological observations of neural mechanisms; the power of evolution in nature in the diverse species that make up our world has been related to a particular form of problem solving based on the idea of survival of the fittest; similarly, artificial immune systems, ant colony optimisation, automated self-assembling programming, membrane computing, etc. also have their roots in natural phenomena. The first and second editions of the International Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO), were held in Granada, Spain, 2006, and in Acireale, Italy, 2007, respectively. As in these two previous editions, the aim of NICSO 2008, held in Tenerife, Spain, was to provide a forum were the latest ideas and state of the art research related to nature inspired cooperative strategies for problem solving were discussed. The contributions collected in this book were strictly peer reviewed by at least three members of the international programme committee, to whom we are indebted for their support and assistance. The topics covered by the contributions include nature-inspired techniques like Genetic Algorithms, Ant Colonies, Amorphous Computing, Artificial Immune Systems, Evolutionary Robotics, Evolvable Systems, Membrane Computing, Quantum Computing, Software Self Assembly, Swarm Intelligence, etc. NICSO 2008 had three plenary lectures given by Prof. Maurice Clerc, Three questions about Particle Swarm Optimisation (PSO), Günther Raidl, Cooperative Hybrids for Combinatorial Optimization, and Thomas Stützle, Ant Colony: A Review. As Workshop Chairs we wish to thank the support given by several people and institutions. We want to thank the University of La Laguna, the Canary Government (TF129) and the Spanish Government (TIN2008-00667-E) for their financial support. D. José A. Moreno Pérez also acknowledges support from projects TIN2005-08404-C04-03, TIN2008-06872-C04-01 (Spanish VI Preface Government). Belén Melián and J. Marcos Moreno-Vega acknowledge support from project PI2007/019 (Canary Government). D. Pelta acknowledges the support from project P07-TIC02970 (Andalusian Government). Our experience with NICSO 2006, 2007 and 2008 demonstrates that there is an emerging and thriving community of scholars doing research on Nature Inspired Cooperative Strategies for Optimization. It is to these scholars, both authors and reviewers, to whom the organisers are indebted for the success of the NICSO series. November 2008 Natalio Krasnogor UK Belén Melián Spain José A. Moreno Spain J. Marcos Moreno-Vega Spain David Pelta Spain Organization Workshop Co-chairs Natalio Krasnogor Belén Melián José A. Moreno J. Marcos Moreno-Vega David A. Pelta University University University University University of Nottingham, UK of La Laguna, Spain of La Laguna, Spain of La Laguna, Spain of Granada, Spain University University University University University University University University University University of of of of of of of of of of Organizing Committee J. David Beltrán Julio Brito Clara Campos Juan Pedro Castro José Luis González Ávila F. Javier Martínez Belén Melián José A. Moreno J. Marcos Moreno-Vega Jonatan Ramos Bonilla La Laguna, Spain La Laguna, Spain La Laguna, Spain Nottingham, UK La Laguna, Spain La Laguna, Spain La Laguna, Spain La Laguna, Spain La Laguna, Spain La Laguna, Spain Program Committee Enrique Alba Torres Francisco Almeida Davide Anguita Cecilio Angulo Paolo Arena Jaume Bacardit Roberto Battiti University of Malaga, Spain University of La Laguna, Spain University of Genova, Italy Technical University of Catalunya, Spain University of Catania, Italy University of Nottingham, UK University of Trento, Italy VIII José Manuel Cadenas José Alejandro Castillo Carlos Coello Coello Emilio Corchado Vincenzo Cutello Marco Dorigo Gianluigi Folino Xiao-Zhi Gao Blas Galván Ignacio José García del Amo Marian Gheorghe Jean-Louis Giavitto Steven Gustafson Francisco Herrera Oliver Korb Natalio Krasnogor María Teresa Lamata Evelyne Lutton Vincenzo Manca Max Manfrin Vittorio Maniezzo Juan José Merelo Belén Melián José A. Moreno J. Marcos Moreno-Vega Gabriela Ochoa Gheorghe Paun Mario Pavone David A. Pelta Stefano Pizzuti Vitorino Ramos Emmanuel Sapin Giuseppe Scollo James Smaldon Jim Smith Thomas Stibor German Terrazas Jon Timmis José Luis Verdegay Pawel Widera Gabriel Winter Organization University of Murcia, Spain ININ, Mexico CINVESTAV-IPN, Mexico University of Burgos, Spain University of Catania, Spain Université Libre de Bruxelles, Belgium ICAR, Italy Helsinki University of Technology, Finland University of Las Palmas de G.C., Spain University of Granada, Spain University of Sheffield, UK Université d’Evry, France General Electric , USA University of Granada, Spain Universität Konstanz, Germany University of Nottingham, UK University of Granada, Spain INRIA, France University of Verona, Italy Université Libre de Bruxelles, Belgium University of Bologna, Italy University of Granada, Spain University of La Laguna, Spain University of La Laguna, Spain University of La Laguna, Spain University of Nottingham, UK Institute of Math. of the Romanian Academy University of Catania, Italy University of Granada, Spain ENEA, Italy Technical University of Lisbon, Portugal University of the West of England, UK University of Catania, Italy University of Nottingham, UK University of the West of England, UK Technische Universität Darmstad, Germany University of Nottingham, UK University of York, UK University of Granada, Spain University of Nottingham, UK University of Las Palmas de G.C., Spain Plenary Lectures Maurice Clerc Three questions about Particle Swarm Optimisation (PSO) PSO is now a well known 13 years old seriously researched method. So, in this talk, instead of presenting the algorithm or its obvious applications, I will focus on a few questions, which I hope the audience will find interesting. Is it possible to get rid of all tuning-dependent parameters? (The results still need to be acceptable, of course) Any PSO variant with suggested default values for the user-defined parameters can be seen as a “parameter-less” one ... if the user modifies nothing. For example, in Standard PSO 2007 the suggested values have been estimated by mathematical analysis. Just using those values usually leads to reasonable results. On the other hand, a variant like TRIBES is explicitly designed so that the user need not do anything except define the problem: all “parameters”, including neighbourhood topology and strategies to use are modified during the run. However, such a completely adaptive approach is slow. As a compromise, several variants have been suggested that lie in between, containing a few user-defined parameters, and a few adaptation rules. PSO was originally designed for continuous problems. My problem is a binary one. How can I adapt the algorithm? There are now a lot of “binary” PSOs, designed after the variant suggested by Kennedy and Eberhart. However, all of those perform very well on some problems and very poorly on some others. So, a possible robust approach is to combine two of them. For example, one combination that works on many problems is the following. Randomly divide the set of particles into two sets. For the first set, simply consider the binary problem like a one dimensional quasi-continuous one. For the second set, use a variant of the pivot method, i.e. just look “around” a good position. X Plenary Lectures On some problems, PSO completely fails. Why? Classical PSO can get trapped into a local minimum, or may even prematurely converge to an uninteresting point. These phenomena have been well studied, and several solutions have been found, like using probability distributions with infinite supports (Gaussian, Cauchy, Levy, etc.), defining a stop/restart strategy or generating new particles. Here, I discuss something not so well known: the class of problems for which “nearer is better” in probability. PSO works well on this class, which seems to contain most, if not all, practical problems. More generally, this notion is useful in explaining the behaviour of a lot of stochastic optimisation algorithms, so it is worthy of a careful analysis. Furthermore, it helps to explain certain interesting points; for example, why using less information can lead to better result, or why most algorithms are centre biased (which is not necessarily a bad thing). Günther Raidl Institute of Computer Graphics and Algorithm Vienna University of Technology Vienna, Austria Cooperative Hybrids for Combinatorial Optimization We consider approaches for (approximately) solving combinatorial optimization problems that are based on nature inspired components and collaboration among different subsystems. Classical, pure nature inspired optimization techniques, such es genetic algorithms or ant colony optimization, are said to be robust methods yielding reasonably good solutions for a large spectrum of applications. Especially on many combinatorial problems, however, these rather simple algorithms often have their limits and cannot compete with more sophisticated state-of-the-art approaches that exploit problem-specific knowledge in better ways. Frequently, nature inspired strategies are therefore combined with other techniques to cooperative hybrid systems. The aim is to exploit the individual advantages of the different approaches, yielding a better overall system, thus, to benefit from synergy. This talk gives a survey on such approaches and illustrates the various concepts by referring to concrete examples. Especially, we will consider simple sequential combinations, asynchronous teams (A-Teams), multi-agent approaches as TECHS, and selected more complex combinations of nature inspired approaches with integer linear programming methods. Plenary Lectures XI Thomas Stützle Institut de Recherches Interdisciplinaries et de Développements en Intelligence Artificielle (IRIDIA) Université Libre de Bruxelles (ULB) Brussels, Belgium Ant Colony Optimization: A Review Ant Colony Optimization (ACO) is swarm intelligence technique that has been inspired by the foraging behavior of some ant species. Since it was proposed in 1991, it has attracted a large number of researchers and in the meantime it has reached a significant level of maturity. In fact, ACO is now a well-established, nature-inspired technique for tackling a wide variety of computationally hard problems. This talk will give a review of past and current developments in ACO. It will start with an explanation of the inspiring source of ACO and the steps taken in the development of the main variants of ACO algorithms. We will then consider several of the most important recent developments. In particular, we will shortly review the main application areas of ACO, highlight recent developments on the algorithmic side including hybrids with other algorithmic techniques, and give an overview of the current status of theoretical results on ACO algorithms. Dario Floreano Laboratory of Intelligent Systems (LIS) Ecole Polytechnique Federale Lausanne (EPFL) Switzerland Artificial Evolution of Truly Cooperative Robots Cooperation is widely spread in nature and takes several forms, ranging from behavioral coordination to sacrifice of one’s own life for the benefit of the society. This latter form of cooperation is known as “true cooperation”, or “altruism”, and is found only in few cases in nature. Truly cooperative robots would be very useful in conditions where unpredictable events in the mission may require a cost by one or more individual robots for the success of the entire mission. However, the interactions among robots sharing the same environment can affect in unexpected ways the behavior of individual robots, making very difficult the design of rules that produce stable cooperative behavior. XII Plenary Lectures It is thus interesting to examine under which conditions stable cooperative behavior evolves in nature and how those conditions can be translated into evolutionary algorithms that are applicable to a wide range of robots. In this talk I will quickly review biological theories of evolution of cooperative behavior and focus on the theories of kin selection and group selection. I will show how these two theories can be mapped into different evolutionary algorithms and compare their efficiency in producing control systems for a swarm of sugar-cube robots in a number of cooperative tasks that vary in the degree of requested cooperation. I will then describe an example where the most efficient algorithm is used to evolve a control system for a swarm of aerial robots that must establish a radio network between persons on the ground. In another set of experiments I describe how those evolutionary conditions can be tested for the emergence of communication where colonies of “expressive” robots are exposed to food and danger sources that cannot be uniquely be identified at distance. Here, communication of the source type brings an advantage to the colony at the expense of the individuals that decide to tell which is the food or poison. The results shed light on the conditions that may have favored the evolution of altruistic cooperation and communication. Finally, I will describe work in progress for a real-world application of a swarm of flying robots that are expected to locate and establish an ad hoc radio network among rescuers deployed in a catastrophic scenario. The stringent mission requirements along with the unpredictable location of the rescuers on the ground made it very difficult to come up with suitable control rules. We solved the problem by using the evolutionary methods that we distilled from the previously described research in order to come up with efficient and extremely simple control systems that satisfy the basic mission requirements. Work performed in collaboration with Sara Mitri (LIS-EPFL), Sabine Hauert (LIS-EPFL), Severin Leven (LIS-EPFL), and Laurent Keller (Department of Evolutionary Biology, University of Lausanne). Contents 1 2 Exploration in Stochastic Algorithms: An Application on MAX–MIN Ant System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Paola Pellegrini, Daniela Favaretto, Elena Moretti 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 The Relevance of Understanding Exploration in Stochastic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Exploration: A Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 The Case of Ant Colony Optimization . . . . . . . . . . . . . . . . . . 1.5 Experimental Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sensitive Ants: Inducing Diversity in the Colony . . . . . . . . . C.-M. Pintea, C. Chira, D. Dumitrescu 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Ant Colony Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Inducing Heterogeneity in Ant Systems . . . . . . . . . . . . . . . . . 2.4 The Sensitive Ant Search Model . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 Renormalized Transition Probabilities in SAM . . . . 2.4.2 Virtual State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.3 Heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.4 Virtual State Decision Rule . . . . . . . . . . . . . . . . . . . . 2.5 Solving TSP Using SAM: Numerical Results and Comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.1 SAM Algorithm for Solving TSP . . . . . . . . . . . . . . . . 2.5.2 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1 2 3 4 5 10 11 15 15 16 17 18 18 19 19 20 20 20 21 23 24 XIV 3 4 5 Contents Decentralised Communication and Connectivity in Ant Trail Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Duncan E. Jackson, Mesude Bicak, Mike Holcombe 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Methodology and Rationale . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Model Parameters and Scale . . . . . . . . . . . . . . . . . . . 3.2.3 Process Overview and Scheduling . . . . . . . . . . . . . . . 3.2.4 Initialisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Experimental Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Homogeneous vs. Heterogeneous U-Turning Populations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Detection of Non-structured Roads Using Visible and Infrared Images and an Ant Colony Optimization Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rafael Arnay, Leopoldo Acosta, Marta Sigut, Jonay T. Toledo 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Vehicle Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Applying an Ant Colony Optimization . . . . . . . . . . . . . . . . . . 4.3.1 Colony Initialization . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.2 The Point of Attraction . . . . . . . . . . . . . . . . . . . . . . . . 4.3.3 Movement Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.4 Pheromone Update . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.5 Agent Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.6 Solution Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.7 Road Pattern Update . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Information Complementation between Thermal Vision and the Visible Spectrum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Nature Inspired Approach for the Uncapacitated Plant Cycle Location Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . Belén Melián-Batista, J. Marcos Moreno-Vega, Nitesh Vaswani, Rayco Yumar 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Uncapacitated Plant-Cycle Location Problem . . . . . . . . . . . . 5.2.1 Mathematical Model . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Solution Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 Application of the HBMO to the UPCLP . . . . . . . . 25 25 28 28 28 31 31 31 31 32 33 35 37 37 38 41 42 43 43 43 43 44 44 46 46 47 49 49 50 50 52 55 Contents 5.4 Computational Experience . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 7 8 Particle Swarm Topologies for Resource Constrained Project Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jens Czogalla, Andreas Fink 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 The Resource Constrained Project Scheduling Problem . . . 6.3 Discrete Particle Swarm Optimization with Different Population Topologies for the RCPSP . . . . . . . . . . . . . . . . . . 6.4 Computational Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discrete Particle Swarm Optimization Algorithm for Data Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . R. Karthi, S. Arumugam, K. Ramesh Kumar 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Data Clustering Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 General Structure of Proposed DPSOA Algorithm . . . . . . . . 7.3.1 Definition of Particle . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.2 Initialization of Particles in DPSC Algorithm . . . . . 7.3.3 Generation of Velocity of Particles . . . . . . . . . . . . . . 7.3.4 Construction of a Particle Sequence . . . . . . . . . . . . . 7.3.5 Search Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Performance Analysis of DPSOA Algorithm . . . . . . . . . . . . . 7.4.1 Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . 7.4.2 Run Length Distribution (RLD) . . . . . . . . . . . . . . . . 7.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A Simple Distributed Particle Swarm Optimization for Dynamic and Noisy Environments . . . . . . . . . . . . . . . . . . . . . . . Xiaohui Cui, Jesse St. Charles, Thomas E. Potok 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Classic Particle Swarm Models . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Related Work in PSO for Dynamic Environment . . . . . . . . . 8.4 Simple Distributed PSO Approach . . . . . . . . . . . . . . . . . . . . . 8.5 Dynamic Environment Generator . . . . . . . . . . . . . . . . . . . . . . . 8.5.1 Environment Landscape . . . . . . . . . . . . . . . . . . . . . . . 8.5.2 Dynamics Generator . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5.3 Measurement for Tracking Optimum Result . . . . . . 8.6 Experiment Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.7 Experiment Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XV 56 59 59 61 61 62 62 67 71 71 75 75 76 78 79 80 80 81 82 84 84 86 87 88 89 89 90 92 93 94 94 94 95 96 97 XVI Contents 8.8 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 9 Exploring Feasible and Infeasible Regions in the Vehicle Routing Problem with Time Windows Using a Multi-objective Particle Swarm Optimization Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Juan P. Castro, Dario Landa-Silva, José A. Moreno Pérez 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2 Particle Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.1 Multi-objective Particle Swarm Optimization . . . . . 9.2.2 Discrete Particle Swarm Optimization . . . . . . . . . . . 9.2.3 Jumping Frog Optimization . . . . . . . . . . . . . . . . . . . . 9.3 Proposed MOJFO Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 9.3.1 Solution Representation and Initilization . . . . . . . . . 9.3.2 Constraints and Objectives . . . . . . . . . . . . . . . . . . . . . 9.4 Experimental Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Two-Swarm PSO for Competitive Location Problems . . . . Clara M. Campos Rodrı́guez, José A. Moreno Pérez, Hartmut Noltemeier, Dolores R. Santos Peñate 10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 Multiple Competitive Location Problems . . . . . . . . . . . . . . . . 10.3 The Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4 Particle Swarm Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . 10.5 The Two-Swarm PSO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.6 Analysis of the Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Aerodynamic Wing Optimisation Using SOMA Evolutionary Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Miroslav Červenka, Vojtěch Křesálek 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Self-Organising Migrating Algorithm . . . . . . . . . . . . . . . . . . . . 11.2.1 Parameters and Terminology . . . . . . . . . . . . . . . . . . . 11.2.2 Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2.3 Mutation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2.4 Crossover/Migration . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3 Wing Optimisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3.1 Optimised Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.4 Experiments and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 103 104 105 106 107 107 108 109 110 112 113 115 116 117 118 119 121 124 125 125 127 127 128 129 130 130 131 131 132 134 Contents XVII 11.4.1 VUT-100 Cobra Wing Optimisation . . . . . . . . . . . . . 135 11.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 12 Experimental Analysis of a Variable Size Monopopulation Cooperative-Coevolution Strategy . . . . . . . . . . . . Olivier Barrière, Evelyne Lutton 12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2 Cooperative-Coevolution Learning on Agrifood Data . . . . . . 12.2.1 The Test-Problem: Phase Estimation of a Camembert-Cheese Ripening Process . . . . . . . . . . . . 12.2.2 Phase Estimation Using a Parisian GP . . . . . . . . . . 12.3 Variable Size Population Strategies . . . . . . . . . . . . . . . . . . . . . 12.3.1 Population Size Decrease Scheme . . . . . . . . . . . . . . . 12.3.2 Partial Restart Scheme: Deflating and Inflating the Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.4 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.4.1 Experimental Protocol . . . . . . . . . . . . . . . . . . . . . . . . . 12.4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.5 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Genetic Algorithm for Tardiness Minimization in Flowshop with Blocking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tiago de O. Januario, José Elias C. Arroyo, Mayron César O. Moreira 13.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2 Proposed Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2.1 Representation of a Solution and Generation of the Initial Population . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2.2 Selection, Crossover and Mutation . . . . . . . . . . . . . . 13.2.3 Local Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2.4 Diversification of the Population . . . . . . . . . . . . . . . . 13.2.5 Post-optimization Using Path Relinking . . . . . . . . . 13.2.6 Steps of the Genetic Algorithm . . . . . . . . . . . . . . . . . 13.3 Numerical Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.3.1 Computational Results . . . . . . . . . . . . . . . . . . . . . . . . 13.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Landscape Mapping by Multi-population Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yuebin B. Guo, Kwok Yip Szeto 14.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139 139 141 141 142 144 144 146 147 147 148 149 150 153 153 154 155 156 157 157 157 158 159 159 162 163 165 166 167 168 XVIII Contents 14.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 15 An Interactive Simulated Annealing Multi-agents Platform to Solve Hierarchical Scheduling Problems with Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Souhail Dhouib, Sana Kouraı̈chi, Taı̈cir loukil, Habib Chabchoub 15.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2 Taboo Central Memory (TCM) . . . . . . . . . . . . . . . . . . . . . . . . 15.3 Multi-agents Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.4 Interactive Simulated Annealing Multi-agents (ISAM) Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.5 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.5.1 ISAM Platform to Solve Lexicographic Goal Programming Problems . . . . . . . . . . . . . . . . . . . . . . . . 15.5.2 ISAM Platform to Solve Single Machine Total Weighted Tardiness (SMTWT) Problems . . . . . . . . 15.5.3 ISAM Platform to Solve Hierarchical Multicriteria Scheduling Problems . . . . . . . . . . . . . . . 15.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Genetic Algorithm and Advanced Tournament Selection Concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Radomil Matoušek 16.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.2 Standard and Elite Tournament Selection . . . . . . . . . . . . . . . 16.3 Probabilities of Selections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.3.1 Probability of Tournament Selection . . . . . . . . . . . . . 16.3.2 Probability of Elite Tournament Selection . . . . . . . . 16.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Terrain-Based Memetic Algorithms for Vector Quantizer Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Carlos R.B. Azevedo, Flávia E.A.G. Azevedo, Waslon T.A. Lopes, Francisco Madeiro 17.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.2 Vector Quantization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.2.1 The K-Means Algorithm . . . . . . . . . . . . . . . . . . . . . . . 17.2.2 The Accelerated K-Means . . . . . . . . . . . . . . . . . . . . . 17.3 Adaptation in Evolutionary Algorithms . . . . . . . . . . . . . . . . . 17.3.1 Adaptation and Spatially Distributed EAs . . . . . . . 17.4 Proposed Terrain-Based Memetic Algorithms . . . . . . . . . . . . 17.4.1 Stationary TBMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17.4.2 Motioner TBMA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 178 179 180 181 182 182 183 185 186 187 189 189 190 192 193 194 196 196 197 197 198 199 199 200 200 202 203 203 Contents XIX 17.5 Simulation Results and Discussion . . . . . . . . . . . . . . . . . . . . . . 17.6 Conclusion and Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 208 209 209 18 Cooperating Classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Magnus Jändel 18.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.2 Setting the Stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.3 Back-Propagation Neural Networks . . . . . . . . . . . . . . . . . . . . . 18.3.1 Brain Cloning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.4 Decision Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.4.1 Brain Surgery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.5 Support Vector Machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.5.1 Hallucinations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.6 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18.7 Discussion and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 19 Evolutionary Multimodal Optimization for Nash Equilibria Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rodica Ioana Lung, Dan Dumitrescu 19.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.2 Theoretical Aspects Related to the Computation of Nash Equilibira . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.3 Evolutionary Multimodal Optimization . . . . . . . . . . . . . . . . . 19.4 Deflection Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.5 Roaming Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.5.1 Roaming Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.6 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.6.1 Test Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.6.2 Experimental Set-Up . . . . . . . . . . . . . . . . . . . . . . . . . . 19.6.3 Dealing with Constraints . . . . . . . . . . . . . . . . . . . . . . . 19.6.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 On the Computational Properties of the MultiObjective Neural Estimation of Distribution Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Luis Martı́, Jesús Garcı́a, Antonio Berlanga, José M. Molina 20.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.2 Theoretical Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.3 Multi–objective Neural EDA . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.3.1 Model–Building with Growing Neural Gas . . . . . . . 20.3.2 MONEDA Algorithmics . . . . . . . . . . . . . . . . . . . . . . . 213 215 216 216 217 218 218 219 220 223 224 227 227 228 229 230 231 231 233 233 234 234 235 236 237 239 239 241 242 242 244 XX Contents 20.4 Measuring Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.5 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20.6 Final Remarks and Future Work . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245 245 249 250 21 Optimal Time Delay in the Control of Epidemic . . . . . . . . . Zhenggang Wang, Kwok Yip Szeto, Frederick Chi-Ching Leung 21.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.3 Results of Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 22 Parallel Hypervolume-Guided Hyperheuristic for Adapting the Multi-objective Evolutionary Island Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Coromoto León, Gara Miranda, Eduardo Segredo, Carlos Segura 22.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.2 Island-Based Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.3 Hyperheuristic Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.4 Hypervolume-Guided Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.4.1 Scoring and Selection Strategy . . . . . . . . . . . . . . . . . . 22.5 Experimental Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22.6 Conclusions and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 A Cooperative Strategy for Guiding the Corridor Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Marco Caserta, Stefan Voß 23.1 The Corridor Method: An Introduction . . . . . . . . . . . . . . . . . 23.2 The Blocks Relocation Problem . . . . . . . . . . . . . . . . . . . . . . . . 23.3 The Cooperative Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.3.1 Corridor Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.3.2 Neighborhood Design and Exploration . . . . . . . . . . . 23.3.3 Move Evaluation and Selection . . . . . . . . . . . . . . . . . 23.3.4 Trajectory Fathoming . . . . . . . . . . . . . . . . . . . . . . . . . 23.4 Computational Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 254 256 258 259 261 261 262 263 264 265 267 270 271 273 273 275 277 279 280 281 282 283 285 286 24 On the Performance of Homogeneous and Heterogeneous Cooperative Search Strategies . . . . . . . . . . . . 287 A.D. Masegosa, D. Pelta, I.G. del Amo, J.L. Verdegay 24.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287 24.2 A Basic Cooperative Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . 289 Contents 24.2.1 The Information Management Strategy . . . . . . . . . . The Uncapacitated Single Allocation p-Hub Median Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24.4 Implementation Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24.4.1 Neighborhood Operator . . . . . . . . . . . . . . . . . . . . . . . . 24.5 Experiments and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XXI 289 24.3 290 291 292 293 298 299 List of Contributors Leopoldo Acosta Dep. Systems Engineering and Automatics University of La Laguna La Laguna, CP: 38204, Spain [email protected] I.G. del Amo Dept. of Computer Science and Artificial Intelligence University of Granada E-18071 Granada, Spain [email protected] Rafael Arnay Dep. Systems Engineering and Automatics University of La Laguna La Laguna, CP: 38204, Spain [email protected] José Elias C. Arroyo Departamento de Informática Universidade Federal de Viçosa Campus Universitário da UFV, 3657000-00, Centro Viçosa, MG, Brasil [email protected] S. Arumugam Chief Executive Officer Nandha College of Engineering, Erode, India [email protected] Olivier Barrière INRIA Saclay - Ile-de-France Parc Orsay Université 4, rue Jacques Monod, 91893 ORSAY Cedex, France [email protected] Antonio Berlanga Group of Applied Artificial Intelligence Universidad Carlos III de Madrid Av. de la Universidad Carlos III, 22. Colmenarejo 28270 Madrid, Spain [email protected] Mesude Bicak Department of Computer Science University of Sheffield Sheffield S1 4DP, UK Clara M. Campos Rodríguez Dpto. de Economía Financiera y Contabilidad Universidad de La Laguna, Spain [email protected] XXIV Marco Caserta Institute of Information Systems (IWI) University of Hamburg Von-Melle-Park 5, 20146 Hamburg, Germany [email protected] Juan P. Castro Automated Scheduling, Optimisation and Planning Research Group (ASAP) University of Nottingham (UK) [email protected] Miroslav Červenka Department of Applied Informatics Tomas Bata University Nad stráněmi 4511, 760 05 Zlín, Czech Republic [email protected] Habib Chabchoub Research unit of G.I.A.D. Economic and Management University of Sfax- Tunisia [email protected] Jesse St. Charles Department of Computer Science and Engineering University of Tennessee Chattanooga TN 37403 [email protected] Frederick Chi-Ching Leung Department of Zoology University of Hong Kong Pokfulam Road, Hong Kong SAR, China [email protected] C. Chira Babes-Bolyai University List of Contributors 400084 Cluj-Napoca, Romania [email protected] Xiaohui Cui Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge, TN 37831-6085 [email protected] Jens Czogalla Helmut-Schmidt-University UniBw Hamburg, Holstenhofweg 85, 22043 Hamburg, Germany [email protected] Souhail Dhouib Research unit of L.O.G.I.Q. Superior Institute of Industrial Management of Sfax-Tunisia [email protected] D. Dumitrescu Babes-Bolyai University 400084 Cluj-Napoca, Romania ddumitr.ubbcluj.ro Daniela Favaretto Department of Applied Mathematics Dorsoduro 3825/E, I-30123 Venezia, Italy [email protected] Andreas Fink Helmut-Schmidt-University UniBw Hamburg, Holstenhofweg 85, 22043 Hamburg, Germany [email protected] Jesús García Group of Applied Artificial Intelligence Universidad Carlos III de Madrid Av. de la Universidad Carlos III, 22. Colmenarejo List of Contributors 28270 Madrid, Spain [email protected] Yuebin B. Guo Department of Physics The Hong Kong University of Science and Technology Hong Kong, China [email protected] Mike Holcombe Department of Computer Science University of Sheffield Sheffield S1 4DP, UK Duncan E. Jackson Department of Computer Science University of Sheffield Sheffield S1 4DP, UK [email protected] Tiago de O. Januario Departamento de Informática Universidade Federal de Viçosa Campus Universitário da UFV, 3657000-00, Centro Viçosa, MG, Brasil [email protected] Magnus Jändel Division of Biometry and Systems Analysis, SLU, Uppsala, Sweden Mobile Life at Stockholm University, Sweden Agora for Biosystems, Sigtuna, Sweden [email protected] R. Karthi Asst Professor, Department of Computer Science Amrita Vishwa Vidyapeetham, India Ettimadai, India, Pin - 641105 [email protected] Sana Kouraïchi Research unit of L.O.G.I.Q. XXV Superior Institute of Industrial Management of Sfax-Tunisia [email protected] K. Ramesh Kumar Professor, Department of Mechanical Engineering Amrita Vishwa Vidyapeetham, India k_rameshumar@ ettimadai.amrita.edu Vojtěch Křesálek Department of Eletrotechnics and Measurements Tomas Bata University Nad stráněmi 4511, 760 05 Zlín, Czech Republic Dario Landa-Silva Automated Scheduling, Optimisation and Planning Research Group (ASAP) University of Nottingham (UK) [email protected] Coromoto León Dpto. Estadística, I.O.y Computación Universidad de La Laguna 38271, Tenerife, Spain [email protected] Taïcir loukil Research unit of L.O.G.I.Q. Superior Institute of Industrial Management of Sfax-Tunisia [email protected] Rodica Ioana Lung Babes-Bolyai University of Cluj Napoca XXVI Cluj-Napoca, Romania [email protected] Evelyne Lutton INRIA Saclay - Ile-de-France Parc Orsay Université 4, rue Jacques Monod, 91893 ORSAY Cedex, France [email protected] Luis Martí Group of Applied Artificial Intelligence Universidad Carlos III de Madrid Av. de la Universidad Carlos III, 22. Colmenarejo 28270 Madrid, Spain [email protected] A.D. Masegosa Dept. of Computer Science and Artificial Intelligence University of Granada E-18071 Granada, Spain [email protected] Radomil Matoušek Department of Applied Computer Science Faculty of Mechanical Engineering Brno University of Technology Technická 2, Brno 616 69, Czech Republic [email protected] List of Contributors Universidad de La Laguna 38271, Tenerife, Spain [email protected] José M. Molina Group of Applied Artificial Intelligence Universidad Carlos III de Madrid Av. de la Universidad Carlos III, 22. Colmenarejo 28270 Madrid, Spain [email protected] Mayron César O. Moreira Departamento de Informática Universidade Federal de Viçosa Campus Universitário da UFV, 3657000-00, Centro Viçosa, MG, Brasil [email protected] J. Marcos Moreno-Vega Dpto. de Estadística, I.O. y Computación Escuela Técnica Superior de Ingeniería Informática Universidad de La Laguna 38271 La Laguna, Tenerife, Spain [email protected] Belén Melián-Batista Dpto. de Estadística, I.O. y Computación Escuela Técnica Superior de Ingeniería Informática Universidad de La Laguna 38271 La Laguna, Tenerife, Spain [email protected] José A. Moreno Pérez Group of Intelligent Computing Dpto. de Estadística, I.O. y Computación Instituto Universitario de Desarrollo Regional Escuela Técnica Superior de Ingeniería Informática Universidad de La Laguna 38271 La Laguna, Tenerife, Spain [email protected] Gara Miranda Dpto. Estadística, I.O.y Computación Elena Moretti Department of Applied Mathematics Dorsoduro 3825/E, List of Contributors I-30123 Venezia, Italy [email protected] Hartmut Noltemeier Lehrstuhl für Informatik I Universität Würzburg, Germany [email protected] XXVII Carlos Segura Dpto. Estadística, I.O.y Computación Universidad de La Laguna 38271, Tenerife, Spain [email protected] Paola Pellegrini Department of Applied Mathematics Dorsoduro 3825/E, I-30123 Venezia, Italy [email protected] Marta Sigut Dep. Systems Engineering and Automatics University of La Laguna La Laguna, CP: 38204, Spain [email protected] D. Pelta Dept. of Computer Science and Artificial Intelligence University of Granada E-18071 Granada, Spain [email protected] Kwok Yip Szeto Department of Physics The Hong Kong University of Science and Technology Hong Kong, China [email protected] C.-M. Pintea Babes-Bolyai University 400084 Cluj-Napoca, Romania [email protected] Jonay T. Toledo Dep. Systems Engineering and Automatics University of La Laguna La Laguna, CP: 38204, Spain [email protected] Thomas E. Potok Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge, TN 37831-6085 [email protected] Dolores R. Santos Peñate Dpto. de Métodos Cuantitativos en Economía y Gestión Universidad de Las Palmas de G.C., Spain [email protected] Eduardo Segredo Dpto. Estadística, I.O.y Computación Universidad de La Laguna 38271, Tenerife, Spain [email protected] Nitesh Vaswani Dpto. de Estadística, I.O. y Computación Escuela Técnica Superior de Ingeniería Informática Universidad de La Laguna 38271 La Laguna, Tenerife, Spain [email protected] J.L. Verdegay Dept. of Computer Science and Artificial Intelligence University of Granada E-18071 Granada, Spain [email protected] Stefan Voß Institute of Information XXVIII Systems (IWI) University of Hamburg Von-Melle-Park 5, 20146 Hamburg, Germany [email protected] Zhenggang Wang Department of Physics The Hong Kong University of Science and Technology List of Contributors Hong Kong, China [email protected] Rayco Yumar Dpto. de Estadística, I.O.y Computación Escuela Técnica Superior de Ingeniería Informática Universidad de La Laguna 38271 La Laguna, Tenerife, Spain [email protected]