Enrique Alba

Patrick Siarry

Amir Nakib

This book is an updated effort in summarizing the trending topics and new hot research lines in solving dynamic problems using metaheuristics. An analysis of the present state in solving complex problems quickly draws a clear picture: problems that change in time, having noise and uncertainties in their definition are becoming very important. The tools to face these problems are still to be built, since existing techniques are either slow or inefficient in tracking the many global optima that those problems are presenting to the solver technique. Thus, this book is devoted to include several of the most important advances in solving dynamic problems. Metaheuristics are the more popular tools to this end, and then we can find in the book how to best use genetic algorithms, particle swarm, ant colonies, immune systems, variable neighborhood search, and many other bioinspired techniques. Also, neural network solutions are considered in this book. Both, theory and practice have been addressed in the chapters of the book. Mathematical background and methodological tools in solving this new class of problems and applications are included. From the applications point of view, not just academic benchmarks are dealt with, but also real world applications in logistics and bioinformatics are discussed here. The book then covers theory and practice, as well as discrete versus continuous dynamic optimization, in the aim of creating a fresh and comprehensive volume. This book is targeted to either beginners and experienced practitioners in dynamic optimization, since we took care of devising the chapters in a way that a wide audience could profit from its contents. We hope to offer a single source for up-to-date information in dynamic optimization, an inspiring and attractive new research domain that appeared in these last years and is here to stay.

Metaheuristics for Dynamic Optimization

Gabriel Luque

Francisco Chicano

This book constitutes the proceedings of the second International Conference on Smart Cities, Smart-CT 2017, held in Málaga, Spain, in June 2017. The 16 papers presented in this volume were carefully reviewed and selected from 21 submissions. The topics covered include studies and tools to improve road traffic, energy consumption, logistics, frameworks to provide new services and take decisions in a holistic way, driving assistance, electric vehicles, public transport, and surveys on smart city concepts.

Second International Conference, Smart-CT 2017, Málaga, Spain, June 14-16, 2017, Proceedings

Smart Cities

This book constitutes the proceedings of the First International Conference on Smart Cities, Smart-CT 2016, held in Malaga, Spain, in June 2016. The 16 papers presented in this volume were carefully reviewed and selected from 28 submissions. They topics covered include studies and tools to improve road traffic, energy consumption, logistics, frameworks to provide new services and take decisions in a holistic way, driving assistance, electric vehicles, public transport, and surveys on smart city concepts.

First International Conference, Smart-CT 2016, Málaga, Spain, June 15-17, 2016, Proceedings

Manuel Ojeda-Aciego

David Camacho

Alicia Troncoso

Carlos Cotta

Susana Montes

José Riquelme

Rodrigo Gil-Merino

This book constitutes the refereed proceedings of the 19th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2020, which was cancelled due to the COVID-19 pandemic, amalgamated with CAEPIA 2021, and held in Malaga, Spain, during September 2021. The 25 full papers presented were carefully selected from 40 submissions. The Conference of the Spanish Association of Artificial Intelligence (CAEPIA) is a biennial forum open to researchers from all over the world to present and discuss their latest scientific and technological advances in Antificial Intelligence (AI). The book is subdivided into the following topical headings: machine learning, optimization and search, and real-world applications. It covers such themes as ambient intelligence and smart environments; computer vision and robotics; constraints, search and planning; creativity and A.I.; education and A.I.; explainable and responsible A.I.; foundation, models and applications of A.I, and others.

19th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2020/2021, Málaga, Spain, September 22–24, 2021, Proceedings

Advances in Artificial Intelligence

Solving complex optimization problems with parallelmetaheuristics Parallel Metaheuristics brings together an international group ofexperts in parallelism and metaheuristics to provide a much-neededsynthesis of these two fields. Readers discover how metaheuristictechniques can provide useful and practical solutions for a widerange of problems and application domains, with an emphasis on thefields of telecommunications and bioinformatics. This volume fillsa long-existing gap, allowing researchers and practitioners todevelop efficient metaheuristic algorithms to find solutions. The book is divided into three parts: * Part One: Introduction to Metaheuristics and Parallelism,including an Introduction to Metaheuristic Techniques, Measuringthe Performance of Parallel Metaheuristics, New Technologies inParallelism, and a head-to-head discussion on Metaheuristics andParallelism * Part Two: Parallel Metaheuristic Models, including ParallelGenetic Algorithms, Parallel Genetic Programming, ParallelEvolution Strategies, Parallel Ant Colony Algorithms, ParallelEstimation of Distribution Algorithms, Parallel Scatter Search,Parallel Variable Neighborhood Search, Parallel SimulatedAnnealing, Parallel Tabu Search, Parallel GRASP, Parallel HybridMetaheuristics, Parallel Multi-Objective Optimization, and ParallelHeterogeneous Metaheuristics * Part Three: Theory and Applications, including Theory of ParallelGenetic Algorithms, Parallel Metaheuristics Applications, ParallelMetaheuristics in Telecommunications, and a final chapter onBioinformatics and Parallel Metaheuristics Each self-contained chapter begins with clear overviews andintroductions that bring the reader up to speed, describes basictechniques, and ends with a reference list for further study.Packed with numerous tables and figures to illustrate the complextheory and processes, this comprehensive volume also includesnumerous practical real-world optimization problems and theirsolutions. This is essential reading for students and researchers in computerscience, mathematics, and engineering who deal with parallelism,metaheuristics, and optimization in general.

A New Class of Algorithms

Parallel Metaheuristics

This book is the result of several years of research trying to better characterize parallel genetic algorithms (pGAs) as a powerful tool for optimization, search, and learning. Readers can learn how to solve complex tasks by reducing their high computational times. Dealing with two scientific fields (parallelism and GAs) is always difficult, and the book seeks at gracefully introducing from basic concepts to advanced topics. The presentation is structured in three parts. The first one is targeted to the algorithms themselves, discussing their components, the physical parallelism, and best practices in using and evaluating them. A second part deals with the theory for pGAs, with an eye on theory-to-practice issues. A final third part offers a very wide study of pGAs as practical problem solvers, addressing domains such as natural language processing, circuits design, scheduling, and genomics. This volume will be helpful both for researchers and practitioners. The first part shows pGAs to either beginners and mature researchers looking for a unified view of the two fields: GAs and parallelism. The second part partially solves (and also opens) new investigation lines in theory of pGAs. The third part can be accessed independently for readers interested in applications. The result is an excellent source of information on the state of the art and future developments in parallel GAs.

Enrique Alba

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