Metaheuristics for Dynamic Optimization

The well known metaheuristics procedures are Genetic Programming, GRASP, Simulated Annealing or Ant Colony Optimization. The reader can find a review of this methods in [4, 11]. In dynamic optimization problems, metaheuristic-based ...

Metaheuristics for Dynamic Optimization

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.

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Metaheuristics for Dynamic Optimization
Language: en
Pages: 400
Authors: Enrique Alba, Amir Nakib, Patrick Siarry
Categories: Technology & Engineering
Type: BOOK - Published: 2012-08-11 - Publisher: Springer

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
Metaheuristics for Dynamic Optimization
Language: en
Pages:
Authors: Enrique Alba, Amir Nakib, Patrick Siarry
Categories: Combinatorial optimization
Type: BOOK - Published: 2013 - Publisher:

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
Metaheuristics for Intelligent Electrical Networks
Language: en
Pages: 286
Authors: Frédéric Héliodore, Amir Nakib, Boussaad Ismail, Salma Ouchraa, Laurent Schmitt
Categories: Computers
Type: BOOK - Published: 2017-08-14 - Publisher: John Wiley & Sons

Intelligence is defined by the ability to optimize, manage and reconcile the currents of physical, economic and even social flows. The strong constraint of immediacy proves to be an opportunity to imagine, propose and deliver solutions on the common basis of optimization techniques. Metaheuristics for Intelligent Electrical Networks analyzes the
Optimization Techniques for Solving Complex Problems
Language: en
Pages: 504
Authors: Enrique Alba, Christian Blum, Pedro Asasi, Coromoto Leon, Juan Antonio Gomez
Categories: Computers
Type: BOOK - Published: 2009-02-17 - Publisher: John Wiley & Sons

Real-world problems and modern optimization techniques to solve them Here, a team of international experts brings together core ideas for solving complex problems in optimization across a wide variety of real-world settings, including computer science, engineering, transportation, telecommunications, and bioinformatics. Part One—covers methodologies for complex problem solving including genetic programming,
Simulated Evolution and Learning
Language: en
Pages: 862
Authors: Grant Dick, Will N. Browne, Peter Whigham, Mengjie Zhang, Lam Thu Bui, Hisao Ishibuchi, Yaochu Jin, Xiaodong Li, Yuhui Shi, Pramod Singh, Kay Chen Tan, Ke Tang
Categories: Computers
Type: BOOK - Published: 2014-11-11 - Publisher: Springer

This volume constitutes the proceedings of the 10th International Conference on Simulated Evolution and Learning, SEAL 2012, held in Dunedin, New Zealand, in December 2014. The 42 full papers and 29 short papers presented were carefully reviewed and selected from 109 submissions. The papers are organized in topical sections on