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Auteur(s) : Ke-Lin Du - M. N. S. SwamyEditeur : Springer International Publishing AgLangue : Anglais Parution : 01/05/2018Format : Moyen, de 350g à 1kgNombre de pages : 456Expédition : 686Dimensions : 23.5 x 15.5 x 2.5
ISBN : 9783319822907
Résumé :
This textbook provides a comprehensive introduction to nature-inspired metaheuristic methods for search and optimization, including the latest trends in evolutionary algorithms and other forms of natural computing. Over 100 different types of these methods are discussed in detail. The authors emphasize non-standard optimization problems and utilize a natural approach to the topic, moving from basic notions to more complex ones.
An introductory chapter covers the necessary biological and mathematical backgrounds for understanding the main material. Subsequent chapters then explore almost all of the major metaheuristics for search and optimization created based on natural phenomena, including simulated annealing, recurrent neural networks, genetic algorithms and genetic programming, differential evolution, memetic algorithms, particle swarm optimization, artificial immune systems, ant colony optimization, tabu search and scatter search, bee and bacteria foraging algorithms, harmony search, biomolecular computing, quantum computing, and many others. General topics on dynamic, multimodal, constrained, and multiobjective optimizations are also described. Each chapter includes detailed flowcharts that illustrate specific algorithms and exercises that reinforce important topics. Introduced in the appendix are some benchmarks for the evaluation of metaheuristics.
Search and Optimization by Metaheuristics is intended primarily as a textbook for graduate and advanced undergraduate students specializing in engineering and computer science. It will also serve as a valuable resource for scientists and researchers working in these areas, as well as those who are interested in search and optimization methods.
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Biographie:
Ke-Lin Du is currently the founder and CEO at Xonlink Inc., China. He is also an Affiliate Associate Professor at the Department of Electrical and Computer Engineering, Concordia University, Canada. In the past, he held positions at Huawei Technologies, the China Academy of Telecommunication Technology, the Chinese University of Hong Kong, the Hong Kong University of Science and Technology, Concordia University, and Enjoyor Inc. He has published four books and over 50 papers, and filed over 30 patents. A Senior Member of the IEEE, his current research interests include signal processing, neural networks, intelligent systems, and wireless communications. MNS Swamy is currently a Research Professor and holder of the Concordia Tier I Research Chair of Signal Processing at the Department of Electrical and Computer Engineering, Concordia University, where he was Dean of the Faculty of Engineering and ComputerScience from 1977 to 1993 and the founding Chair of the EE department. He has published extensively in the areas of circuits, systems and signal processing, and co-authored nine books and holds five patents. Professor Swamy is a Fellow of the IEEE, IET (UK) and EIC (Canada), and has received many IEEE-CAS awards, including the Guillemin-Cauer award in 1986, as well as the Education Award and the Golden Jubilee Medal, both in 2000. He has been the Editor-in-Chief of the journal Circuits, Systems and Signal Processing (CSSP) since 1999. Recently, CSSP has instituted the Best Paper Award in his name....
Sommaire:
This textbook provides a comprehensive introduction to nature-inspired metaheuristic methods for search and optimization, including the latest trends in evolutionary algorithms and other forms of natural computing. Over 100 different types of these methods are discussed in detail. The authors emphasize non-standard optimization problems and utilize a natural approach to the topic, moving from basic notions to more complex ones.
An introductory chapter covers the necessary biological and mathematical backgrounds for understanding the main material. Subsequent chapters then explore almost all of the major metaheuristics for search and optimization created based on natural phenomena, including simulated annealing, recurrent neural networks, genetic algorithms and genetic programming, differential evolution, memetic algorithms, particle swarm optimization, artificial immune systems, ant colony optimization, tabu search and scatter search, bee and bacteria foraging algorithms, harmony search, biomolecular computing, quantum computing, and many others. General topics on dynamic, multimodal, constrained, and multiobjective optimizations are also described. Each chapter includes detailed flowcharts that illustrate specific algorithms and exercises that reinforce important topics. Introduced in the appendix are some benchmarks for the evaluation of metaheuristics.
Search and Optimization by Metaheuristics is intended primarily as a textbook for graduate and advanced undergraduate students specializing in engineering and computer science. It will also serve as a valuable resource for scientists and researchers working in these areas, as well as those who are interested in search and optimization methods.
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