大葉大學圖書館 |

Search and optimization by metaheuristics[electronic resource] :techniques and algorithms inspired by nature /

紀錄類型:	書目-電子資源 : Monograph/item
杜威分類號:	519.6
書名/作者:	Search and optimization by metaheuristics : techniques and algorithms inspired by nature // by Ke-Lin Du, M.N.S. Swamy.
作者:	Du, Ke-Lin.
其他作者:	Swamy, M.N.S.
出版者:	Cham : : Springer International Publishing :, 2016.
面頁冊數:	xxi, 434 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Mathematical optimization.
標題:	Mathematics.
標題:	Computational Science and Engineering.
標題:	Algorithms.
標題:	Optimization.
標題:	Simulation and Modeling.
標題:	Computational Intelligence.
ISBN:	9783319411927
ISBN:	9783319411910
內容註:	Preface -- Introduction -- Simulated Annealing -- Optimization by Recurrent Neural Networks -- Genetic Algorithms and Genetic Programming -- Evolutionary Strategies -- Differential Evolution -- Estimation of Distribution Algorithms -- Mimetic Algorithms -- Topics in EAs -- Particle Swarm Optimization -- Artificial Immune Systems -- Ant Colony Optimization -- Tabu Search and Scatter Search -- Bee Metaheuristics -- Harmony Search -- Biomolecular Computing -- Quantum Computing -- Other Heuristics-Inspired Optimization Methods -- Dynamic, Multimodal, and Constraint-Satisfaction Optimizations -- Multiobjective Optimization -- Appendix 1: Discrete Benchmark Functions -- Appendix 2: Test Functions -- Index.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-41192-7

Search and optimization by metaheuristics[electronic resource] :techniques and algorithms inspired by nature /
Du, Ke-Lin.

Search and optimization by metaheuristicstechniques and algorithms inspired by nature /[electronic resource] :by Ke-Lin Du, M.N.S. Swamy. - Cham :Springer International Publishing :2016. - xxi, 434 p. :ill., digital ;24 cm.

Preface -- Introduction -- Simulated Annealing -- Optimization by Recurrent Neural Networks -- Genetic Algorithms and Genetic Programming -- Evolutionary Strategies -- Differential Evolution -- Estimation of Distribution Algorithms -- Mimetic Algorithms -- Topics in EAs -- Particle Swarm Optimization -- Artificial Immune Systems -- Ant Colony Optimization -- Tabu Search and Scatter Search -- Bee Metaheuristics -- Harmony Search -- Biomolecular Computing -- Quantum Computing -- Other Heuristics-Inspired Optimization Methods -- Dynamic, Multimodal, and Constraint-Satisfaction Optimizations -- Multiobjective Optimization -- Appendix 1: Discrete Benchmark Functions -- Appendix 2: Test Functions -- Index.

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.

ISBN: 9783319411927

Standard No.: 10.1007/978-3-319-41192-7doiSubjects--Topical Terms:

176332
Mathematical optimization.

LC Class. No.: QA402.5

Dewey Class. No.: 519.6

Search and optimization by metaheuristics[electronic resource] :techniques and algorithms inspired by nature /
LDR:03445nmm a2200325 a 4500 001 462350
003 DE-He213
005 20160720101518.0
006 m d
007 cr nn 008maaau
008 170223s2016 gw s 0 eng d
020 $a 9783319411927 $q (electronic bk.)
020 $a 9783319411910 $q (paper)
024 7 $a 10.1007/978-3-319-41192-7 $2 doi
035 $a 978-3-319-41192-7
040 $a GP $c GP
041 0 $a eng
050 4 $a QA402.5
072 7 $a PDE $2 bicssc
072 7 $a COM014000 $2 bisacsh
072 7 $a MAT003000 $2 bisacsh
082 0 4 $a 519.6 $2 23
090 $a QA402.5 $b .D812 2016
100 1 $a Du, Ke-Lin. $3 614067
245 1 0 $a Search and optimization by metaheuristics $h [electronic resource] : $b techniques and algorithms inspired by nature / $c by Ke-Lin Du, M.N.S. Swamy.
260 $a Cham : $b Springer International Publishing : $b Imprint: Birkhauser, $c 2016.
300 $a xxi, 434 p. : $b ill., digital ; $c 24 cm.
505 0 $a Preface -- Introduction -- Simulated Annealing -- Optimization by Recurrent Neural Networks -- Genetic Algorithms and Genetic Programming -- Evolutionary Strategies -- Differential Evolution -- Estimation of Distribution Algorithms -- Mimetic Algorithms -- Topics in EAs -- Particle Swarm Optimization -- Artificial Immune Systems -- Ant Colony Optimization -- Tabu Search and Scatter Search -- Bee Metaheuristics -- Harmony Search -- Biomolecular Computing -- Quantum Computing -- Other Heuristics-Inspired Optimization Methods -- Dynamic, Multimodal, and Constraint-Satisfaction Optimizations -- Multiobjective Optimization -- Appendix 1: Discrete Benchmark Functions -- Appendix 2: Test Functions -- Index.
520 $a 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.
650 0 $a Mathematical optimization. $3 176332
650 1 4 $a Mathematics. $3 172349
650 2 4 $a Computational Science and Engineering. $3 463702
650 2 4 $a Algorithms. $3 182797
650 2 4 $a Optimization. $3 463683
650 2 4 $a Simulation and Modeling. $3 463796
650 2 4 $a Computational Intelligence. $3 463962
700 1 $a Swamy, M.N.S. $3 666802
710 2 $a SpringerLink (Online service) $3 463450
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-41192-7
950 $a Mathematics and Statistics (Springer-11649)