國立虎尾科技大學 |

Search and Optimization by Metaheuristics = Techniques and Algorithms Inspired by Nature /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Search and Optimization by Metaheuristics/ by Ke-Lin Du, M. N. S. Swamy.
Reminder of title:	Techniques and Algorithms Inspired by Nature /
Author:	Du, Ke-Lin.
other author:	Swamy, M. N. S.
Description:	XXI, 434 p. 68 illus., 40 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computer mathematics. -
Online resource:	https://doi.org/10.1007/978-3-319-41192-7
ISBN:	9783319411927

Search and Optimization by Metaheuristics = 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. - 1st ed. 2016. - XXI, 434 p. 68 illus., 40 illus. in color.online resource.

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:

1199796
Computer mathematics.

LC Class. No.: QA71-90

Dewey Class. No.: 004

Search and Optimization by Metaheuristics = Techniques and Algorithms Inspired by Nature /
LDR:03839nam a22003975i 4500 001 978815
003 DE-He213
005 20200704033250.0
007 cr nn 008mamaa
008 201211s2016 gw | s |||| 0|eng d
020 $a 9783319411927 $9 978-3-319-41192-7
024 7 $a 10.1007/978-3-319-41192-7 $2 doi
035 $a 978-3-319-41192-7
050 4 $a QA71-90
072 7 $a PDE $2 bicssc
072 7 $a COM014000 $2 bisacsh
072 7 $a PDE $2 thema
082 0 4 $a 004 $2 23
100 1 $a Du, Ke-Lin. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1023068
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.
250 $a 1st ed. 2016.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Birkhäuser, $c 2016.
300 $a XXI, 434 p. 68 illus., 40 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
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 Computer mathematics. $3 1199796
650 0 $a Algorithms. $3 527865
650 0 $a Mathematical optimization. $3 527675
650 0 $a Computer simulation. $3 560190
650 0 $a Computational intelligence. $3 568984
650 1 4 $a Computational Science and Engineering. $3 670319
650 2 4 $a Optimization. $3 669174
650 2 4 $a Simulation and Modeling. $3 669249
650 2 4 $a Computational Intelligence. $3 768837
700 1 $a Swamy, M. N. S. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1023069
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319411910
776 0 8 $i Printed edition: $z 9783319411934
776 0 8 $i Printed edition: $z 9783319822907
856 4 0 $u https://doi.org/10.1007/978-3-319-41192-7
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)