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Search and optimization by metaheuri...

Du, Ke-Lin.

Search and optimization by metaheuristicstechniques and algorithms inspired by nature /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Search and optimization by metaheuristicsby 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.
Published:	Cham :Springer International Publishing :2016.
Description:	xxi, 434 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Mathematical optimization.
Online resource:	http://dx.doi.org/10.1007/978-3-319-41192-7
ISBN:	9783319411927$q(electronic bk.)

Search and optimization by metaheuristicstechniques 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$q(electronic bk.)

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

183292
Mathematical optimization.

LC Class. No.: QA402.5

Dewey Class. No.: 519.6

Search and optimization by metaheuristicstechniques and algorithms inspired by nature /
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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.
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