Search and Optimization by Metaheuristics
Springer International Publishing (Verlag)
978-3-319-82290-7 (ISBN)
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.
Ke-Lin Du, PhD, is Affiliate Associate Professor at Concordia University, Montreal, Quebec, Canada, and Founder and CEO of Xonlink Inc, Ningbo, China. M.N.S. Swamy, PhD, is Research Professor and Tier I Concordia Research Chair in the Department of Electrical and Computer Engineering at Concordia University, Montreal, Quebec, Canada.
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.
"The book under review contains large amount of precisely selected topics covering various aspects and design techniques related to efficient metaheuristic algorithms for searching and optimization. ... is intended primarily as a textbook for graduate students specializing in engineering and computer science. Besides being very useful as a valuable resource for post-docs and researchers working in these areas, it may as well be used by those who are interested in search and optimization methods in general." (Vladimír Lacko, zbMATH, 1351.90002, 2017)
| Erscheinungsdatum | 05.03.2022 |
|---|---|
| Zusatzinfo | XXI, 434 p. 68 illus., 40 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 6847 g |
| Themenwelt | Mathematik / Informatik ► Informatik |
| Mathematik / Informatik ► Mathematik ► Analysis | |
| Mathematik / Informatik ► Mathematik ► Angewandte Mathematik | |
| Schlagworte | evolutionary computation • Metaheuristics • Natural Computing • Optimization • Swarm intelligence |
| ISBN-10 | 3-319-82290-X / 331982290X |
| ISBN-13 | 978-3-319-82290-7 / 9783319822907 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
aus dem Bereich
