Search and Optimization by Metaheuristics

Techniques and Algorithms Inspired by Nature
 Previously published in hardcover
Besorgungstitel | Lieferzeit:3-5 Tage I
ISBN-13:
9783319822907
Einband:
Previously published in hardcover
Erscheinungsdatum:
31.05.2018
Seiten:
456
Autor:
Ke-Lin Du
Gewicht:
684 g
Format:
235x155x24 mm
Sprache:
Englisch
Beschreibung:

Offers a comprehensive and state-of-the-art introduction to nature-inspired metaheuristicsIncludes detailed, implementable algorithmic flowcharts for the most popular algorithmsDiscusses over 100 different types of nature-inspired search and optimization methodsWill allow students to discover the newest trends in metaheuristics and optimization
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.

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