UID:
almahu_9948612943202882
Format:
XII, 192 p. 112 illus., 92 illus. in color.
,
online resource.
Edition:
1st ed. 2021.
ISBN:
9783030611118
Series Statement:
Studies in Computational Intelligence, 927
Content:
This book exemplifies how algorithms are developed by mimicking nature. Classical techniques for solving day-to-day problems is time-consuming and cannot address complex problems. Metaheuristic algorithms are nature-inspired optimization techniques for solving real-life complex problems. This book emphasizes the social behaviour of insects, animals and other natural entities, in terms of converging power and benefits. Major nature-inspired algorithms discussed in this book include the bee colony algorithm, ant colony algorithm, grey wolf optimization algorithm, whale optimization algorithm, firefly algorithm, bat algorithm, ant lion optimization algorithm, grasshopper optimization algorithm, butterfly optimization algorithm and others. The algorithms have been arranged in chapters to help readers gain better insight into nature-inspired systems and swarm intelligence. All the MATLAB codes have been provided in the appendices of the book to enable readers practice how to solve examples included in all sections. This book is for experts in Engineering and Applied Sciences, Natural and Formal Sciences, Economics, Humanities and Social Sciences.
Note:
Introduction To Optimization -- Particle Swarm Optimisation -- Artificial Bee Colony Algorithm -- Ant Colony Algorithm -- Grey Wolf Optimizer -- Whale Optimization Algorithm -- Bat Algorithm -- Ant Lion Optimization Algorithm -- Grasshopper Optimisation Algorithm (Goa) -- Moths-Flame Optimization Algorithm -- Genetic Algorithm -- Artificial Neural Network -- Future of Nature Inspired Algorithm, Swarm and Computational Intelligence.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9783030611101
Additional Edition:
Printed edition: ISBN 9783030611125
Additional Edition:
Printed edition: ISBN 9783030611132
Language:
English
Subjects:
Computer Science
DOI:
10.1007/978-3-030-61111-8
URL:
https://doi.org/10.1007/978-3-030-61111-8
URL:
Volltext
(URL des Erstveröffentlichers)
