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Mathematical foundations of nature-i...

Yang, Xin-She.

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Mathematical foundations of nature-inspired algorithms

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Mathematical foundations of nature-inspired algorithms/ by Xin-She Yang, Xing-Shi He.
作者:	Yang, Xin-She.
其他作者:	He, Xing-Shi.
出版者:	Cham :Springer International Publishing : : 2019.,
面頁冊數:	xi, 107 p. :ill., digital ;24 cm.
內容註:	1 Introduction to Optimization -- 2 Nature-Inspired Algorithms -- 3 Mathematical Foundations -- 4 Mathematical Analysis I -- 5 Mathematical Analysis II.
Contained By:	Springer eBooks
標題:	Internet - Mathematical models. -
電子資源:	https://doi.org/10.1007/978-3-030-16936-7
ISBN:	9783030169367

Mathematical foundations of nature-inspired algorithms
Yang, Xin-She.

Mathematical foundations of nature-inspired algorithms[electronic resource] /by Xin-She Yang, Xing-Shi He. - Cham :Springer International Publishing :2019. - xi, 107 p. :ill., digital ;24 cm. - SpringerBriefs in optimization,2190-8354. - SpringerBriefs in optimization..

1 Introduction to Optimization -- 2 Nature-Inspired Algorithms -- 3 Mathematical Foundations -- 4 Mathematical Analysis I -- 5 Mathematical Analysis II.

This book presents a systematic approach to analyze nature-inspired algorithms. Beginning with an introduction to optimization methods and algorithms, this book moves on to provide a unified framework of mathematical analysis for convergence and stability. Specific nature-inspired algorithms include: swarm intelligence, ant colony optimization, particle swarm optimization, bee-inspired algorithms, bat algorithm, firefly algorithm, and cuckoo search. Algorithms are analyzed from a wide spectrum of theories and frameworks to offer insight to the main characteristics of algorithms and understand how and why they work for solving optimization problems. In-depth mathematical analyses are carried out for different perspectives, including complexity theory, fixed point theory, dynamical systems, self-organization, Bayesian framework, Markov chain framework, filter theory, statistical learning, and statistical measures. Students and researchers in optimization, operations research, artificial intelligence, data mining, machine learning, computer science, and management sciences will see the pros and cons of a variety of algorithms through detailed examples and a comparison of algorithms.

ISBN: 9783030169367

Standard No.: 10.1007/978-3-030-16936-7doiSubjects--Topical Terms:

589995
Internet
--Mathematical models.

LC Class. No.: TK5105.875.I57 / Y364 2019

Dewey Class. No.: 004.678

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