| 书目名称 | Mathematical Foundations of Nature-Inspired Algorithms | | 编辑 | Xin-She Yang,Xing-Shi He | | 视频video | | | 概述 | Analyzes nature-inspired algorithms.Provides a unified framework of mathematical analysis for convergence and stability.Features methods and techniques for selecting specific algorithms | | 丛书名称 | SpringerBriefs in Optimization | | 图书封面 |  | | 描述 | .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.. | | 出版日期 | Book 2019 | | 关键词 | General Formulation of Optimization; Essence of an Algorithm; ; Unconstrained Optimization; Gradient-Bas | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-16936-7 | | isbn_softcover | 978-3-030-16935-0 | | isbn_ebook | 978-3-030-16936-7Series ISSN 2190-8354 Series E-ISSN 2191-575X | | issn_series | 2190-8354 | | copyright | The Author(s), under exclusive license to Springer Nature Switzerland AG 2019 |
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发表于 2025-3-21 16:17:54
