Overview
- Reviews the state-of-the-art developments in nature-inspired algorithms and optimization
- Presents a number of theories (no-free-lunch theorems and convergence analysis) and insights into nature-inspired algorithms
- Introduces algorithms with an emphasis on applied optimization in real-world applications
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Computational Intelligence (SCI, volume 744)
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Table of contents (14 chapters)
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Front Matter
Keywords
- Algorithm
- Applied Optimization
- Bio-inspired Computing
- Bat Algorithm
- Convergence Analysis
- Cuckoo Search
- Economic Load Dispatch
- Feature Selection
- Firefly Algorithm
- Flower Pollination Algorithm
- Simulated Annealing
- Swarm Intelligence
- Nature-inspired Algorithm
- Wireless Sensor Network
- Particle Swarm Optimization
- Scheduling
- Metaheuristics
- Multi-objective Optimization
- No Free Lunch Theorem
- Vehicle Routing
About this book
This book reviews the state-of-the-art developments in nature-inspired algorithms and their applications in various disciplines, ranging from feature selection and engineering design optimization to scheduling and vehicle routing. It introduces each algorithm and its implementation with case studies as well as extensive literature reviews, and also includes self-contained chapters featuring theoretical analyses, such as convergence analysis and no-free-lunch theorems so as to provide insights into the current nature-inspired optimization algorithms. Topics include ant colony optimization, the bat algorithm, B-spline curve fitting, cuckoo search, feature selection, economic load dispatch, the firefly algorithm, the flower pollination algorithm, knapsack problem, octonian and quaternion representations, particle swarm optimization, scheduling, wireless networks, vehicle routing with time windows, and maximally different alternatives. This timely book serves as a practical guide and reference resource for students, researchers and professionals.
Reviews
“The book is rich with relevant illustrations and real-life/practical problems, where the various topics are or can be applied. The book is a comprehensive and in-depth study, and the style of presentation is remarkable. These aspects make reading this book an absolute delight.” (Sudev Naduvath,Computing Reviews, August, 2018)
Editors and Affiliations
Bibliographic Information
Book Title: Nature-Inspired Algorithms and Applied Optimization
Editors: Xin-She Yang
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-319-67669-2
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing AG, part of Springer Nature 2018
Hardcover ISBN: 978-3-319-67668-5Published: 18 October 2017
Softcover ISBN: 978-3-319-88465-3Published: 15 August 2018
eBook ISBN: 978-3-319-67669-2Published: 08 October 2017
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XI, 330
Number of Illustrations: 14 b/w illustrations, 28 illustrations in colour
Topics: Computational Intelligence, Artificial Intelligence, Algorithms, Optimization