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Title
Nature-inspired algorithms and applied optimization / Xin-She Yang, editor.
ISBN
9783319676692 (electronic book)
3319676695 (electronic book)
9783319676685
Published
Cham, Switzerland : Springer, 2018.
Language
English
Description
1 online resource (xi, 330 pages) : illustrations.
Item Number
10.1007/978-3-319-67669-2 doi
Call Number
QA76.9.N37
Dewey Decimal Classification
006.3/8
Summary
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.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed October 23, 2017).
Series
Studies in computational intelligence ; v. 744.
Available in Other Form
Print version: 9783319676685