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Mathematical Foundations of Nature-Inspired Algorithms - Xin-She Yang, Xing-Shi He

Mathematical Foundations of Nature-Inspired Algorithms

Buch | Softcover
XI, 107 Seiten
2019 | 1st ed. 2019
Springer International Publishing (Verlag)
978-3-030-16935-0 (ISBN)
CHF 89,85 inkl. MwSt

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.

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


Erscheinungsdatum
Reihe/Serie SpringerBriefs in Optimization
Zusatzinfo XI, 107 p. 4 illus., 2 illus. in color.
Verlagsort Cham
Sprache englisch
Maße 155 x 235 mm
Gewicht 195 g
Themenwelt Mathematik / Informatik Mathematik Analysis
Mathematik / Informatik Mathematik Angewandte Mathematik
Mathematik / Informatik Mathematik Wahrscheinlichkeit / Kombinatorik
Schlagworte Algorithm Analysis • ant colony optimization • Bat Algorithm • bayesian framework • Bees-inspired Algorithms • Cuckoo Search • Essence of an Algorithm. • filter theory • firefly algorithm • General Formulation of Optimization • Gradient-Based Optimization Techniques • Gradient-Free Methods and Metaheuristics • Hyper-Optimization • Markov Chain Monte Carlo • Nature-Inspired Algorithms • Parameter Tuning and Control • Particle swarm optimization • Stability of an Algorithm • Swarm intelligence • Unconstrained optimization
ISBN-10 3-030-16935-9 / 3030169359
ISBN-13 978-3-030-16935-0 / 9783030169350
Zustand Neuware
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