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000890273 019__ $$a1105184126
000890273 020__ $$a9783030169367$$q(electronic book)
000890273 020__ $$a3030169367$$q(electronic book)
000890273 020__ $$z9783030169350
000890273 0248_ $$a10.1007/978-3-030-16
000890273 035__ $$aSP(OCoLC)on1100588288
000890273 035__ $$aSP(OCoLC)1100588288$$z(OCoLC)1105184126
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000890273 050_4 $$aTK5105.875.I57$$bY36 2019
000890273 08204 $$a004.67/8$$223
000890273 1001_ $$aYang, Xin-She,$$eauthor.
000890273 24510 $$aMathematical foundations of nature-inspired algorithms /$$cXin-She Yang, Xing-Shi He.
000890273 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2019]
000890273 300__ $$a1 online resource.
000890273 336__ $$atext$$btxt$$2rdacontent
000890273 337__ $$acomputer$$bc$$2rdamedia
000890273 338__ $$aonline resource$$bcr$$2rdacarrier
000890273 4901_ $$aSpringer Briefs in optimization
000890273 504__ $$aIncludes bibliographical references and index.
000890273 5050_ $$aIntro; Preface; Contents; About the Authors; 1 Introduction to Optimization; 1.1 Introduction; 1.2 Essence of an Algorithm; 1.3 Unconstrained Optimization; 1.3.1 Univariate Functions; 1.3.2 Multivariate Functions; 1.4 Optimization; 1.5 Gradient-Based Methods; 1.5.1 Newton's Method; 1.5.2 Steepest Descent Method; 1.5.3 Line Search; 1.5.4 Conjugate Gradient Method; 1.5.5 Stochastic Gradient Descent; 1.5.6 Subgradient Method; 2 Nature-Inspired Algorithms; 2.1 A Brief History of Nature-Inspired Algorithms; 2.2 Genetic Algorithms; 2.3 Simulated Annealing; 2.4 Ant Colony Optimization
000890273 5058_ $$a2.5 Differential Evolution2.6 Particle Swarm Optimization; 2.7 Bees-Inspired Algorithms; 2.8 Bat Algorithm; 2.9 Firefly Algorithm; 2.10 Cuckoo Search; 2.11 Flower Pollination Algorithm; 2.12 Other Algorithms; 3 Mathematical Foundations; 3.1 Convergence Analysis; 3.1.1 Rate of Convergence; 3.1.2 Convergence Analysis of Newton's Method; 3.2 Stability of an Algorithm; 3.3 Robustness Analysis; 3.4 Probability Theory; 3.4.1 Random Variables; 3.4.2 Poisson Distribution and Gaussian Distribution; 3.4.3 Common Probability Distributions; 3.5 Random Walks and Lévy Flights; 3.6 Performance Measures
000890273 5058_ $$a3.7 Monte Carlo and Markov Chains4 Mathematical Analysis of Algorithms: Part I; 4.1 Algorithm Analysis and Insight; 4.1.1 Characteristics of Nature-Inspired Algorithms; 4.1.2 What's Wrong with Traditional Algorithms?; 4.2 Advantages of Heuristics and Metaheuristics; 4.3 Key Components of Algorithms; 4.3.1 Deterministic or Stochastic; 4.3.2 Exploration and Exploitation; 4.3.3 Role of Components; 4.4 Complexity; 4.4.1 Time and Space Complexity; 4.4.2 Complexity of Algorithms; 4.5 Fixed Point Theory; 4.6 Dynamical System; 4.7 Self-organized Systems; 4.8 Markov Chain Monte Carlo
000890273 5058_ $$a4.8.1 Biased Monte Carlo4.8.2 Random Walks; 4.9 No-Free-Lunch Theorems; 5 Mathematical Analysis of Algorithms: Part II; 5.1 Swarm Intelligence; 5.2 Filter Theory; 5.3 Bayesian Framework and Statistical Analysis; 5.4 Stochastic Learning; 5.5 Parameter Tuning and Control; 5.5.1 Parameter Tuning; 5.5.2 Parameter Control; 5.6 Hyper-Optimization; 5.6.1 A Multiobjective View; 5.6.2 Self-tuning Framework; 5.6.3 Self-tuning Firefly Algorithm; 5.7 Multidisciplinary Perspectives; 5.8 Future Directions; 6 Applications of Nature-Inspired Algorithms; 6.1 Design Optimization in Engineering
000890273 5058_ $$a6.1.1 Design of a Spring6.1.2 Pressure Vessel Design; 6.1.3 Speed Reducer Design; 6.1.4 Other Design Problems; 6.2 Inverse Problems and Parameter Identification; 6.3 Image Processing; 6.4 Classification, Clustering and Feature Selection; 6.5 Travelling Salesman Problem; 6.6 Vehicle Routing; 6.7 Scheduling; 6.8 Software Testing; 6.9 Deep Belief Networks; 6.10 Swarm Robots; References; Index
000890273 506__ $$aAccess limited to authorized users.
000890273 520__ $$aThis 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.
000890273 588__ $$aDescription based on online resource; title from digital title page (viewed on June 25, 2019).
000890273 650_0 $$aInternet$$xMathematical models.
000890273 650_0 $$aAlgorithms$$xMathematical models.
000890273 650_0 $$aWorld Wide Web$$xMathematical models.
000890273 7001_ $$aHe, Xing-Shi,$$eauthor.
000890273 830_0 $$aSpringerBriefs in optimization.
000890273 852__ $$bebk
000890273 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-16936-7$$zOnline Access$$91397441.1
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000890273 980__ $$aEBOOK
000890273 980__ $$aBIB
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