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

• Autore: Yang, Xin-She
• Pubblicazione: Cham, : Springer, 2019
• Titolo uniforme: Mathematical Foundations of Nature-Inspired Algorithms
• Descrizione fisica: xi, 107 p. : ill. ; 24 cm
• Soggetto topico: 68Q25 - Analysis of algorithms and problem complexity [MSC 2020]; 60J10 - Markov chains (discrete-time Markov processes on discrete state spaces) [MSC 2020]; 90Cxx - Mathematical programming [MSC 2020]; 46N10 - Applications of functional analysis in optimization, convex analysis, mathematical programming, economics [MSC 2020]; 97N40 - Numerical analysis (educational aspects) [MSC 2020]; 37N40 - Dynamical systems in optimization and economics [MSC 2020]; 68W20 - Randomized algorithms [MSC 2020]; 80M50 - Optimization problems in thermodynamics and heat transfer [MSC 2020]
• Soggetto non controllato: 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
• Altri autori: He, Xing-Shi
• Titolo autorizzato: Mathematical Foundations of Nature-Inspired Algorithms
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: VAN0126993
• Lo trovi qui: Univ. Vanvitelli
• Localizzazioni e accesso elettronico: http://doi.org/10.1007/978-3-030-16936-7
• Serie: SpringerBriefs in optimization Berlin [etc.] . -Springer