Nature-Inspired Optimization Algorithms 2nd Edition

Автор: literator от 17-02-2021, 18:45, Коментариев: 0

Категория: КНИГИ » ПРОГРАММИРОВАНИЕ

Nature-Inspired Optimization Algorithms 2nd EditionНазвание: Nature-Inspired Optimization Algorithms 2nd Edition
Автор: Xin-She Yang
Издательство: Academic Press/Elsevier
Год: 2021
Страниц: 297
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

Nature-Inspired Optimization Algorithms, Second Edition provides an introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, and multi-objective optimization. This book can serve as an introductory book for graduates, for lecturers in computer science, engineering and natural sciences, and as a source of inspiration for new applications.

Behind any computer simulation and computational methods, there are always some algorithms at work. The basic components and the ways of they interact determine how an algorithm works and the efficiency and performance of the algorithm. The Chapter I introduces algorithms and analyze the essence of the algorithms. Then we discuss the general formulation of an optimization problem and describe the modern approaches in terms of swarm intelligence and bio-inspired computation. A brief history of nature-inspired algorithms is reviewed.

Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literature
Provides a theoretical understanding and practical implementation hints
Presents a step-by-step introduction to each algorithm
Includes four new chapters covering mathematical foundations, techniques for solving discrete and combination optimization problems, data mining techniques and their links to optimization algorithms, and the latest deep learning techniques, background and various applications

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