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Nature-inspired Optimization Algorithms, Paperback by Yang, Xin-She, Brand Ne...

Description: Nature-inspired Optimization Algorithms, Paperback by Yang, Xin-She, ISBN 0128219866, ISBN-13 9780128219867, Brand New, Free shipping in the US Nature-Inspired Optimization Algorithms, Second Edition provides an introduction to all major nature-inspired algorithms for optimization. Th'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. 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

Price: 173.8 USD

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End Time: 2024-12-20T07:56:56.000Z

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Nature-inspired Optimization Algorithms, Paperback by Yang, Xin-She, Brand Ne...

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Book Title: Nature-inspired Optimization Algorithms

Number of Pages: 332 Pages

Language: English

Publication Name: Nature-Inspired Optimization Algorithms

Publisher: Elsevier Science & Technology

Publication Year: 2020

Subject: Biotechnology, General, Biomedical

Type: Textbook

Subject Area: Mathematics, Technology & Engineering, Science

Item Length: 9.2 in

Author: Xin-She Yang

Item Width: 7.5 in

Format: Trade Paperback

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