Nature-Inspired Optimization Algorithms
- ISBN-10
- 0124167454
- ISBN-13
- 9780124167452
- Series
- Nature-Inspired Optimization Algorithms
- Category
- Computers
- Pages
- 300
- Language
- English
- Published
- 2014-02-17
- Publisher
- Elsevier
- Author
- Xin-She Yang
Description
Nature-Inspired Optimization Algorithms provides a systematic 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 well-chosen 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, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference. Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literature Provides a theoretical understanding as well as practical implementation hints Provides a step-by-step introduction to each algorithm
Get the book
Other editions
-
- 2021-02-08
- 168 pages
- Ebook
- Walter de Gruyter GmbH & Co KG
-
- 2021-02-08
- 168 pages
- Ebook
- Walter de Gruyter GmbH & Co KG
Similar books
-
Nature-Inspired Optimization Algorithms with Java: A Look at Optimization Techniques
By Shashank JainGain insight into the world of nature-inspired optimization techniques and algorithms. This book will prepare you to apply different nature-inspired optimization techniques to solve problems using Java.
-
Nature-inspired Metaheuristic Algorithms
By Xin-She YangThis book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms for global optimization, including ant and bee algorithms, bat algorithm, cuckoo search, differential evolution, firefly algorithm, genetic ...
-
Introduction to Nature-Inspired Optimization
By George Lindfield, John PennyThis book provides a quick reference to practicing engineers, researchers and graduate students who work in the field of optimization.
-
Advanced Optimization by Nature-Inspired Algorithms
By Omid Bozorg-HaddadThis book, compiles, presents, and explains the most important meta-heuristic and evolutionary optimization algorithms whose successful performance has been proven in different fields of engineering, and it includes application of these ...
-
Nature-Inspired Algorithms and Applied Optimization
By Xin-She YangThis book reviews the state-of-the-art developments in nature-inspired algorithms and their applications in various disciplines, ranging from feature selection and engineering design optimization to scheduling and vehicle routing.
-
Nature-Inspired Computing and Optimization: Theory and Applications
By Xin-She Yang, Srikanta Patnaik, Kazumi NakamatsuThe book provides readers with a snapshot of the state of the art in the field of nature-inspired computing and its application in optimization.
-
Nature-Inspired Methods for Metaheuristics Optimization: Algorithms and Applications in Science and Engineering
By Fouad Bennis, Rajib Kumar BhattacharjyaThis book gathers together a set of chapters covering recent development in optimization methods that are inspired by nature.
-
Nature-Inspired Optimization Algorithms for Fuzzy Controlled Servo Systems
By Radu-Emil Precup, Radu-Codrut DavidThis book aims at a large category of audience including graduate students, engineers (designers, practitioners and researchers), and everyone who faces challenging control problems.
-
Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications
By Modestus O. Okwu, Lagouge K. TartibuThe second and third fittest solution is Beta and delta wolves respectively. Omega follows these three wolves ... The other wolves will update their positions based on the position of Alpha, Beta, and Delta [1, 5]. −→D∝ ∣ ∣∣C1 = .
-
Harmony Search and Nature Inspired Optimization Algorithms: Theory and Applications, ICHSA 2018
By Joong Hoon Kim, Anupam Yadav, Kusum DeepThe book covers different aspects of real-world applications of optimization algorithms.