Handbook of Metaheuristic Algorithms
From Fundamental Theories to Advanced Applications
Published on 5. June 2023
Book
Paperback/Softback
622 pages
978-0-443-19108-4 (ISBN)
Description
Handbook of Metaheuristic Algorithms: From Fundamental Theories to Advanced Applications provides a brief introduction to metaheuristic algorithms from the ground up, including basic ideas and advanced solutions. Although readers may be able to find source code for some metaheuristic algorithms on the Internet, the coding styles and explanations are generally quite different, and thus requiring expanded knowledge between theory and implementation. This book can also help students and researchers construct an integrated perspective of metaheuristic and unsupervised algorithms for artificial intelligence research in computer science and applied engineering domains.
Metaheuristic algorithms can be considered the epitome of unsupervised learning algorithms for the optimization of engineering and artificial intelligence problems, including simulated annealing (SA), tabu search (TS), genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), differential evolution (DE), and others. Distinct from most supervised learning algorithms that need labeled data to learn and construct determination models, metaheuristic algorithms inherit characteristics of unsupervised learning algorithms used for solving complex engineering optimization problems without labeled data, just like self-learning, to find solutions to complex problems.
Metaheuristic algorithms can be considered the epitome of unsupervised learning algorithms for the optimization of engineering and artificial intelligence problems, including simulated annealing (SA), tabu search (TS), genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), differential evolution (DE), and others. Distinct from most supervised learning algorithms that need labeled data to learn and construct determination models, metaheuristic algorithms inherit characteristics of unsupervised learning algorithms used for solving complex engineering optimization problems without labeled data, just like self-learning, to find solutions to complex problems.
Reviews / Votes
"The present book provides resources, references and alternative ways of simple and fast solution methods and algorithms. It is organized in such a way that the readers can not only realize most of the metaheuristic algorithms, but also use them to solve real-world problems. The book can be used by students and researchers as a reference for self-study to enter this research domain or by teachers as a reference or textbook for a course.... The ultimate goal of the book is to share with the audience the authors' experience and know-how on metaheuristic algorithms from the ground up, that is, from the basic ideas to advanced technologies, even for readers who have no background knowledge in artificial intelligence or machine learning." --Haydar Akca, zbMATHOpenMore details
Series
Language
English
Place of publication
San Diego
United States
Publishing group
Elsevier Science Publishing Co Inc
Target group
Professional and scholarly
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 152 mm
Width: 227 mm
Thickness: 34 mm
Weight
1020 gr
ISBN-13
978-0-443-19108-4 (9780443191084)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Chun-Wei Tsai | Ming-Chao Chiang
Handbook of Metaheuristic Algorithms
From Fundamental Theories to Advanced Applications
E-Book
05/2023
Academic Press
€160.00
Available for download
Persons
Chun-Wei Tsai received his Ph.D. degree from the Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan in 2009 where he is currently an assistant professor. He has more than 20 years of experience in metaheuristic algorithms and their applications and has served as the secretary general of Taiwan Association of Cloud Computing from 2018 to 2021; as an associate editor for Journal of Internet Technology, IEEE Access, IET Networks, and IEEE Internet of Things Journal since 2014, 2017, 2018, and 2020, respectively. He has also been a member of the Editorial Board of the Elsevier Journal of Network and Computer Applications (JNCA) and Elsevier ICT Express since 2017 and 2021, respectively. His research interests include computational intelligence, data mining, cloud computing, and internet of things. Ming-Chao Chiang received his B.S. degree in Management Science from National Chiao Tung University, Hsinchu, Taiwan, R.O.C. in 1978, and the M.S., M.Phil., and Ph.D. degrees in Computer Science from Columbia University, New York, USA in 1991, 1998, and 1998, respectively. He has over 12 years of experience in the software industry encompassing a wide variety of roles and responsibilities in both large and start-up companies in Taiwan and the USA before joining the faculty of the Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan, R.O.C. in 2003, where he is currently a professor. His research interests include image processing, evolutionary computation, and system software.
Content
PART 1 Fundamentals 1. Introduction
2. Optimization problems
3. Traditional methods
4. Metaheuristic algorithms
5. Simulated annealing
6. Tabu search
7. Genetic algorithm
8. Ant colony optimization
9. Particle swarm optimization
10. Differential evolution
PART 2 Advanced technologies
11. Solution encoding and initialization operator
12. Transition operator
13. Evaluation and determination operators
14. Parallel metaheuristic algorithm
15. Hybrid metaheuristic and hyperheuristic algorithms
16. Local search algorithm
17. Pattern reduction
18. Search economics
19. Advanced applications
20. Conclusion and future research directions
A. Interpretations and analyses of simulation results
B. Implementation in Python
2. Optimization problems
3. Traditional methods
4. Metaheuristic algorithms
5. Simulated annealing
6. Tabu search
7. Genetic algorithm
8. Ant colony optimization
9. Particle swarm optimization
10. Differential evolution
PART 2 Advanced technologies
11. Solution encoding and initialization operator
12. Transition operator
13. Evaluation and determination operators
14. Parallel metaheuristic algorithm
15. Hybrid metaheuristic and hyperheuristic algorithms
16. Local search algorithm
17. Pattern reduction
18. Search economics
19. Advanced applications
20. Conclusion and future research directions
A. Interpretations and analyses of simulation results
B. Implementation in Python