Handbook of Metaheuristic Algorithms
Academic Press Inc (Verlag)
978-0-443-19108-4 (ISBN)
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.
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.
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
| Erscheinungsdatum | 10.07.2023 |
|---|---|
| Reihe/Serie | Uncertainty, Computational Techniques, and Decision Intelligence |
| Verlagsort | San Diego |
| Sprache | englisch |
| Maße | 152 x 229 mm |
| Gewicht | 1020 g |
| Themenwelt | Informatik ► Theorie / Studium ► Algorithmen |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Technik | |
| ISBN-10 | 0-443-19108-5 / 0443191085 |
| ISBN-13 | 978-0-443-19108-4 / 9780443191084 |
| Zustand | Neuware |
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