Chun-Wei Tsai, Ming-Chao Chiang

Handbook of Metaheuristic Algorithms (eBook, ePUB)

From Fundamental Theories to Advanced Applications

• Format: ePub

Produktbeschreibung

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.

• Presents a unified framework for metaheuristics and describes well-known algorithms and their variants
• Introduces fundamentals and advanced topics for solving engineering optimization problems, e.g., scheduling problems, sensors deployment problems, and clustering problems
• Includes source code based on the unified framework for metaheuristics used as examples to show how TS, SA, GA, ACO, PSO, DE, parallel metaheuristic algorithm, hybrid metaheuristic, local search, and other advanced technologies are realized in programming languages such as C++ and Python

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 622
• Erscheinungstermin: 1. Juni 2023
• Englisch
• ISBN-13: 9780443191091
• Artikelnr.: 68150973

Autorenporträt

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.

Inhaltsangabe

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