Handbook of Metaheuristic Algorithms: From Fundamental Theories to Advanced Applications

The Handbook of Metaheuristic Algorithms provides a comprehensive introduction to metaheuristic algorithms, covering basic ideas and advanced solutions. It helps students and researchers understand the relationship between theory and implementation and construct an integrated perspective of metaheuristic and unsupervised algorithms for AI research. Metaheuristic algorithms are unsupervised learning algorithms used for optimizing engineering and AI problems, such as SA, TS, GA, ACO, PSO, DE, and others.

Format: Paperback / softback
Length: 622 pages
Publication date: 05 June 2023
Publisher: Elsevier Science Publishing Co Inc

The Handbook of Metaheuristic Algorithms: From Fundamental Theories to Advanced Applications is a comprehensive guide that delves into the realm of metaheuristic algorithms, offering a foundational understanding from the very basics. While some metaheuristic algorithms may have their source code available on the Internet, it's important to note that the coding styles and explanations often differ, necessitating a deeper knowledge gap between theory and implementation. This book serves as a valuable resource for students and researchers seeking to gain an integrated perspective of metaheuristic and unsupervised algorithms in the fields of computer science and applied engineering.

Metaheuristic algorithms are considered the pinnacle of unsupervised learning algorithms for optimizing engineering and artificial intelligence problems. These algorithms encompass a wide range of techniques, such as simulated annealing (SA), tabu search (TS), genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), differential evolution (DE), and others. Unlike most supervised learning algorithms that rely on labeled data to learn and construct determination models, metaheuristic algorithms inherit the characteristics of unsupervised learning algorithms used for solving complex engineering optimization problems without labeled data. This self-learning ability allows them to find solutions to intricate problems, akin to the process of self-improvement.

In summary, the Handbook of Metaheuristic Algorithms provides a thorough introduction to metaheuristic algorithms, covering basic ideas, advanced solutions, and their applications in various domains. It serves as a valuable resource for students, researchers, and practitioners seeking to leverage these powerful optimization techniques to address complex problems in engineering and artificial intelligence.

Weight: 980g
Dimension: 152 x 227 x 34 (mm)
ISBN-13: 9780443191084