Metaheuristic Optimization Algorithms
Optimizers, Analysis, and Applications
- 1st Edition - May 8, 2024
- Editor: Laith Abualigah
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 1 3 9 2 5 - 3
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 1 3 9 2 6 - 0
Metaheuristic Optimization Algorithms: Optimizers, Analysis, and Applications presents the most recent optimization algorithms and their applications across a wide range of scient… Read more
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Metaheuristic Optimization Algorithms: Optimizers, Analysis, and Applications presents the most recent optimization algorithms and their applications across a wide range of scientific and engineering research fields. The book provides readers with a comprehensive overview of eighteen optimization algorithms to address this complex data, including Particle Swarm Optimization Algorithm, Arithmetic Optimization Algorithm, Whale Optimization Algorithm, and Marine Predators Algorithm, along with new and emerging methods such as Aquila Optimizer, Quantum Approximate Optimization Algorithm, Manta-Ray Foraging Optimization Algorithm, and Gradient Based Optimizer, among others. Each chapter includes an introduction to the modeling concepts used to create the algorithm that is followed by the mathematical and procedural structure of the algorithm, associated pseudocode, and real-world case studies.
- World-renowned researchers and practitioners in Metaheuristics present the procedures and pseudocode for creating a wide range of optimization algorithms
- Helps readers formulate and design the best optimization algorithms for their research goals through case studies in a variety of real-world applications
- Helps readers understand the links between Metaheuristic algorithms and their application in Computational Intelligence, Machine Learning, and Deep Learning problems
Computer Scientists and researchers in Artificial Intelligence and Machine Learning, specifically in the field of developing Meta-Heuristic algorithms and applications. As such, academics, researchers, and professionals in a variety of research fields who work with AI, algorithms, and machine learning and their applications to various real-world research problems will be a target audience. Engineers who need to understand the impacts of AI and Machine Learning algorithms in complex systems. Could become a supplementary text for a wide range of upper-level undergrad and graduate-level Computer Science courses on AI, ML, and algorithm development
1. Particle Swarm Optimization Algorithm: Analysis and Applications
2. Social spider optimization algorithm: Analysis and Applications
3. Animal Migration Optimization Algorithm: Analysis And Applications
4. Cuckoo Search Algorithm: Analysis and Applications
5. Teaching Learning Based Optimization Algorithm: Analysis and Applications
6. Arithmetic Optimization Algorithm: Analysis and Applications
7. Aquila Optimizer: Algorithm, Analysis, and Applications
8. Whale Optimization Algorithm: Analysis and Applications
9. Spider Monkey Optimization Algorithm: Analysis and Applications
10. Marine Predators Algorithm: Analysis and Applications
11. Quantum Approximate Optimization Algorithm: Analysis and Applications
12. Crow Search Algorithm: Analysis and Applications
13. Henry Gas Solubility Optimization Algorithm: Analysis and Applications
14. Manta-Ray Foraging Optimization: Algorithm, Analysis, and Applications
15. Moth-flame Optimization Algorithm: Analysis and Applications
16. Gradient Based Optimizer: Analysis and Application of Berry Soft-ware Product
17. Krill Herd (KH) Algorithm: Analysis and Applications
18. Salp Swarm Algorithm: Optimization, Analysis, and Applications
2. Social spider optimization algorithm: Analysis and Applications
3. Animal Migration Optimization Algorithm: Analysis And Applications
4. Cuckoo Search Algorithm: Analysis and Applications
5. Teaching Learning Based Optimization Algorithm: Analysis and Applications
6. Arithmetic Optimization Algorithm: Analysis and Applications
7. Aquila Optimizer: Algorithm, Analysis, and Applications
8. Whale Optimization Algorithm: Analysis and Applications
9. Spider Monkey Optimization Algorithm: Analysis and Applications
10. Marine Predators Algorithm: Analysis and Applications
11. Quantum Approximate Optimization Algorithm: Analysis and Applications
12. Crow Search Algorithm: Analysis and Applications
13. Henry Gas Solubility Optimization Algorithm: Analysis and Applications
14. Manta-Ray Foraging Optimization: Algorithm, Analysis, and Applications
15. Moth-flame Optimization Algorithm: Analysis and Applications
16. Gradient Based Optimizer: Analysis and Application of Berry Soft-ware Product
17. Krill Herd (KH) Algorithm: Analysis and Applications
18. Salp Swarm Algorithm: Optimization, Analysis, and Applications
- Edition: 1
- Published: May 8, 2024
- Language: English
LA
Laith Abualigah
Dr. Laith Abualigah is an Associate Professor at Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Jordan. He is also a distinguished researcher at the School of Computer Science, Universiti Sains Malaysia. His main research interests focus on Arithmetic Optimization Algorithms (AOA), Bio-inspired Computing, Nature-inspired Computing, Swarm Intelligence, Artificial Intelligence, Meta-heuristic Modeling, as well as Optimization Algorithms, Evolutionary Computations, Information Retrieval, Text Clustering, Feature Selection, Combinatorial Problems, Optimization, Advanced Machine Learning, Big Data, and Natural Language Processing. Dr. Abualigah currently serves as Associate Editor of the Journal of Cluster Computing (Springer), the Journal of Soft Computing (Springer), and Journal of King Saud University - Computer and Information Sciences (Elsevier).
Affiliations and expertise
Associate Professor, Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, JordanRead Metaheuristic Optimization Algorithms on ScienceDirect