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Metaheuristic Optimization Algorithms
Optimizers, Analysis, and Applications
- 1st Edition - May 5, 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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Request a sales quoteMetaheuristic 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
- Cover image
- Title page
- Table of Contents
- Copyright
- List of contributors
- 1. Particle swarm optimization algorithm: review and applications
- Abstract
- 1.1 Introduction
- 1.2 Particle swarm optimization
- 1.3 Related works
- 1.4 Discussion
- 1.5 Conclusion
- References
- 2. Social spider optimization algorithm: survey and new applications
- Abstract
- 2.1 Introduction
- 2.2 Related work
- 2.3 Social spider optimization method
- 2.4 Experiment result
- 2.5 Discussion
- 2.6 Conclusion
- References
- 3. Animal migration optimization algorithm: novel optimizer, analysis, and applications
- Abstract
- 3.1 Introduction
- 3.2 Animal migration optimization algorithm procedure
- 3.3 Related works
- 3.4 Discussion
- 3.5 Conclusion
- References
- 4. A Survey of cuckoo search algorithm: optimizer and new applications
- Abstract
- 4.1 Introduction
- 4.2 Cuckoo search algorithm
- 4.3 Related works
- 4.4 Method
- 4.5 Discussion
- 4.6 Advanced work
- 4.7 Conclusion
- References
- 5. Teaching–learning-based optimization algorithm: analysis study and its application
- Abstract
- 5.1 Introduction
- 5.2 Teaching–learning-based optimization
- 5.3 Literature review
- 5.4 Discussion and future works
- 5.5 Conclusion
- References
- 6. Arithmetic optimization algorithm: a review and analysis
- Abstract
- 6.1 Introduction
- 6.2 Arithmetic optimization algorithm
- 6.3 Related Works
- 6.4 Discussion
- 6.5 Conclusion and future work
- References
- 7. Aquila optimizer: review, results and applications
- Abstract
- 7.1 Introduction
- 7.2 Procedure
- 7.3 Related works
- 7.4 Discussion
- 7.5 Conclusion
- References
- 8. Whale optimization algorithm: analysis and full survey
- Abstract
- 8.1 Introduction
- 8.2 The whale optimization algorithm
- 8.3 Related work
- 8.4 Discussion
- 8.5 Conclusion and future work
- References
- 9. Spider monkey optimizations: application review and results
- Abstract
- 9.1 Introduction
- 9.2 Spider monkey optimization algorithm
- 9.3 Related work
- 9.4 Discussion
- 9.5 Conclusion and future works
- References
- 10. Marine predator’s algorithm: a survey of recent applications
- Abstract
- 10.1 Introduction
- 10.2 Marine Predator's Algorithm
- 10.3 Related Works
- 10.4 Discussion
- 10.5 Conclusion and Future Work
- References
- 11. Quantum approximate optimization algorithm: a review study and problems
- Abstract
- 11.1 Introduction
- 11.2 Methods
- 11.3 Related works
- 11.4 Result
- 11.5 Discussion
- 11.6 Conclusion
- References
- 12. Crow search algorithm: a survey of novel optimizer and its recent applications
- Abstract
- 12.1 Introduction
- 12.2 Crow search algorithm
- 12.3 Related work
- 12.4 Conclusion and future work
- References
- 13. A review of Henry gas solubility optimization algorithm: a robust optimizer and applications
- Abstract
- 13.1 Introduction
- 13.2 Henry gas solubility optimization
- 13.3 Related works
- 13.4 Discussion
- 13.5 Conclusion and future works
- References
- 14. A survey of the manta ray foraging optimization algorithm
- Abstract
- 14.1 Introduction
- 14.2 Manta ray foraging optimization
- 14.3 Related works
- 14.4 Discussion
- 14.5 Conclusion and future work
- References
- 15. A review of mothflame optimization algorithm: analysis and applications
- Abstract
- 15.1 Introduction
- 15.2 Moth Flame Optimization Algorithm
- 15.3 The Growth of the Moth Flame Optimization Algorithm in the Literature
- 15.4 Application
- 15.5 Discussion
- 15.6 Concluding Remarks
- References
- 16. Gradient-based optimizer: analysis and application of the Berry software product
- Abstract
- 16.1 Introduction
- 16.2 Literature review
- 16.3 Results and discussion
- 16.4 Conclusion
- References
- 17. A review of krill herd algorithm: optimization and its applications
- Abstract
- 17.1 Introduction
- 17.2 Krill herd algorithm procedure
- 17.3 Related work
- 17.4 Conclusion
- References
- 18. Salp swarm algorithm: survey, analysis, and new applications
- Abstract
- 18.1 Introduction
- 18.2 Related work procedure of the algorithm
- 18.3 Methods
- 18.4 Results
- 18.5 Conclusion
- References
- Index
- No. of pages: 290
- Language: English
- Edition: 1
- Published: May 5, 2024
- Imprint: Morgan Kaufmann
- Paperback ISBN: 9780443139253
- eBook ISBN: 9780443139260
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