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. Metaheuristic Optimization Algorithms have become indispensable tools, with applications in data analysis, text mining, classification problems, computer vision, image analysis, pattern recognition, medicine, and many others. Most complex systems problems involve a continuous flow of data that makes it impossible to manage and analyze manually. The outcome depends on the processing of high-dimensional data, most of it irregular and unordered, present in various forms such as text, images, videos, audio, and graphics. The authors of Meta-Heuristic Optimization Algorithms provide 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, followed by the mathematical and procedural structure of the algorithm, associated pseudocode, and real-world case studies to demonstrate how each algorithm can be applied to a variety of scientific and engineering solutions.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xiii 7. 1. Particle swarm optimization algorithm: review and applications - p. 1 8. 2. Social spider optimization algorithm: survey and new applications - p. 15 9. 3. Animal migration optimization algorithm: novel optimizer, analysis, and applications - p. 33 10. 4. A Survey of cuckoo search algorithm: optimizer and new applications - p. 45 11. 5. Teaching–learning-based optimization algorithm: analysis study and its application - p. 59 12. 6. Arithmetic optimization algorithm: a review and analysis - p. 73 13. 7. Aquila optimizer: review, results and applications - p. 89 14. 8. Whale optimization algorithm: analysis and full survey - p. 105 15. 9. Spider monkey optimizations: application review and results - p. 117 16. 10. Marine predator’s algorithm: a survey of recent applications - p. 133 17. 11. Quantum approximate optimization algorithm: a review study and problems - p. 147 18. 12. Crow search algorithm: a survey of novel optimizer and its recent applications - p. 167 19. 13. A review of Henry gas solubility optimization algorithm: a robust optimizer and applications - p. 177 20. 14. A survey of the manta ray foraging optimization algorithm - p. 193 21. 15. A review of mothflame optimization algorithm: analysis and applications - p. 205 22. 16. Gradient-based optimizer: analysis and application of the Berry software product - p. 221 23. 17. A review of krill herd algorithm: optimization and its applications - p. 231 24. 18. Salp swarm algorithm: survey, analysis, and new applications - p. 241 25. Index - p. 259
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. Metaheuristic Optimization Algorithms have become indispensable tools, with applications in data analysis, text mining, classification problems, computer vision, image analysis, pattern recognition, medicine, and many others. Most complex systems problems involve a continuous flow of data that makes it impossible to manage and analyze manually. The outcome depends on the processing of high-dimensional data, most of it irregular and unordered, present in various forms such as text, images, videos, audio, and graphics. The authors of Meta-Heuristic Optimization Algorithms provide 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, followed by the mathematical and procedural structure of the algorithm, associated pseudocode, and real-world case studies to demonstrate how each algorithm can be applied to a variety of scientific and engineering solutions.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. List of contributors - p. xiii 7. 1. Particle swarm optimization algorithm: review and applications - p. 1 8. 2. Social spider optimization algorithm: survey and new applications - p. 15 9. 3. Animal migration optimization algorithm: novel optimizer, analysis, and applications - p. 33 10. 4. A Survey of cuckoo search algorithm: optimizer and new applications - p. 45 11. 5. Teaching–learning-based optimization algorithm: analysis study and its application - p. 59 12. 6. Arithmetic optimization algorithm: a review and analysis - p. 73 13. 7. Aquila optimizer: review, results and applications - p. 89 14. 8. Whale optimization algorithm: analysis and full survey - p. 105 15. 9. Spider monkey optimizations: application review and results - p. 117 16. 10. Marine predator’s algorithm: a survey of recent applications - p. 133 17. 11. Quantum approximate optimization algorithm: a review study and problems - p. 147 18. 12. Crow search algorithm: a survey of novel optimizer and its recent applications - p. 167 19. 13. A review of Henry gas solubility optimization algorithm: a robust optimizer and applications - p. 177 20. 14. A survey of the manta ray foraging optimization algorithm - p. 193 21. 15. A review of mothflame optimization algorithm: analysis and applications - p. 205 22. 16. Gradient-based optimizer: analysis and application of the Berry software product - p. 221 23. 17. A review of krill herd algorithm: optimization and its applications - p. 231 24. 18. Salp swarm algorithm: survey, analysis, and new applications - p. 241 25. Index - p. 259
