Metaheuristic Optimization Algorithms

Metaheuristic Optimization Algorithms

by Laith Abualigah
Released May 2024
Publisher(s): Morgan Kaufmann
ISBN: 9780443139260

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Book description

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.
  • 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

Table of contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. List of contributors
  6. 1. Particle swarm optimization algorithm: review and applications
    1. Abstract
    2. 1.1 Introduction
    3. 1.2 Particle swarm optimization
    4. 1.3 Related works
    5. 1.4 Discussion
    6. 1.5 Conclusion
    7. References
  7. 2. Social spider optimization algorithm: survey and new applications
    1. Abstract
    2. 2.1 Introduction
    3. 2.2 Related work
    4. 2.3 Social spider optimization method
    5. 2.4 Experiment result
    6. 2.5 Discussion
    7. 2.6 Conclusion
    8. References
  8. 3. Animal migration optimization algorithm: novel optimizer, analysis, and applications
    1. Abstract
    2. 3.1 Introduction
    3. 3.2 Animal migration optimization algorithm procedure
    4. 3.3 Related works
    5. 3.4 Discussion
    6. 3.5 Conclusion
    7. References
  9. 4. A Survey of cuckoo search algorithm: optimizer and new applications
    1. Abstract
    2. 4.1 Introduction
    3. 4.2 Cuckoo search algorithm
    4. 4.3 Related works
    5. 4.4 Method
    6. 4.5 Discussion
    7. 4.6 Advanced work
    8. 4.7 Conclusion
    9. References
  10. 5. Teaching–learning-based optimization algorithm: analysis study and its application
    1. Abstract
    2. 5.1 Introduction
    3. 5.2 Teaching–learning-based optimization
    4. 5.3 Literature review
    5. 5.4 Discussion and future works
    6. 5.5 Conclusion
    7. References
  11. 6. Arithmetic optimization algorithm: a review and analysis
    1. Abstract
    2. 6.1 Introduction
    3. 6.2 Arithmetic optimization algorithm
    4. 6.3 Related Works
    5. 6.4 Discussion
    6. 6.5 Conclusion and future work
    7. References
  12. 7. Aquila optimizer: review, results and applications
    1. Abstract
    2. 7.1 Introduction
    3. 7.2 Procedure
    4. 7.3 Related works
    5. 7.4 Discussion
    6. 7.5 Conclusion
    7. References
  13. 8. Whale optimization algorithm: analysis and full survey
    1. Abstract
    2. 8.1 Introduction
    3. 8.2 The whale optimization algorithm
    4. 8.3 Related work
    5. 8.4 Discussion
    6. 8.5 Conclusion and future work
    7. References
  14. 9. Spider monkey optimizations: application review and results
    1. Abstract
    2. 9.1 Introduction
    3. 9.2 Spider monkey optimization algorithm
    4. 9.3 Related work
    5. 9.4 Discussion
    6. 9.5 Conclusion and future works
    7. References
  15. 10. Marine predator’s algorithm: a survey of recent applications
    1. Abstract
    2. 10.1 Introduction
    3. 10.2 Marine Predator's Algorithm
    4. 10.3 Related Works
    5. 10.4 Discussion
    6. 10.5 Conclusion and Future Work
    7. References
  16. 11. Quantum approximate optimization algorithm: a review study and problems
    1. Abstract
    2. 11.1 Introduction
    3. 11.2 Methods
    4. 11.3 Related works
    5. 11.4 Result
    6. 11.5 Discussion
    7. 11.6 Conclusion
    8. References
  17. 12. Crow search algorithm: a survey of novel optimizer and its recent applications
    1. Abstract
    2. 12.1 Introduction
    3. 12.2 Crow search algorithm
    4. 12.3 Related work
    5. 12.4 Conclusion and future work
    6. References
  18. 13. A review of Henry gas solubility optimization algorithm: a robust optimizer and applications
    1. Abstract
    2. 13.1 Introduction
    3. 13.2 Henry gas solubility optimization
    4. 13.3 Related works
    5. 13.4 Discussion
    6. 13.5 Conclusion and future works
    7. References
  19. 14. A survey of the manta ray foraging optimization algorithm
    1. Abstract
    2. 14.1 Introduction
    3. 14.2 Manta ray foraging optimization
    4. 14.3 Related works
    5. 14.4 Discussion
    6. 14.5 Conclusion and future work
    7. References
  20. 15. A review of mothflame optimization algorithm: analysis and applications
    1. Abstract
    2. 15.1 Introduction
    3. 15.2 Moth Flame Optimization Algorithm
    4. 15.3 The Growth of the Moth Flame Optimization Algorithm in the Literature
    5. 15.4 Application
    6. 15.5 Discussion
    7. 15.6 Concluding Remarks
    8. References
  21. 16. Gradient-based optimizer: analysis and application of the Berry software product
    1. Abstract
    2. 16.1 Introduction
    3. 16.2 Literature review
    4. 16.3 Results and discussion
    5. 16.4 Conclusion
    6. References
  22. 17. A review of krill herd algorithm: optimization and its applications
    1. Abstract
    2. 17.1 Introduction
    3. 17.2 Krill herd algorithm procedure
    4. 17.3 Related work
    5. 17.4 Conclusion
    6. References
  23. 18. Salp swarm algorithm: survey, analysis, and new applications
    1. Abstract
    2. 18.1 Introduction
    3. 18.2 Related work procedure of the algorithm
    4. 18.3 Methods
    5. 18.4 Results
    6. 18.5 Conclusion
    7. References
  24. Index

Product information

  • Title: Metaheuristic Optimization Algorithms
  • Author(s): Laith Abualigah
  • Release date: May 2024
  • Publisher(s): Morgan Kaufmann
  • ISBN: 9780443139260