Metaheuristic Optimization Algorithms - Optimizers, Analysis, and Applications (for Raymond Rhine)

✍ Scribed by Laith Abualigah

• Publisher: Elsevier Inc.
• Year: 2024
• Tongue: English
• Leaves: 250
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

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