Evolutionary Optimization Algorithms

1. Auflage 2021
ISBN: 978-0-367-75054-1
Verlag: CRC Press

Buch, Englisch, 274 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 558 g

This comprehensive reference text discusses evolutionary optimization techniques, to find optimal solutions for single and multi-objective problems.

The text presents each evolutionary optimization algorithm along with its history and other working equations. It also discusses variants and hybrids of optimization techniques. The text presents step-by-step solution to a problem and includes software’s like MATLAB and Python for solving optimization problems. It covers important optimization algorithms including single objective optimization, multi objective optimization, Heuristic optimization techniques, shuffled frog leaping algorithm, bacteria foraging algorithm and firefly algorithm.

Aimed at senior undergraduate and graduate students in the field of electrical engineering, electronics engineering, mechanical engineering, and computer science and engineering, this text:

- Provides step-by-step solution for each evolutionary optimization algorithm.

- Provides flowcharts and graphics for better understanding of optimization techniques.

- Discusses popular optimization techniques include particle swarm optimization and genetic algorithm.

- Presents every optimization technique along with the history and working equations.

- Includes latest software like Python and MATLAB.

Zielgruppe

Academic, Postgraduate, and Undergraduate Advanced

Autoren/Hrsg.

Q H Badar, Altaf

Fachgebiete

• Technische Wissenschaften Maschinenbau | Werkstoffkunde Maschinenbau
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
• Mathematik | Informatik Mathematik Algebra Zahlentheorie
• Technische Wissenschaften Technik Allgemein Technik: Allgemeines

Inhaltsverzeichnis

1. Introduction. 2. Optimization Functions. 3. Genetic Algorithm. 4. Differential Evolution. 5. Particle Swarm Optimization. 6. Artificial Bee Colony. 7. Shuffled Frog Leaping Algorithm. 8. Grey Wolf Optimizer. 9. Teaching Learning Based Optimization. 10. Introduction to Other Optimization Techniques. 11. Real Time Application of PSO. 12. Optimization Techniques in Python. 13. Standard Optimization Problems. 14. Bibliography.

Über Autor(innen)

Altaf Q. H. Badar is currently working as an assistant professor, department of electrical engineering, National Institute of Technology, Warangal. His research areas include artificial intelligence applications to power systems, evolutionary optimization techniques, and smart home energy management systems. He has taught courses including electric and magnetic fields, and real-time control of power systems. He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and Indian Society for Technical Education (ISTE).