Evolutionary Optimization Algorithms
1st Edition
Published on 29. October 2021
273 pages
978-1-000-46214-2 (ISBN)
Description
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.
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.
More details
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Illustrations
145 Tables, black and white; 100 Line drawings, black and white; 100 Illustrations, black and white
File size
25,57 MB
ISBN-13
978-1-000-46214-2 (9781000462142)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Altaf Q. H. Badar
Evolutionary Optimization Algorithms
Book
10/2024
1st Edition
CRC Press
€70.30
Shipment within 10-20 days
Altaf Q. H. Badar
Evolutionary Optimization Algorithms
Book
10/2021
1st Edition
CRC Press
€171.10
Shipment within 15-20 days
Person
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).
Content
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.
