Evolutionary Optimization Algorithms


Evolutionary Optimization Algorithms
by: Altaf Q. H. Badar
Publisher Finelybook 出版社:CRC Press; 1st edition (12 Oct. 2021)
Language 语言:English
pages 页数:273 pages
ISBN-10 书号:0367750546
ISBN-13 书号:9780367750541

Book 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.
Contents
Preface
Chapter 1: Introduction
Chapter 2: Optimization Functions
Chapter 3: Genetic Algorithm
Chapter 4: Differential Evolution
Chapter 5: Particle Swarm Optimization
Chapter 6: Artificial Bee Colony
Chapter 7: Shuffled Frog Leaping Algorithm
Chapter 8: Grey Wolf Optimizer
Chapter 9: Teaching Learning Based Optimization
Chapter 10: Introduction to other Optimization Techniques
Real-Time Application of pso
Optimization Techniques in Python
Standard Optimization problems
Bibliography


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