
- 296 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Evolutionary Optimization Algorithms
About this book
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.
Frequently asked questions
- Essential is ideal for learners and professionals who enjoy exploring a wide range of subjects. Access the Essential Library with 800,000+ trusted titles and best-sellers across business, personal growth, and the humanities. Includes unlimited reading time and Standard Read Aloud voice.
- Complete: Perfect for advanced learners and researchers needing full, unrestricted access. Unlock 1.4M+ books across hundreds of subjects, including academic and specialized titles. The Complete Plan also includes advanced features like Premium Read Aloud and Research Assistant.
Please note we cannot support devices running on iOS 13 and Android 7 or earlier. Learn more about using the app.
Information
Table of contents
- Cover
- Half Title
- Title Page
- Copyright Page
- Dedication
- 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
- Index