Evolutionary Optimization Algorithms

✍ Scribed by Altaf Q. H. Badar

• Publisher: CRC Press
• Year: 2021
• Tongue: English
• Leaves: 274
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
Preface
Chapter 1: Introduction
1.1. Introduction
1.2. Terminology
1.3. Optimization Problem
1.3.1. Constraints
1.4. MultiObjective Optimization Problem
1.5. Optimization Techniques
1.6. Conclusion
Chapter 2: Optimization Functions
2.1. Introduction
2.2. Standard Optimization Functions
2.3. Traveling Salesman Problem
2.4. Hill Climbing
Chapter 3: Genetic Algorithm
3.1. Introduction
3.2. Terminology
3.3. Fundamental Concept
3.3.1. Selection
3.3.2. CrossOver
3.3.3. Mutation
3.4. Algorithm and Pseudocode
3.5. Flowchart
3.6. Example
3.7. Variants and Hybrid
3.7.1. Variants
3.7.2. Hybrid
Chapter 4: Differential Evolution
4.1. Introduction
4.2. Terminology
4.3. Fundamental Concept
4.4. Algorithm and Pseudocode
4.5. Flowchart
4.6. Example
4.7. Variants and Hybrid
4.7.1. Variants
4.7.2. Hybrid DE
Chapter 5: Particle Swarm Optimization
5.1. Introduction
5.2. Terminology
5.3. Evolution of Particle Swarm Optimization
5.4. Fundamental Concept
5.5. Algorithm and Pseudocode
5.6. Flowchart
5.7. Example
5.8. Variants and Hybrid
5.8.1. Variants
5.8.2. Hybrid PSO
Chapter 6: Artificial Bee Colony
6.1. Introduction
6.2. Terminology
6.3. Fundamental Concept
6.4. Algorithm and Pseudocode
6.5. Flowchart
6.6. Example
Chapter 7: Shuffled Frog Leaping Algorithm
7.1. Introduction
7.2. Terminology
7.3. Fundamental Concept
7.4. Algorithm and Pseudocode
7.5. Flowchart
7.6. Example
Chapter 8: Grey Wolf Optimizer
8.1. Introduction
8.2. Terminology
8.3. Fundamental Concept
8.4. Algorithm and Pseudocode
8.5. Flowchart
8.6. Example
Chapter 9: Teaching Learning Based Optimization
9.1. Introduction
9.2. Terminology
9.3. Fundamental Concept
9.4. Algorithm and Pseudocode
9.5. Flowchart
9.6. Example
Chapter 10: Introduction to Other Optimization Techniques
10.1. Introduction
10.2. Bacteria Foraging Algorithm
10.3. Whale Optimization
10.4. Bat Algorithm
10.5. Firefly Algorithm
10.6. Gravitational Search Algorithm
10.7. Reducing Variable Trend Search Method
10.8. Summary
Real-Time Application of PSO
Optimization Techniques in Python
Standard Optimization Problems
Bibliography
Index