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E-raamat: Evolutionary Optimization Algorithms [Taylor & Francis e-raamat]

(NIT Warangal, India)
  • Formaat: 274 pages, 145 Tables, black and white; 100 Line drawings, black and white; 100 Illustrations, black and white
  • Ilmumisaeg: 12-Oct-2021
  • Kirjastus: CRC Press
  • ISBN-13: 9781003206477
Teised raamatud teemal:
  • Taylor & Francis e-raamat
  • Hind: 170,80 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Tavahind: 244,00 €
  • Säästad 30%
Evolutionary Optimization AlgorithmsSuurem pilt
  • Formaat: 274 pages, 145 Tables, black and white; 100 Line drawings, black and white; 100 Illustrations, black and white
  • Ilmumisaeg: 12-Oct-2021
  • Kirjastus: CRC Press
  • ISBN-13: 9781003206477
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9781003206477
The text covers evolutionary optimizing algorithms with the help of MATLAB and Python programming in a comprehensive manner. It will be an ideal reference text for readers in diverse engineering fields including electrical, electronics and communication, computer science and mechanical engineering.

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.

Preface xi
Chapter 1 Introduction
1(10)
1.1 Introduction
1(1)
1.2 Terminology
1(3)
1.3 Optimization Problem
4(3)
1.3.1 Constraints
6(1)
1.4 MultiObjective Optimization Problem
7(1)
1.5 Optimization Techniques
7(3)
1.6 Conclusion
10(1)
Chapter 2 Optimization Functions
11(18)
2.1 Introduction
11(1)
2.2 Standard Optimization Functions
11(5)
2.3 Traveling Salesman Problem
16(4)
2.4 Hill Climbing
20(9)
Chapter 3 Genetic Algorithm
29(42)
3.1 Introduction
29(1)
3.2 Terminology
30(1)
3.3 Fundamental Concept
31(17)
3.3.1 Selection
32(10)
3.3.2 Crossover
42(4)
3.3.3 Mutation
46(2)
3.4 Algorithm and Pseudocode
48(1)
3.5 Flowchart
49(1)
3.6 Example
50(10)
3.7 Variants and Hybrid
60(11)
3.7.1 Variants
61(8)
3.7.2 Hybrid
69(2)
Chapter 4 Differential Evolution
71(18)
4.1 Introduction
71(1)
4.2 Terminology
71(1)
4.3 Fundamental Concept
72(1)
4.4 Algorithm and Pseudocode
73(1)
4.5 Flowchart
74(1)
4.6 Example
75(9)
4.7 Variants and Hybrid
84(5)
4.7.1 Variants
84(3)
4.7.2 Hybrid DE
87(2)
Chapter 5 Particle Swarm Optimization
89(26)
5.1 Introduction
89(2)
5.2 Terminology
91(1)
5.3 Evolution of Particle Swarm Optimization
92(3)
5.4 Fundamental Concept
95(1)
5.5 Algorithm and Pseudocode
96(1)
5.6 Flowchart
97(1)
5.7 Example
98(8)
5.8 Variants and Hybrid
106(9)
5.8.1 Variants
106(6)
5.8.2 Hybrid PSO
112(3)
Chapter 6 Artificial Bee Colony
115(22)
6.1 Introduction
115(1)
6.2 Terminology
116(2)
6.3 Fundamental Concept
118(3)
6.4 Algorithm and Pseudocode
121(2)
6.5 Flowchart
123(3)
6.6 Example
126(11)
Chapter 7 Shuffled Frog Leaping Algorithm
137(28)
7.1 Introduction
137(1)
7.2 Terminology
138(1)
7.3 Fundamental Concept
138(5)
7.4 Algorithm and Pseudocode
143(3)
7.5 Flowchart
146(4)
7.6 Example
150(15)
Chapter 8 Grey Wolf Optimizer
165(26)
8.1 Introduction
165(2)
8.2 Terminology
167(1)
8.3 Fundamental Concept
168(2)
8.4 Algorithm and Pseudocode
170(2)
8.5 Flowchart
172(1)
8.6 Example
173(18)
Chapter 9 Teaching Learning Based Optimization
191(20)
9.1 Introduction
191(1)
9.2 Terminology
192(1)
9.3 Fundamental Concept
192(2)
9.4 Algorithm and Pseudocode
194(2)
9.5 Flowchart
196(4)
9.6 Example
200(11)
Chapter 10 Introduction to Other Optimization Techniques
211(12)
10.1 Introduction
211(1)
10.2 Bacteria Foraging Algorithm
211(1)
10.3 Whale Optimization
212(2)
10.4 Bat Algorithm
214(2)
10.5 Firefly Algorithm
216(1)
10.6 Gravitational Search Algorithm
217(2)
10.7 Reducing Variable Trend Search Method
219(2)
10.8 Summary
221(2)
Real-Time Application of PSO 223(12)
Optimization Techniques in Python 235(6)
Standard Optimization Problems 241(4)
Bibliography 245(14)
Index 259
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).
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