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

  • Formaat: 260 pages, 1 Tables, black and white; 125 Illustrations, black and white
  • Ilmumisaeg: 15-Jun-2020
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-13: 9780429289071
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  • Taylor & Francis e-raamat
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Nature-Inspired Optimization AlgorithmsSuurem pilt
  • Formaat: 260 pages, 1 Tables, black and white; 125 Illustrations, black and white
  • Ilmumisaeg: 15-Jun-2020
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-13: 9780429289071
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9780429289071

Nature-Inspired Optimization Algorithms, a comprehensive work on the most popular optimization algorithms based on nature, starts with an overview of optimization going from the classical to the latest swarm intelligence algorithm. Nature has a rich abundance of flora and fauna that inspired the development of optimization techniques, providing us with simple solutions to complex problems in an effective and adaptive manner. The study of the intelligent survival strategies of animals, birds, and insects in a hostile and ever-changing environment has led to the development of techniques emulating their behavior.

This book is a lucid description of fifteen important existing optimization algorithms based on swarm intelligence and superior in performance. It is a valuable resource for engineers, researchers, faculty, and students who are devising optimum solutions to any type of problem ranging from computer science to economics and covering diverse areas that require maximizing output and minimizing resources. This is the crux of all optimization algorithms.

Features:

  • Detailed description of the algorithms along with pseudocode and flowchart

  • Easy translation to program code that is also readily available in Mathworks website for some of the algorithms

  • Simple examples demonstrating the optimization strategies are provided to enhance understanding

  • Standard applications and benchmark datasets for testing and validating the algorithms are included

This book is a reference for undergraduate and post-graduate students. It will be useful to faculty members teaching optimization. It is also a comprehensive guide for researchers who are looking for optimizing resources in attaining the best solution to a problem. The nature-inspired optimization algorithms are unconventional, and this makes them more efficient than their traditional counterparts.

Preface xi
Author xiii
1 Introduction 1(16)
1.1 Introduction
1(1)
1.2 Fundamentals of Optimization
1(4)
1.3 Types of Optimization Problems
5(2)
1.4 Examples of Optimization
7(2)
1.5 Formulation of Optimization Problem
9(1)
1.6 Classification of Optimization Algorithms
10(4)
1.7 Traveling Salesman Problem and Knapsack Problem
14(2)
1.8 Summary
16(1)
2 Classical Optimization Methods 17(12)
2.1 Introduction
17(1)
2.2 Mathematical Model of Optimization
18(1)
2.3 Linear Programming
19(3)
2.3.1 Simplex Method
20(1)
2.3.2 Revised Simplex Method
20(1)
2.3.3 Kamarkar's Method
20(1)
2.3.4 Duality Theorem
21(1)
2.3.5 Decomposition Principle
22(1)
2.3.6 Transportation Problem
22(1)
2.4 Non-Linear Programming
22(2)
2.4.1 Quadratic Programming
23(1)
2.4.2 Geometric Programming
23(1)
2.5 Dynamic Programming
24(1)
2.6 Integer Programming
25(1)
2.7 Stochastic Programming
26(1)
2.8 Lagrange Multiplier Method
26(1)
2.9 Summary
27(1)
References
28(1)
3 Nature-Inspired Algorithms 29(18)
3.1 Introduction
29(1)
3.2 Traditional versus Nature-Inspired Algorithms
30(1)
3.3 Bioinspired Algorithms
31(1)
3.4 Swarm Intelligence
32(5)
3.5 Metaheuristics
37(2)
3.6 Diversification and Intensification
39(1)
3.7 No Free Lunch Theorem
40(1)
3.8 Parameter Tuning and Control
40(1)
3.9 Algorithm
41(1)
3.10 Pseudocode
42(1)
3.11 Summary
43(1)
References
44(3)
4 Genetic Algorithm 47(14)
4.1 Introduction
47(1)
4.2 Basics of Genetic Algorithm
47(2)
4.3 Genetic Operators
49(3)
4.4 Example of GA
52(1)
4.5 Algorithm
53(1)
4.6 Pseudocode
54(2)
4.7 Schema Theory
56(2)
4.8 Prisoner's Dilemma Problem
58(1)
4.9 Variants and Hybrids of GA
59(1)
4.10 Summary
59(1)
References
60(1)
5 Genetic Programming 61(16)
5.1 Introduction
61(1)
5.2 Basics of Genetic Programming
62(1)
5.3 Data Structures for Genetic Programming
63(3)
5.4 Binary Tree Traversals
66(1)
5.5 Genetic Programming Operators
67(4)
5.6 Genetic Programming Algorithm
71(1)
5.7 Pseudocode
72(2)
5.8 Variants of the Algorithm
74(1)
5.9 Summary
75(1)
References
75(2)
6 Particle Swarm Optimization 77(12)
6.1 Introduction
77(2)
6.2 Swarm Behavior
79(2)
6.3 Particle Swarm Optimization
81(4)
6.3.1 Algorithm
81(2)
6.3.2 Pseudocode
83(2)
6.4 Variants of the Algorithm
85(1)
6.5 Summary
86(1)
References
87(2)
7 Differential Evolution 89(10)
7.1 Introduction
89(1)
7.2 Differential Evolution
90(6)
7.2.1 Algorithm
92(2)
7.2.2 Pseudocode
94(2)
7.3 Variants of the Algorithm
96(2)
7.4 Summary
98(1)
References
98(1)
8 Ant Colony Optimization 99(16)
8.1 Introduction
99(1)
8.2 Ant Colony Characteristics
99(5)
8.3 Ant Colony Optimization
104(6)
8.3.1 Traveling Salesman Problem
105(1)
8.3.2 Algorithm
106(2)
8.3.3 Pseudocode
108(2)
8.4 Variants of the Algorithm
110(2)
8.5 Summary
112(1)
References
113(2)
9 Bee Colony Optimization 115(16)
9.1 Introduction
115(1)
9.2 Honey Bee Characteristics
116(5)
9.3 Bee Colony Optimization
121(4)
9.3.1 Algorithm
121(2)
9.3.2 Pseudocode
123(2)
9.4 Variants of the Algorithm
125(4)
9.5 Summary
129(1)
References
130(1)
10 Fish School Search Algorithm 131(12)
10.1 Introduction
131(1)
10.2 Fish School Behavior
131(4)
10.3 Fish School Search Optimization
135(6)
10.3.1 Algorithm
137(2)
10.3.2 Pseudocode
139(2)
10.4 Variants and Applications
141(1)
10.5 Summary
141(1)
References
142(1)
11 Cuckoo Search Algorithm 143(14)
11.1 Introduction
143(1)
11.2 Cuckoo Bird Behavior
143(3)
11.3 Levy Flights
146(1)
11.4 Cuckoo Search Optimization
147(5)
11.4.1 Algorithm
149(1)
11.4.2 Pseudocode
150(2)
11.5 Variants of the Algorithm
152(2)
11.5.1 Discrete Cuckoo Search Algorithm
152(1)
11.5.2 Binary Cuckoo Search (BCS) Algorithm
152(1)
11.5.3 Multi-Objective Cuckoo Search Algorithm (MOCS)
153(1)
11.6 Summary
154(1)
References
155(2)
12 Firefly Algorithm 157(10)
12.1 Introduction
157(1)
12.2 Firefly Behavior and Characteristics
157(3)
12.3 Firefly-Inspired Optimization
160(5)
12.3.1 Algorithm
162(1)
12.3.2 Pseudocode
163(2)
12.4 Variants and Applications
165(1)
12.5 Summary
165(1)
References
166(1)
13 Bat Algorithm 167(14)
13.1 Introduction
167(1)
13.2 Behavior of Bats in Nature
168(4)
13.3 Bat Optimization Algorithm
172(4)
13.3.1 Algorithm
173(1)
13.3.2 Pseudocode
174(2)
13.4 Variants and Applications
176(2)
13.5 Summary
178(1)
References
178(3)
14 Flower Pollination Algorithm 181(16)
14.1 Introduction
181(1)
14.2 Flower Pollination
182(5)
14.3 Flower Pollination Optimization
187(5)
14.3.1 Algorithm
189(1)
14.3.2 Pseudocode
190(2)
14.4 Variants of the Algorithm
192(2)
14.5 Summary
194(1)
References
194(3)
15 Gray Wolf Optimization 197(14)
15.1 Introduction
197(1)
15.2 Gray Wolf Characteristics
197(3)
15.3 Gray Wolf Optimization
200(6)
15.3.1 Gray Wolf Encircling Prey
201(1)
15.3.2 Hunting Behavior of Gray Wolves
202(1)
15.3.3 Attacking of Prey by Gray Wolves
202(1)
15.3.4 Gray Wolves Searching for Prey (Exploration)
203(3)
15.4 Variants and Applications
206(3)
15.5 Summary
209(1)
References
209(2)
16 Elephant Herding Optimization 211(8)
16.1 Introduction
211(1)
16.2 Elephant Herding Behavior
212(1)
16.3 Elephant Herding Optimization
213(4)
16.3.1 Algorithm
213(2)
16.3.2 Pseudocode
215(2)
16.4 Variants of the Algorithm
217(1)
16.5 Summary
217(1)
References
218(1)
17 Crow Search Algorithm 219(10)
17.1 Introduction
219(1)
17.2 Crows in Nature
219(3)
17.3 Crow Search Optimization
222(5)
17.3.1 Algorithm
224(1)
17.3.2 Pseudocode
225(2)
17.4 Variants and Applications
227(1)
17.5 Summary
228(1)
References
228(1)
18 Raven Roosting Optimization Algorithm 229(12)
18.1 Introduction
229(1)
18.2 Raven Roosting Behavior
230(4)
18.3 Raven Roosting Optimization
234(4)
18.3.1 Algorithm
234(2)
18.3.2 Pseudocode
236(1)
Flowchart
237(1)
18.4 Variants of the Algorithm
238(1)
18.5 Summary
239(1)
References
239(2)
19 Applications 241(6)
19.1 Introduction
241(1)
19.2 Benchmark Test Functions
241(2)
19.3 Applications
243(2)
19.3.1 Traveling Salesman Problem
244(1)
19.3.2 Knapsack Problem
244(1)
19.3.3 Graph Coloring Problem
244(1)
19.3.4 Job Scheduling Problem
244(1)
19.3.5 Feature Reduction Problem
244(1)
19.3.6 Network Routing Problem
245(1)
19.4 Summary
245(2)
20 Conclusion 247(6)
Index 253
Dr. Vasuki A is currently working as Professor in the Department of Mechatronics Engineering at Kumaraguru College of Technology, Coimbatore, India. She has more than 27 years of teaching, research and academic administration experience. She has completed B.E in Electronics and Communication Engineering from PSG College of Technology in 1989. She has completed her postgraduate degree M.E Applied Electronics from Coimbatore Institute of Technology in 1991. She has done her Ph.D in Image Compression from PSG College of Technology under Anna University Chennai in 2010. Her research interests are Signal Processing, Image Processing, Communication and Optimization. She has published 3 Book Chapters, 38 National and International Journal papers and 60 National and International Conference papers. She has guided 30 PG projects and 50 UG projects. She is an approved Research Supervisor under Anna University Chennai and is currently guiding 9 research scholars.
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