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Practical Approach to Metaheuristics using LabVIEW and MATLAB® [Kõva köide]

, (Tecnologico de Monterrey, Mexico), (Tecnologico de Monterrey, Mexico), (Tecnológico de Monterrey, Mexico), (Tecnológico de Monterrey, Mexico),
  • Formaat: Hardback, 160 pages, kõrgus x laius: 234x156 mm, kaal: 399 g, 12 Tables, black and white; 126 Illustrations, black and white
  • Sari: Chapman & Hall/CRC Computer and Information Science Series
  • Ilmumisaeg: 15-Jun-2020
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 0367494264
  • ISBN-13: 9780367494261
Teised raamatud teemal:
Practical Approach to Metaheuristics using LabVIEW and MATLAB®Suurem pilt
  • Formaat: Hardback, 160 pages, kõrgus x laius: 234x156 mm, kaal: 399 g, 12 Tables, black and white; 126 Illustrations, black and white
  • Sari: Chapman & Hall/CRC Computer and Information Science Series
  • Ilmumisaeg: 15-Jun-2020
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 0367494264
  • ISBN-13: 9780367494261
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780367494261.html
Märksõnad:
Metaheuristic optimization has become a prime alternative for solving complex optimization problems in several areas. Hence, practitioners and researchers have been paying extensive attention to those metaheuristic algorithms that are mainly based on natural phenomena. However, when those algorithms are implemented, there are not enough books that deal with theoretical and experimental problems in a friendly manner so this book presents a novel structure that includes a complete description of the most important metaheuristic optimization algorithms as well as a new proposal of a new metaheuristic optimization named earthquake optimization. This book also has several practical exercises and a toolbox for MATLAB® and a toolkit for LabVIEW are integrated as complementary material for this book. These toolkits allow readers to move from a simulation environment to an experimentation one very fast. This book is suitable for researchers, students, and professionals in several areas, such as economics, architecture, computer science, electrical engineering, and control systems.

The unique features of this book are as follows:















Developed for researchers, undergraduate and graduate students, and practitioners





A friendly description of the main metaheuristic optimization algorithms





Theoretical and practical optimization examples





A new earthquake optimization algorithm





Updated state-of-the-art and research optimization projects





The authors are multidisciplinary/interdisciplinary lecturers and researchers who have written a structure-friendly learning methodology to understand each metaheuristic optimization algorithm presented in this book.
List Of Figures
xi
List Of Tables
xvii
Foreword xix
Preface xxi
Authors xxiii
Section I Basis
Chapter 1 Fundamental Concepts of Optimization
3(14)
1.1 Introduction
3(14)
1.1.1 Case Study of Electric Vehicle Driving and Temperature of Power Electronic Stage Optimization
6(3)
1.1.2 Conventional Optimization
9(3)
1.1.2.1 MATLAB Symbolic Code
12(5)
Chapter 2 Software Fundamentals for Optimization
17(44)
2.1 Matlab Fundamentals
17(26)
2.1.1 User Interface
17(2)
2.1.2 Variables Definition
19(2)
2.1.3 Constants
21(1)
2.1.4 Arrays, Vectors, and Matrices
21(4)
2.1.5 Basic Commands clc, who, clear, save, and load
25(1)
2.1.6 Basic Functions
26(2)
2.1.7 Programming
28(2)
2.1.8 Conditionals
30(1)
2.1.8.1 IF
31(1)
2.1.8.2 Switch
31(1)
2.1.9 Loops
32(1)
2.1.9.1 For Loop
32(2)
2.1.9.2 While Loop
34(1)
2.1.9.3 Break and Continue Loops
34(2)
2.1.10 Graphs
36(4)
2.1.11 Examples
40(3)
2.2 Simulink Fundamentals
43(4)
2.2.1 Working with Blocks
44(1)
2.2.2 Block Settings
45(1)
2.2.3 Simulink Models and MATLAB Variables
45(1)
2.2.4 Simple Simulink Example
46(1)
2.3 General Introduction To Labview
47(14)
Section II Metaheuristic Optimization
Chapter 3 Basic Metaheuristic Optimization Algorithms
61(4)
3.1 Exhaustive Search
61(1)
3.2 Random Optimization
61(1)
3.3 Nelder-Mead Algorithm
61(4)
Chapter 4 Evolution Algorithms
65(6)
4.1 Genetic Algorithms
65(1)
4.2 Simulated Annealing
66(1)
4.3 Tabu Search
67(4)
Chapter 5 Memetic Algorithms
71(6)
5.1 Ant Colony Optimization
71(1)
5.2 Particle Swarm Optimization
72(1)
5.3 Bat Optimization
73(1)
5.4 Gray Wolf Optimization
74(3)
Chapter 6 Geological Optimization
77(10)
6.1 Earthquake Algorithm
78(9)
6.1.1 Background
78(2)
6.1.2 P- And S-Wave Velocities
80(2)
6.1.3 Earthquake Optimization Algorithm
82(5)
Chapter 7 Optimization Matlab App And Labview Toolkit
87(16)
7.1 Matlab App
87(9)
7.1.1 Primary User Interface
88(1)
7.1.1.1 Algorithm User Interfaces
89(2)
7.1.2 Secondary User Interface
91(1)
7.1.2.1 Algorithm User Interfaces
91(1)
7.1.3 Individual Functions
92(4)
7.1.4 Matlab Simulink
96(1)
7.1.4.1 Mppt Simulink Models
96(1)
7.2 Labview App - Front Panels
96(7)
7.2.1 Ga Application In Labview
99(1)
7.2.2 Pso Algorithm Application In Labview
99(1)
7.2.3 Ba Application In Labview
100(1)
7.2.4 Aco Algorithm Application In Labview
100(1)
7.2.5 Gwo Algorithm Application In Labview
101(1)
7.2.6 Ea Application In Labview
101(1)
7.2.7 Nm Algorithm Application In Labview
102(1)
Chapter 8 Equations And Ongoing Projects
103(40)
8.1 Equations
103(4)
8.1.1 Equation 01
103(1)
8.1.2 Equation 02
103(1)
8.1.3 Equation 03
104(1)
8.1.4 Equation 04
105(1)
8.1.5 Equation 05
106(1)
8.2 Projects
107(4)
8.2.1 Project 01: Linear Square Regression
107(1)
8.2.2 Project 02: Welded Cantilever Minimization
108(1)
8.2.3 Project 03: Traveling Salesman Problem
109(1)
8.2.4 Project 04: 3D Traveling Salesman Problem
109(2)
8.3 Mppt Case Study
111(10)
8.3.1 Simulink Models
116(3)
8.3.2 Results
119(2)
8.4 Industry 4.0 Case Study: Three-Phase Inverter
121(6)
8.4.1 Matlab Optimization Solution
124(1)
8.4.2 Labview Optimization Solution
125(2)
8.4.3 Final Pcb
127(1)
8.5 Dc Motor Speed Controller With Pid Tuning Optimization Algorithm
127(5)
8.6 Optimization Algorithms Embedded In Labview Fpga
132(11)
8.6.1 Benchmark Functions
134(1)
8.6.1.1 Implementation into FPGA
134(4)
8.6.1.2 Benchmark Functions Utilization Summary
138(2)
8.6.2 Optimization Algorithms Implementation
140(1)
8.6.2.1 Pseudo Random Number Generation
141(1)
8.6.3 Optimization Algorithms Utilization Summary
142(1)
Appendix 143(4)
Bibliography 147(8)
Index 155
Pedro Ponce-Cruz, Arturo Molina Gutiérrez, Ricardo A. Ramírez-Mendoza, Efraín Méndez Flores, Alexandro Antonio Ortiz Espinoza, David Christopher Balderas Silva