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Metaheuristic Computation with MATLAB® [Kõva köide]

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  • Formaat: Hardback, 280 pages, kõrgus x laius: 254x178 mm, kaal: 680 g, 3 Tables, black and white; 100 Illustrations, black and white
  • Ilmumisaeg: 23-Jul-2020
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 0367438860
  • ISBN-13: 9780367438869
Teised raamatud teemal:
Metaheuristic Computation with MATLAB®Suurem pilt
  • Formaat: Hardback, 280 pages, kõrgus x laius: 254x178 mm, kaal: 680 g, 3 Tables, black and white; 100 Illustrations, black and white
  • Ilmumisaeg: 23-Jul-2020
  • Kirjastus: Chapman & Hall/CRC
  • ISBN-10: 0367438860
  • ISBN-13: 9780367438869
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780367438869.html
Märksõnad:
Metaheuristic algorithms are considered as generic optimization tools that can solve very complex problems characterized by having very large search spaces. Metaheuristic methods reduce the effective size of the search space through the use of effective search strategies.

Book Features:











Provides a unified view of the most popular metaheuristic methods currently in use





Includes the necessary concepts to enable readers to implement and modify already known metaheuristic methods to solve problems





Covers design aspects and implementation in MATLAB®





Contains numerous examples of problems and solutions that demonstrate the power of these methods of optimization

The material has been written from a teaching perspective and, for this reason, this book is primarily intended for undergraduate and postgraduate students of artificial intelligence, metaheuristic methods, and/or evolutionary computation. The objective is to bridge the gap between metaheuristic techniques and complex optimization problems that profit from the convenient properties of metaheuristic approaches. Therefore, engineer practitioners who are not familiar with metaheuristic computation will appreciate that the techniques discussed are beyond simple theoretical tools, since they have been adapted to solve significant problems that commonly arise in such areas.
Preface xi
Acknowledgments xvii
Authors xix
Chapter 1 Introduction and Main Concepts
1(28)
1.1 Introduction
1(2)
1.2 Classical Optimization Methods
3(4)
1.2.1 The Gradient Descent Method
3(1)
1.2.2 Gradient Computation
4(1)
1.2.3 Computational Example in MATLAB
4(3)
1.3 Metaheuristic Methods
7(5)
1.3.1 The Generic Procedure of a Metaheuristic Algorithm
11(1)
1.4 Exploitation And Exploration
12(1)
1.5 Probabilistic Decision And Selection
12(2)
1.5.1 Probabilistic Decision
12(1)
1.5.2 Probabilistic Selection
13(1)
1.6 Random Search
14(5)
1.6.1 Computational Implementation in MATLAB
15(4)
1.7 Simulated Annealing
19(10)
1.7.1 Computational Example in MATLAB
22(3)
Exercises
25(3)
References
28(1)
Chapter 2 Genetic Algorithms (GA)
29(36)
2.1 Introduction
29(2)
2.2 Binary GA
31(12)
2.2.1 Selection Operator
33(2)
2.2.2 Binary Crossover Operator
35(1)
2.2.3 Binary Mutation
36(1)
2.2.4 Computational Procedure
37(6)
2.3 GA With Real Parameters
43(22)
2.3.1 Real-Parameter Crossover Operator
43(10)
2.3.2 Real-Parameter Mutation Operator
53(4)
2.3.3 Computational Procedure
57(6)
References
63(2)
Chapter 3 Evolutionary Strategies (ES)
65(48)
3.1 Introduction
65(1)
3.2 The (1 + 1) ES
66(2)
3.2.1 Initialization
66(1)
3.2.2 Mutation
66(1)
3.2.3 Selection
67(1)
3.3 Computational Procedure Of The (1 + 1) ES
68(2)
3.3.1 Description of the Algorithm (1 + 1) ES
68(2)
3.4 Matlab Implementation Of Algorithm (1 + 1) ES
70(3)
3.5 ES Variants
73(40)
3.5.1 Adaptive (1 +1) ES
73(7)
3.5.2 (μ + 1)ES
80(10)
3.5.3 (μ + λ) ES
90(4)
3.5.4 (μ, λ)ES
94(6)
3.5.5 (μ, α, λ, β)ES
100(1)
3.5.6 Adaptive (μ+ λ) ES and (μ λ) ES
100(12)
References
112(1)
Chapter 4 Moth-Flame Optimization (MFO) Algorithm
113(26)
4.1 MFO Metaphor
113(1)
4.2 MFO Search Strategy
114(4)
4.2.1 Initialization
114(1)
4.2.2 Cross Orientation
115(1)
4.2.3 Other Mechanisms for the Balance of Exploration-Exploitation
116(2)
4.2.4 MFO Variants
118(1)
4.3 MFO Computation Procedure
118(5)
4.3.1 Algorithm Description
119(4)
4.4 Implementation Of MFO In Matlab
123(3)
4.5 Applications Of MFO
126(13)
4.5.1 Application of the MFO to Unconstrained Problems
127(4)
4.5.2 Application of the MFO to Problems with Constrained
131(6)
References
137(2)
Chapter 5 Differential Evolution (DE)
139(20)
5.1 Introduction
139(1)
5.2 DE Search Strategy
140(7)
5.2.1 Population Structure
141(1)
5.2.2 Initialization
142(1)
5.2.3 Mutation
142(3)
5.2.4 Crossover
145(1)
5.2.5 Selection
146(1)
5.3 Computational Process Of DE
147(2)
5.3.1 Implementation of the DE Scheme
147(1)
5.3.2 The General Process of DE
148(1)
5.4 Matlab Implementation Of DE
149(4)
5.5 Spring Design Using The De Algorithm
153(6)
References
157(2)
Chapter 6 Particle Swarm Optimization (PSO) Algorithm
159(24)
6.1 Introduction
159(1)
6.2 PSO Search Strategy
160(3)
6.2.1 Initialization
160(1)
6.2.2 Particle Velocity
161(1)
6.2.3 Particle Movement
162(1)
6.2.4 PSO Analysis
163(1)
6.2.5 Inertia Weighting
163(1)
6.3 Computing Procedure Of PSO
163(5)
6.3.1 Algorithm Description
164(4)
6.4 Matlab Implementation Of The PSO Algorithm
168(3)
6.5 Applications Of The PSO Method
171(12)
6.5.1 Application of PSO without Constraints
171(4)
6.5.2 Application of the PSO to Problems with Constraints
175(6)
References
181(2)
Chapter 7 Artificial Bee Colony (ABC) Algorithm
183(18)
7.1 Introduction
183(2)
7.2 Artificial Bee Colony
185(10)
7.2.1 Initialization of the Population
185(1)
7.2.2 Sending Worker Bees
185(1)
7.2.3 Selecting Food Sources by Onlooker Bees
186(1)
7.2.4 Determining the Exploring Bees
186(1)
7.2.5 Computational Process ABC
186(1)
7.2.6 Computational Example in MATLAB
187(8)
7.3 Recent Applications Of The Abc Algorithm In Image Processing
195(6)
7.3.1 Applications in the Area of Image Processing
195(1)
7.3.1.1 Image Enhancement
195(1)
7.3.1.2 Image Compression
196(1)
7.3.1.3 Border Detection
197(1)
7.3.1.4 Clustering
197(1)
7.3.1.5 Image Classification
197(1)
7.3.1.6 Fusion in Images
198(1)
7.3.1.7 Scene Analysis
198(1)
7.3.1.8 Pattern Recognition
198(1)
7.3.1.9 Object Detection
199(1)
References
199(2)
Chapter 8 Cuckoo Search (CS) Algorithm
201(28)
8.1 Introduction
201(2)
8.2 CS Strategy
203(3)
8.2.1 Levy Flight (A)
204(1)
8.2.2 Replace Some Nests by Constructing New Solutions (B)
205(1)
8.2.3 Elitist Selection Strategy (C)
205(1)
8.2.4 Complete CS Algorithm
205(1)
8.3 CS Computational Procedure
206(3)
8.4 The Multimodal Cuckoo Search (MCS)
209(9)
8.4.1 Memory Mechanism (D)
210(1)
8.4.1.1 Initialization Phase
211(1)
8.4.1.2 Capture Phase
211(1)
8.4.1.3 Significant Fitness Value Rule
211(2)
8.4.1.4 Non-Significant Fitness Value Rule
213(1)
8.4.2 New Selection Strategy (E)
214(1)
8.4.3 Depuration Procedure (F)
215(3)
8.4.4 Complete MCS Algorithm
218(1)
8.5 Analysis OF CS
218(11)
8.5.1 Experimental Methodology
218(4)
8.5.2 Comparing MCS Performance for Functions f1 - f7
222(2)
8.5.3 Comparing MCS Performance for Functions f8 - f14
224(2)
References
226(3)
Chapter 9 Metaheuristic Multimodal Optimization
229(28)
9.1 Introduction
229(1)
9.2 Diversity Through Mutation
230(1)
9.3 Preselection
231(1)
9.4 Crowding Model
231(1)
9.5 Sharing Function Model
231(15)
9.5.1 Numerical Example for Sharing Function Calculation
234(2)
9.5.2 Computational Example in MATLAB
236(1)
9.5.3 Genetic Algorithm without Multimodal Capacities
237(5)
9.5.4 Genetic Algorithm with Multimodal Capacities
242(4)
9.6 Firefly Algorithm
246(11)
9.6.1 Computational Example in MATLAB
248(4)
Exercises
252(3)
References
255(2)
Index 257
Erik Cuevas is a professor in the Department of Electronics at the University of Guadalajara, Mexico.

Alma Rodríguez is a PhD candidate in electronics and computer science at the University of Guadalajara, Mexico.