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Algorithms for Variable-Size Optimization: Applications in Space Systems and Renewable Energy [Kõva köide]

  • Formaat: Hardback, 230 pages, kõrgus x laius: 234x156 mm, kaal: 590 g, 41 Tables, black and white; 8 Illustrations, color; 88 Illustrations, black and white
  • Ilmumisaeg: 05-Apr-2021
  • Kirjastus: CRC Press Inc
  • ISBN-10: 0815360169
  • ISBN-13: 9780815360162
Teised raamatud teemal:
Algorithms for Variable-Size Optimization: Applications in Space Systems and Renewable EnergySuurem pilt
  • Formaat: Hardback, 230 pages, kõrgus x laius: 234x156 mm, kaal: 590 g, 41 Tables, black and white; 8 Illustrations, color; 88 Illustrations, black and white
  • Ilmumisaeg: 05-Apr-2021
  • Kirjastus: CRC Press Inc
  • ISBN-10: 0815360169
  • ISBN-13: 9780815360162
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780815360162.html
Märksõnad:
Many systems architecture optimization problems are characterized by a variable number of optimization variables. Many classical optimization algorithms are not suitable for such problems. The book presents recently developed optimization concepts that are designed to solve such problems. These new concepts are implemented using genetic algorithms and differential evolution. The examples and applications presented show the effectiveness of the use of these new algorithms in optimizing systems architectures.

The book focuses on systems architecture optimization. It covers new algorithms and its applications, besides reviewing fundamental mathematical concepts and classical optimization methods. It also provides detailed modeling of sample engineering problems. The book is suitable for graduate engineering students and engineers. The second part of the book includes numerical examples on classical optimization algorithms, which are useful for undergraduate engineering students.

While focusing on the algorithms and their implementation, the applications in this book cover the space trajectory optimization problem, the optimization of earth orbiting satellites orbits, and the optimization of the wave energy converter dynamic system: architecture and control. These applications are illustrated in the starting of the book, and are used as case studies in later chapters for the optimization methods presented in the book.

Arvustused

"The title of this book perfectly sums up the topic and scope of the text: optimization algorithms. A specialist in aerospace engineering, Abdelkhalik (Iowa State Univ.) provides a solid background review of the application domains and traditional approaches to optimization techniques. This textbook is a suitable reference for graduate students, post-docs, faculty, and engineers working on optimization algorithms to solve similar problems in the space and renewable energy sectors."

R. S. Stansbury, Embry-Riddle Aeronautical University

Preface iv
Symbol Description x
SECTION I BACKGROUND AND MOTIVATION
1 Introduction and Background
2(13)
1.1 Mathematical Background
3(5)
1.1.1 Definitions
3(1)
1.1.2 Differentiability and Taylor's Theorem
4(1)
1.1.3 Orthogonal Vectors
5(1)
1.1.3.1 Gram-Schmidt Orthogonalization
6(1)
1.1.3.2 Q-Orthogonal (Q-Conjugate) Directions
7(1)
1.1.4 Convergence Rates
7(1)
1.2 Systems Architecture Optimization
8(7)
1.2.1 Interplanetary Trajectory Optimization
8(3)
1.2.2 Microgrid Optimization
11(1)
1.2.3 Traffic Network Signal Coordination Planning
11(1)
1.2.4 Optimal Grouping Problems
12(1)
1.2.5 Systems Design Optimization
12(1)
1.2.6 Structural Topology Optimization
13(1)
1.2.7 Pixel Classification Problems
14(1)
2 Modeling Examples of Variable-Size Design Space Problems
15(19)
2.1 Satellite Orbit Design Optimization
15(4)
2.2 Interplanetary Trajectory Optimization
19(6)
2.3 Optimization of Wave Energy Converters
25(9)
SECTION II CLASSICAL OPTIMIZATION ALGORITHMS
3 Fundamentals and Core Algorithms
34(13)
3.1 Equal Interval Search Algorithm
35(2)
3.2 Golden Section Method
37(4)
3.3 Linear versus Nonlinear Optimization
41(6)
3.3.1 Linear Programming
42(1)
3.3.2 Nonlinear Programming
43(4)
4 Unconstrained Optimization
47(13)
4.1 Non-Gradient Algorithms
47(4)
4.1.1 Cyclic Coordinated Descent Method
48(1)
4.1.2 Pattern Search Method
48(3)
4.1.3 Powell's Method
51(1)
4.2 Gradient-Based Optimization
51(7)
4.2.1 Steepest Descent Method
52(1)
4.2.2 Conjugate Gradient Method
52(4)
4.2.3 Variable Metric Methods
56(2)
4.3 Second Order Methods
58(2)
5 Numerical Algorithms for Constrained Optimization
60(16)
5.1 Indirect Methods
61(7)
5.1.1 Barrier Methods
61(2)
5.1.2 Exterior Penalty Function Methods
63(2)
5.1.3 Augmented Lagrange Multiplier Method
65(2)
5.1.4 Algorithm for Indirect Methods
67(1)
5.2 Direct Methods
68(8)
5.2.1 Sequential Linear Programming
68(1)
5.2.2 Quadratic Programming
69(3)
5.2.3 Sequential Quadratic Programming
72(4)
SECTION III VARIABLE-SIZE DESIGN SPACE OPTIMIZATION
6 Hidden Genes Genetic Algorithms
76(41)
6.1 Introduction to Global Optimization
76(2)
6.2 Genetic Algorithms
78(6)
6.2.1 Similarity Templates (schemata)
80(1)
6.2.2 Markov Chain Model
80(4)
6.3 Fundamental Concepts of Hidden Genes Genetic Algorithms
84(5)
6.3.1 The Hidden Genes Concept in Biology
84(1)
6.3.2 Concept of Optimization using Hidden Genes Genetic Algorithms
85(1)
6.3.3 Outline of a Simple HGGA
86(3)
6.4 The Schema Theorem and the Simple HGGA
89(5)
6.4.1 Reproduction
89(4)
6.4.2 Crossover
93(1)
6.4.3 Mutation
93(1)
6.5 Hidden Genes Assignment Methods
94(3)
6.5.1 Logical Evolution of Tags
95(1)
6.5.2 Stochastic Evolution of Tags
95(2)
6.6 Examples: VSDS Mathematical Functions
97(7)
6.6.1 Examples using Stochastically Evolving Tags
98(6)
6.6.2 Examples using Logically Evolving Tags
104(1)
6.7 Statistical Analysis
104(3)
6.8 Markov Chain Model of HGGA
107(8)
6.9 Final Remarks
115(2)
7 Structured Chromosome Genetic Algorithms
117(23)
7.1 Structured-Chromosome Evolutionary Algorithms (SCEAs)
118(6)
7.1.1 Crossover in SCGA
120(1)
7.1.2 Mutation in SCGA
121(1)
7.1.3 Transformation in SCDE
122(1)
7.1.4 Niching in SCGA and SCDE
123(1)
7.2 Trajectory Optimization using SCEA
124(12)
7.2.1 Earth-Mars Mission
126(2)
7.2.2 Earth-Saturn Mission (Cassini 2-like Mission)
128(3)
7.2.3 Jupiter Europa Orbiter Mission
131(5)
7.3 Comparisons and Discussion
136(4)
8 Dynamic-Size Multiple Population Genetic Algorithms
140(16)
8.1 The Concept of DSMPGA
140(2)
8.2 Application: Space Trajectory Optimization
142(3)
8.3 Numerical Examples
145(7)
8.4 Discussion
152(4)
SECTION IV APPLICATIONS
9 Space Trajectory Optimization
156(27)
9.1 Background
156(4)
9.2 A Simple Implementation of HGGA
160(16)
9.2.1 Optimization
160(4)
9.2.2 Numerical Results
164(8)
9.2.3 Discussion
172(4)
9.3 Trajectory Optimization using HGGA with Binary Tags
176(7)
9.3.1 Earth-Jupiter Mission using HGGA
176(1)
9.3.2 Earth-Jupiter Mission: Numerical Results and Comparisons
177(6)
10 Control and Shape Optimization of Wave Energy Converters
183(21)
10.1 A Conical Buoy in Regular Wave
187(2)
10.2 General Shape Buoys in Regular Waves
189(5)
10.3 WECs in Irregular Waves
194(4)
10.3.1 Simultaneous Optimization of Shape and Control
197(1)
10.4 Discussion
198(6)
Bibliography 204(11)
Index 215
Dr. Ossama Abdelkhalik is Associate Professor in the Department of Aerospace Engineering at Iowa State University. He received his PhD from Texas A&M University, College Station, TX, in Aerospace Engineering. His research interests are in Dynamics, Control, and Optimization with applications in space trajectory optimization, ocean wave energy conversion, and spacecraft dynamics. Dr. Abdelkhalik is associate fellow of AIAA, and currently a member of the AIAA technical committee on Astrodynamics.