كتاب Mechanical Design Optimization Using Advanced Optimization Techniques
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 كتاب Mechanical Design Optimization Using Advanced Optimization Techniques

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مُساهمةموضوع: كتاب Mechanical Design Optimization Using Advanced Optimization Techniques    كتاب Mechanical Design Optimization Using Advanced Optimization Techniques  Emptyالإثنين 01 مايو 2023, 2:41 am

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Mechanical Design Optimization Using Advanced Optimization Techniques
R. Venkata Rao , Vimal J. Savsani

كتاب Mechanical Design Optimization Using Advanced Optimization Techniques  M_d_o_14
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Contents
1 Introduction 1
2 Advanced Optimization Techniques . 5
2.1 Genetic Algorithm 6
2.1.1 Selection . 6
2.1.2 Crossover 7
2.1.3 Mutation . 7
2.2 Artificial Immune Algorithm . 8
2.3 Differential Evolution 10
2.4 Biogeography-Based Optimization . 11
2.4.1 Migration 12
2.4.2 Mutation . 12
2.5 Particle Swarm Optimization . 14
2.5.1 Modifications in PSO 16
2.6 Artificial Bee Colony Algorithm 17
2.6.1 Modifications in ABC . 19
2.7 Harmony Elements Algorithm 20
2.7.1 Modifications in HEA . 20
2.8 Hybrid Algorithms 23
2.8.1 HPABC . 24
2.8.2 HBABC . 25
2.8.3 HDABC . 26
2.8.4 HGABC . 27
2.9 Shuffled Frog Leaping Algorithm . 28
2.10 Grenade Explosion Algorithm 29
References 32
ix3 Mechanical Design Optimization Using the Existing
Optimization Techniques . 35
3.1 Description of Different Mechanical Design
Optimization Problems . 35
3.1.1 Example 1: Optimization of a Gear Train 35
3.1.2 Example 2: Optimization of a Radial Ball Bearing 39
3.1.3 Example 3: Optimization of a Belleville Spring 44
3.1.4 Example 4: Optimization of a Multiple
Disc Clutch Brake 46
3.1.5 Example 5: Optimization of a Robot Gripper . 47
3.1.6 Example 6: Optimization of a Hydrodynamic
Thrust Bearing . 49
3.1.7 Example 7: Discrete Optimization of a Four
Stage Gear Train . 51
3.2 Applications of Advanced Optimization Techniques
to Different Design Optimization Problems
of Mechanical Elements 55
3.2.1 Example 1: Optimization of Gear Train . 55
3.2.2 Example 2: Optimization of Radial Ball Bearing . 57
3.2.3 Example 3: Optimization of Belleville Spring . 59
3.2.4 Example 4: Optimization of Multiple
Disc Clutch Brake 60
3.2.5 Example 5: Optimization of a Robotic Gripper 61
3.2.6 Example 6: Optimization of a Hydrostatic
Thrust Bearing . 62
3.2.7 Example 7: Discrete Optimization of a Four Stage
Gear Train . 63
References 67
4 Applications of Modified Optimization Algorithms
to the Unconstrained and Constrained Problems 69
4.1 Unconstrained Benchmark Functions (BM-UC) 69
4.2 Constrained Benchmark Functions (BM-C) . 72
4.3 Additional Mechanical Element Design Optimization
Problems (MD) 87
4.3.1 Example 8: Design of Pressure Vessel 87
4.3.2 Example 9: Design of Welded Beam . 88
4.3.3 Example 10: Design of Tension/Compression Spring . 90
4.3.4 Example 11: Design of a Speed Reducer 91
4.3.5 Example 12: Design of Stiffened Cylindrical Shell . 92
4.3.6 Example 13: Design of Step Cone Pulley 97
4.3.7 Example 14: Design of Screw Jack 98
4.3.8 Example 15: Design of C-Clamp 100
4.3.9 Example 16: Design of Hydrodynamic Bearing 101
x Contents4.3.10 Example 17: Design of Cone Clutch . 103
4.3.11 Example 18: Design of Cantilever Support . 104
4.3.12 Example 19: Design of Hydraulic Cylinder . 109
4.3.13 Example 20: Design of Planetary Gear Train . 110
4.4 Applications of Modified PSO 113
4.5 Applications of Modified ABC . 115
4.6 Applications of Modified HEA . 116
References 119
5 Applications of Hybrid Optimization Algorithms
to the Unconstrained and Constrained Problems 123
5.1 Applications of Hybrid Optimization Algorithms . 126
6 Development and Applications of a New
Optimization Algorithm 133
6.1 Teaching–Learning-Based Optimization . 134
6.1.1 Teacher Phase . 134
6.1.2 Learner Phase . 135
6.2 Demonstration of TLBO for Optimization 137
6.3 Comparison of TLBO with Other Optimization Techniques . 140
6.4 Implementation of TLBO for the Optimization
of Unconstrained Problems 140
6.4.1 Experiment 1 143
6.4.2 Experiment 2 144
6.4.3 Experiment 3 145
6.4.4 Experiment 4 146
6.4.5 Experiment 5 148
6.4.6 Experiment 6 149
6.5 Implementation of TLBO for the Optimization
of Constrained Benchmark Functions . 151
6.5.1 Experiment 7 151
6.5.2 Experiment 8 153
6.5.3 Experiment 9 154
6.6 Implementation of TLBO for the Design Optimization
of Mechanical Elements 154
6.6.1 Experiment 10 . 154
6.6.2 Experiment 11 . 157
6.6.3 Experiment 12 . 159
6.7 Implementation of TLBO for the Real
Parameter Optimization 163
6.7.1 Experiment 1 163
6.7.2 Experiment 2 165
6.7.3 Experiment 3 167
References 193
Contents xi7 Design Optimization of Selected Thermal Equipment
Using Advanced Optimization Techniques 195
7.1 Design Optimization of Thermoelectric Cooler 195
7.1.1 Thermal Modeling of Two-Stage TECs . 197
7.1.2 Multi-Objective Optimization and Formulation
of Objective Functions . 200
7.1.3 Application Example of a Two-Stage TEC . 201
7.2 Design Optimization of Shell and Tube Heat Exchanger
Using Shuffled Frog Leaping Algorithm . 207
7.2.1 Mathematical Model . 214
7.2.2 Case Study . 219
7.3 Design Optimization of Heat Pipe Using Grenade
Explosion Algorithm 223
7.3.1 Case Study . 226
References 228
8 Conclusions . 231
Appendix 1: Additional Demonstrative Examples Solved
by TLBO Algorithm . 235
Appendix 2: Sample Codes 295
Authors’ Biographies . 317
Index 319
Index
A
Ackley function, 270
Artificial bee colony, 2, 5, 17, 19, 24, 32, 33,
86, 87, 89, 119, 120, 125, 144, 192, 232
Artificial immune algorithm, 2, 5, 8, 9
B
Belleville spring, 35, 44, 45, 59, 60, 128, 129,
231
Biogeography-based optimization, 5, 11, 13,
26, 33, 119
C
Cantilever support, 103, 106, 107, 113, 117,
128, 129, 159, 160
C-clamp, 99, 106, 107, 113, 117, 128, 129,
159, 160
Cone clutch, 102, 103, 106, 107, 113, 117,
128, 129, 159, 160
Constrained benchmark functions, 64, 71, 72,
74, 76, 78, 80, 82, 84, 86, 105, 111, 112,
114–116, 118, 126, 127, 130, 152, 153,
155, 156, 158, 163, 164, 174, 176, 178,
184, 188, 231
D
Differential evolution, 2, 5, 10, 11, 24, 26, 27,
33, 86, 87, 89, 119, 120, 152, 155, 191,
192, 194, 209, 228
F
Four stage gear train, 35, 51
G
Gear train, 35, 51, 55, 56, 63, 106, 107, 109,
113, 117, 128, 129, 159, 160, 231
Genetic algorithm, 2, 5–7, 15, 24, 27, 28, 32,
33, 40, 55, 67, 118, 119, 125, 132, 152,
192, 196, 209–212, 228, 229, 232
Grenade explosion algorithm, 29, 31, 225, 227
Griewank function, 127, 144, 147
H
Harmony elements algorithm, 20, 21, 23, 32
Heat pipe, 223–227, 229
Heat transfer, 204, 206, 208–212, 214–217,
220–223, 225, 227–229
Himmelblau function, 286
HGABC, 24, 27, 28, 125–130, 149–151, 155,
156, 159, 160, 232
HPABC, 24, 25, 125–130, 149–151, 155, 156,
159, 160, 232
HBABC, 24–26, 125, 126–130, 149–151, 155,
156, 159, 160, 232
HDABC, 24, 26, 27, 125–130, 149, 150, 151,
155, 156, 158–162, 232, 233
Hybrid algorithms, 2, 3, 23–25, 27, 118, 122,
127, 130, 149, 153, 158, 232, 233
Hydraulic cylinder, 106–108, 113, 117, 128,
129, 159, 160, 231
H (cont.)
Hybrid biogeography-based artificial bee
colony algorithm, 24, 126
Hybrid differential evolution based artificial
bee colony algorithm, 24, 126
Hybrid genetic algorithm based artificial bee
colony algorithm, 24, 126
Hybrid particle swarm based artificial bee
colony algorithm (HPABC), 24, 126
Hydrodynamic thrust bearing, 49
Hydrostatic thrust bearing, 35, 49, 62
M
Mechanical design, 1–3, 5, 35–56, 58, 60,
62, 64, 66–68, 86, 106, 107,
113–115, 117–119, 122, 128–130,
132, 153, 155, 157–161, 165–167,
193, 195, 231, 232
Modified ABC, 64, 114, 150
Modified HEA, 64, 115, 116, 118, 232
Modified PSO, 32, 64, 112, 114, 119,
150, 232
Multi-objective optimization, 2, 44, 58, 59,
193, 197, 200, 203, 210, 228, 229
Multiple disc clutch brake, 46, 47, 60
O
Objective function, 1, 5, 8–13, 16, 18, 19, 21,
23, 29, 35, 36, 39–42, 44, 56–64, 66,
71, 91, 93, 101–103, 137–140, 150,
153, 165–167, 195, 197, 200, 201, 203,
208, 210–212, 218, 226
P
Particle swarm optimization, 2, 5, 14, 15, 17,
24, 25, 28, 32, 33, 86, 87, 89, 90, 119,
120, 143, 155, 192, 229
Penalty1 function, 278
Penalty2 function, 282
Planetary gear train, 109
Pressure drop, 206–209, 211, 212, 214, 217,
219, 226
Pressure vessel, 86, 87, 106, 107, 113, 117,
128, 129, 153, 156, 159, 160, 206
Q
Quartic function, 112, 127, 158
R
Radial ball bearing, 35, 39, 40, 57, 59, 231
Rastrigin function, 136, 144
Real parameter optimization, 162, 164, 166,
168, 170, 172, 174, 176, 178, 180, 182,
184, 186, 188, 190
Robot gripper, 2, 35, 47, 48, 61, 62, 67, 106,
107, 113, 117, 128, 129, 159, 160, 231
Rosenbrock function, 143, 145, 147
S
Schwefel 1.2 function, 243
Schwefel 2.21 function, 247
Schwefel 2.22 function, 239
Schwefel 2.26 function, 263
Screw jack, 97, 98, 106, 107, 113, 117, 128,
129, 159, 160
Shell and tube heat exchanger, 204, 206,
209–212, 227–229
Shuffled frog leaping algorithm, 28, 29, 32,
204, 218
Speed reducer, 90, 106, 107, 113, 117, 128,
129, 153, 159, 160
Step-cone pulley, 97
Step function, 112, 127
Stiffened cylindrical shell, 91, 92, 120
T
Thermoelectric cooler, 195, 197, 199, 201,
203, 228, 233
U
Unconstrained benchmark functions, 3, 68–70,
104, 108, 112, 114, 115, 118, 122–125,
127, 130, 148, 149, 158, 231
W
Welded beam, 87, 88, 106, 107, 113, 117, 128,
129, 153, 156, 159, 160


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