EBOOK - New Optimization Techniques in Engineering (Godfrey C. Onwubolu & B.V. Babu)

Presently, general-purpose optimization techniques such as Simulated Annealing, and Genetic Algorithms, have become standard optimization techniques. These optimization techniques commence with a single solution and then find the best from several moves made, and generally, past history is not carried forward into the present.
Many researchers agree that firstly, having a population of initial solutions increases the possibility of converging to an optimum solution, and secondly, updating the current information of the search strategy from previous history is a natural tendency. Accordingly, attempts have been made by researchers to restructure these standard optimization techniques in order to achieve the two goals mentioned.
To achieve these two goals, researchers have made concerted efforts in the last one-decade in order to invent novel optimization techniques for solving real life problems, which have the attributes of memory update and population-based search solutions. This book describes these novel optimization techniques, which in most cases outperform their counterpart standard optimization techniques in
many application areas. Despite these already promising results, these novel optimization techniques are still in their infancy and can most probably be improved.
To date, researchers are still carrying out studies on sound theoretical basis and analysis to determine why some of these novel optimization techniques converge so well compared to their counterpart standard optimization techniques.

Interestingly, most books that have reported the applications and results of these novel optimization techniques have done so without sufficiently considering practical problems in the different engineering disciplines. This book,New Optimization Techniques in Engineeringhas three main objectives: (i) to discuss in the clearest way possible, these novel optimization techniques, (ii) to apply these novel optimization techniques in the conventional engineering disciplines, and (iii)
to suggest and incorporate the improvements in these novel optimization techniques that are feasible as and when it is possible in the application areas chosen.
To achieve the first objective, Part I containing seven chapters have been written by the inventors of these novel optimization techniques or experts who have done considerable work in the areas (Genetic Algorithm, Memetic Algorithm, Scatter Search, Ant Colony Optimization, Differential Evolution, Self-Organizing Migrating Algorithm, Particle Swarm Optimization). Genetic Algorithm has been included for completeness since it is the progenitor of Memetic Algorithm.
The contributors for Genetic Algorithm and Particle Swarm Optimization have been chosen, not as the inventors, but due to their expertise and contributions in these areas of optimization. To achieve the second objective, Part II contains several chapters in which researchers have applied these novel optimization techniques to different Engineering disciplines such as Chemical/Metallurgical Engineering, Civil/Environmental Engineering/Interdisciplinary, Electrical/Electronics Engineering, Manufacturing/Industrial Engineering, and Mechanical/Aeronautical Engineering. Firstly, the Engineering background is sufficiently given concerning the problem-domain, and then a novel optimization technique is applied.
Consequently, Part II makes it easy for engineers and scientists to understand the link between theory and application of a particular novel optimization technique. To achieve the third objective, the possible improvements in these novel optimization techniques are identified, suggested and applied to some of the engineering problems successfully. Part III discusses newer areas, which are considered as extended frontiers.
The text serves as an instructional material for upper division undergraduates, entry-level graduate student, and a resource material for practicing engineers, research scientists, operations researchers, computer scientists, applied mathematicians, and management scientists. Those to purchase the book include upper division undergraduates or entry-level graduate students, academics, professionals and researchers of disciplines listed above, and libraries.

Chapter 1: Introduction 1
Godfrey C Onwubolu and B V Babu
1.1 Optimization 1
1.2 Stochastic Optimization Technique 4
1.2.1 Local Search 4
1.2.2 Population-based Search 5
1.3  Framework for Well-Established Optimization Techniques   6
1.4  New & Novel Optimization Techniques     7
1.5  The Structure of the Book      9
1.6 Summary 10
References 11
Part I: New Optimization Techniques
Chapter 2: Introduction to Genetic Algorithms for Engineering Optimization
Kalyanmoy Deb       13
2.1 Introduction 13
2.2  Classical Search and Optimization Techniques    14
2.3 Motivation from Nature 16
2.4 Genetic Algorithms 17
2.4.1 Working Principle 17
2.4.2 An Illustration 22
2.4.3 A Hand Calculation 27
2.4.4 Constraint Handling 31
2.5  Car Suspension Design Using Genetic Algorithms    34
2.5.1 Two-dimensional model 34
2.5.2 Three-dimensional model 37
2.6 Real-Parameter Genetic Algorithms 40
2.7  A Combined Genetic Algorithm      43
2.7.1 Gear Train Design 44
2.8 A Spring Design 45
2.9 Advanced Genetic Algorithms 47
2.10 Conclusions 48
References 49
Chapter 3: Memetic Algorithms 53
Pablo Moscato, Carlos Cotta and Alexandre Mendes
3.1 Introduction 53
3.2 The MA Search Template 54
3.3 Design of Effective MAs 60
3.4 Applications of MAs 65
3.4.1  NP-hard Combinatorial Optimization problems    66
3.4.2 Telecomunications and networking 66
3.4.3  Scheduling and Timetabling Problems     67
3.4.4 Machine Learning and Robotics 67
3.4.5  Engineering, Electronics and Electromagnetics    68
3.4.6  Problems involving optimization in molecules    68
3.4.7 Other Applications 69
3.5 Conclusions and Future Directions 69
References 72
Chapter 4: Scatter Search and Path Relinking: Foundations and
AdvancedDesigns   87 Fred Glover, Manuel Laguna and Rafael Martí    
4.1 Introduction 87
4.2 Foundations 89
4.2.1 Scatter Search 89
4.2.2 Path Relinking 91
4.3 Advanced Strategies 93
4.3.1 Scatter Search 93
4.3.2 Path Relinking 96
References 99
Chapter 5: Ant Colony Optimization     101
Vittorio Maniezzo, Luca Maria Gambardella, Fabio de Luigi
5.1  Introduction       101
5.2  Ant Colony Optimization      102
5.2.1  Ant System       103
5.2.2  Ant Colony System      105
5.2.3  ANTS        107
5.3  Significant problems      109
5.3.1  Sequential ordering problem     110
5.3.2  Vehicle routing problems      111
5.3.3  Quadratic Assignment Problem     113
5.3.4  Other problems       114
5.4  Convergence proofs      115
5.5  Conclusions       116
References       117
Chapter 6: Differential Evolution     123
Jouni Lampinen and Rainer Storn
6.1  Introduction       123
6.2  Mixed integer-discrete-continuous non-linear programming  124
6.3  Differential Evolution      125
6.3.1  Initialization       127
6.3.2  Mutation and Crossover      128
6.3.3  Selection       130
6.3.4  DE dynamics       132
6.4  Constraint handling      138
6.4.1  Boundary constraints      138
6.4.2  Constraint functions      139
6.4  Handling integer and discrete variables    142
6.5.1  Methods       142
6.5.2  An Illustrative Example      143
6.6  Numerical examples      144
6.6.1  Example 1: Designing a gear train     146
6.6.2  Example 2: Designing a pressure vessel    149
6.6.3  Example 3: Designing a coil compression spring   153
6.7  DE’s Sensitivity to Its Control Variables    157
6.8  Conclusions       160
References       163
Chapter 7: SOMA - Self-Organizing Migrating Algorithm  167
Ivan Zelinka
7.1  Introduction       167
7.2  Function domain of SOMA     168
7.3  Population       169
7.4  Mutation       170
7.5  Crossover       171
7.6  Parameters and Terminology     172
7.7  Principles of SOMA      175
7.8  Variations of SOMA      179
7.9  SOMA dependence on control parameters    180
7.10  On the SOMA classification and some additional information 182
7.11  Constraint Handling      184
7.11.1  Boundary constraints      185
7.11.2  Constraint functions      186
7.11.3  Handling of Integer and Discrete Variables    187
7.12  Selected Applications and Open Projects    189
7.13  Gallery of test functions      192
7.14  SOMA on tested functions     200
7.15  Conclusion       212
References       215
Chapter 8: Discrete Particle Swarm Optimization, illustrated by the
Traveling Salesman Problem 219
Maurice Clerc
8.1  Introduction       219
8.2  A few words about “classical” PSO    219
8.3  Discrete PSO       221
8.4  PSO elements for TSP      222
8.4.1  Positions and state space      222
8.4.2  Objective function      222
8.4.3  Velocity       223
8.4.4  Opposite of a velocity      223
8.4.5  Move (addition) “position plusvelocity”    223
8.4.6 Subtraction “position minusposition”    224
8.4.7 Addition “velocity plusvelocity”     224
8.4.8 Multiplication “coefficient timesvelocity”    224
8.4.9  Distance between two positions     225
8.5  The algorithm “PSO for TSP”. Core and variations   225
8.5.1  Equations       225
8.5.2  NoHope tests       226
8.5.3  ReHope process       227
8.5.4  Adaptive ReHope Method (ARM)     228
8.5.5  Queens        228
8.5.6  Extra-best particle      228
8.5.7  Parallel and sequential versions     229
8.6  Examples and results      229
8.6.1  Parameters choice      229
8.6.2  A toy example as illustration     230
8.6.3  Some results, and discussion     235
Appendix      236
References       238
Part II: Applications of New Optimization Techniques in Engineering
Part II.1: Chemical/Metallurgical Engineering
Chapter 9: Applications in Heat Transfer    241
B V Babu
9.1  Introduction       241
9.2  Heat Transfer Parameters in Trickle Bed Reactor   244
9.2.1  Orthogonal collocation      247
9.2.2  Experimental setup and procedure     249
9.2.3  Results and discussions      251
9.2.4  Conclusions       258
Contents XV
9.3 Design of Shell-and-Tube Heat Exchanger 259
9.3.1 The Optimal HED problem 259
9.3.2 Problem Formulation 262
9.3.3 Results & Discussions 263
9.3.4 Conclusions 276
Nomenclature 277
References 281
Chapter 10: Applications in Mass Transfer 287
BVBabu
10.1 Introduction 287
10.2 Optimization of Liquid Extraction Process 287
10.2.1 Process Model 290
10.2.2 Objective function 291
10.2.3 Inequality constraints 291
10.2.4 Results & Discussion 292
10.2.5 Conclusions 294
10.3 Optimization of a Separation Train of Distillation Columns 295
10.3.1 Problem formulation 295
10.3.2 Results & Discussion 298
10.3.3 Conclusions 300
10.4 Optimization and Synthesis of Heat Integrated Distillation Column
Sequences 300
10.4.1 Problem formulation 301
10.4.2 Synthesis of Distillation system 303
10.4.3 Results & Discussion 305
10.4.4 Conclusions 308
References 309
Chapter 11: Applications in Fluid Mechanics 313
BVBabu
11.1 Introduction 313
11.2 Gas Transmission Network 314
11.2.1 Problem Formulation 315
11.2.2 Results & Discussion 320
11.3 Water Pumping System 327
11.3.1 Differential Evolution Strategies 327
11.3.2 Problem Formulation 331
11.3.3 Results & Discussion 332
11.4 Conclusions 334
References 336
Chapter 12: Applications in Reaction Engineering 341
BVBabu
12.1 Introduction 341
XVI Contents
12.2 Design of Auto-Thermal Ammonia Synthesis Reactor 343
12.2.1 Ammonia Synthesis Reactor 343
12.2.2 Problem Formulation 345
12.2.3 Simulated Results & Discussion 345
12.2.4 Optimization 352
12.2.5 Conclusions 356
12.3 Thermal Cracking Operation 356
12.3.1 Thermal Cracking 357
12.3.2 Problem Description 357
12.3.3 Problem Reformulation 360
12.3.4 Simulated Results and Discussion 361
12.3.5 Conclusions 362
References 363
Part II.2: Civil/Environmental Engineering/ Interdisciplinary
Chapter 13: New Ideas and Applications of Scatter Search and Path
Relinking 367
Fred Glover, Manuel Laguna and Rafael Martí
13.1 Introduction 367
13.2 Scatter Search Applications 368
13.2.1 Neural Network Training 368
13.2.2 Multi-Objective Routing Problem 369
13.2.3 OptQuest: A Commercial Implementation 371
13.2.4 A Context-Independent Method for Permutation Problems 373
13.2.5 Classical Vehicle Routing 375
13.3 Path Relinking Applications 378
13.3.1 Matrix Bandwidth Minimization 378
13.3.2 Arc Crossing Minimization 379
References 382
Chapter 14: Improvement of Search Process in Genetic Algorithms:
An Application of PCB Assembly Sequencing Problem   385
Nguyen Van Hop and Mario T Tabucanon
14.1 Introduction 385
14.2 Guided Genetic Algorithm (GGA) 388
14.2.1 Coding scheme 389
14.2.2 Fitness function 390
14.2.3 Genetic Operators 390
14.2.4 Input parameters 394
14.2.5 Guided Genetic Algorithm (GGA) 395
14.3 The GGA for the PCB Assembly Sequencing Problem 396
14.3.1 The PCB Sequencing Problem on Multiple Non-identical Parallel
Machines 396
14.3.2 Related works 399
14.3.3 The GGA Solution 401
14.3.4 Experimental Results 403
14.4 Concluding Remarks 407
References 408
Chapter 15: An ANTS Heuristic for the Long-Term Car Pooling
Problem:ACO 411
Vittorio Maniezzo, Antonella Carbonaro, Hanno Hildmann
15.1 Introduction 411
15.2 Problem Definition and Formulation 413
15.2.1 The objective function 414
15.2.2 A four-indices mathematical formulation 416
15.2.3 A set partitioning formulation 418
15.2.4 Reduction rules 418
15.3 The ANTS metaheuristic 420
15.3.1 Attractiveness 421
15.3.2 Trail update 421
15.4 ANTS approaches for the LCPP 422
15.4.1 Attractiveness quantification 422
15.4.2 Local optimization 423
15.5 A DSS for the LCPP 424
15.6 Computational results 426
15.7 Conclusions 429
References 430
Chapter 16: Genetic Algorithms in Irrigation Planning: A Case Study
of Sri Ram Sagar Project, India 431
K Srinivasa Raju and D Nagesh Kumar
16.1 Introduction 431
16.1.1 Working Principle of Genetic Algorithms 432
16.1.2 Necessity of Mathematical Modeling in Irrigation Planning 433
16.2 Literature Review 433
16.3 Irrigation System and Mathematical Modeling 434
16.3.1 Continuity equation 436
16.3.2 Crop area restrictions 436
16.3.3 Crop water diversions 436
16.3.4 Canal capacity restrictions 437
16.3.5 Live storage restrictions 437
16.3.6 Crop diversification considerations 437
16.4 Results and Discussion 437
16.5 Conclusions 441
References 443
Chapter 17: Optimization of Helical Antenna Electromagnetic Pattern Field
Ivan Zelinka        445
17.1  Introduction       445
17.2  Problem description      445
17.3  Simulations       448
17.4  Software support      451
17.5  Conclusion       452
References       453
Chapter 18: VLSI design: Gate Matrix Layout Problem   455
Pablo Moscato, Alexandre Mendes and Alexandre Linhares
18.1  Introduction       455
18.2  The gate matrix layout problem     456
18.3  The memetic algorithm      458
18.3.1  Population structure      458
18.3.2  Representation and crossover     459
18.3.3  Mutation       461
18.3.4  Local search       462
18.3.5  Selection for recombination     466
18.3.6  Offspring insertion      467
18.3.7  Pseudo-code of the MA      468
18.3.8  Migration policies      469
18.4  Computational experiments     471
18.5  Discussion       475
References       477
Chapter 19: Parametric Optimization of a Fuzzy Logic Controller
for Nonlinear Dynamical Systems using Evolutionary Computation 479
Laxmidhar Behera
19.1  Introduction       480
19.2  Differential Evolution      482
19.3  Simple Genetic Algorithm with Search Space Smoothing  483
19.4  Simple Genetic Algorithm Vs Differential Evolution  485
19.5  pH Neutralization Process     486
19.6  Simulation       488
19.7  Experiments & Results      490
19.8  The Univariate Marginal Distribution Algorithm   493
19.9  Robot arm control      493
19.9.1  Control Architecture      493
19.9.2  Inverse Dynamics Model      494
19.9.3  Feedback fuzzy PD Controller     497
19.10  Conclusions       499
References       500
Part II.3: Electrical/Electronics Engineering
Chapter 20: DNA Coded GA: Rule Base Optimization of FLC
for Mobile Robot 503
Prahlad Vadakkepat, Xiao Peng and Lee Tong Heng
20.1 Introduction 503
20.2 DNA Computing 504
20.3 The Khepera Robot and Webots Software 506
20.3.1 The Khepera Robot 506
20.3.2 The Webots Software 507
20.4 The Fuzzy logic controller 508
20.5 DNA coded Genetic Algorithm for FLC 510
20.6 Simulation Results 512
20.7 Discussion 514
References 515
Part II.4: Manufacturing/Industrial Engineering
Chapter 21: TRIBES application to the flow shop scheduling problem 517
Godfrey C Onwubolu
21.1 Introduction 517
21.2 Flow-shop scheduling problem (FSP) 518
21.3 TRIBES approach 519
21.3.1 Terminology and concepts 519
21.3.2 Informers 520
21.3.3 Hyper-spheres, and promising areas 520
21.3.4 Adaptations 525
21.3.5 Adaptive scheme 527
21.3.6 Transformer 527
21.3.7 Local search 528
21.3.8 The transformer-local search scheme 528
21.3.9 Illustrating Tribes 529
21.4 The TRIBES Algorithm 530
21.5 Experimental results 533
21.5.1 Parameter setting 533
21.5.2 Comparison with other heuristics 534
21.6 Conclusion 534
References 536
Chapter 22: Optimizing CNC Drilling Machine Operations:
TravelingSalesman Problem-Differential Evolution Approach 537
Godfrey C Onwubolu
22.1 Introduction 537
22.2 Travelling Salesman Problem (TSP) 539
22.3 TSP using Closest Insertion Algorithm 540
22.4 TSP using Differential Evolution 544
22.4.1 Differential Evolution Method 544
22.4.2 Differential Evolution Method for TSP 551
22.4.3 Parameter Setting 554
22.4.4 An Example 554
22.4.5 Experimentation 555
22.5 TSP/Differential Evolution Application in CNC Drilling of PCB 556
22.5.1 PCB Manufacturing 557
22.5.2 Automated Drilling Location and Hit Sequencing using DE 560
22.6 Summary 562
References 564
Chapter 23: Particle swarm optimization for the assignment of facilities
to locations 567
Godfrey C Onwubolu and Anuraganand Sharma
23.1 Introduction 567
23.2 Problem Formulation 568
23.3 Application of the PSO to the QAP 569
23.3.1 Explosion Control 572
23.3.2 Particle Swarm Optimization Operators 573
23.3.3 Particle Swarm Optimization Neighborhood 576
23.3.4 Particle Swarm Optimization Improvement Strategies 577
23.4 Experimentation 580
23.4.1 Parameter settings 580
23.4.2 Computational results 580
23.5 Conclusion 581
References 582
Chapter 24: Differential Evolution for the Flow Shop Scheduling Problem585
Godfrey C Onwubolu
24.1 Introduction 585
24.2 Problem Formulation for the flow shop schedules 587
24.3 Differential Evolution 589
24.3.1 Constraint Handling 592
24.3.2 Integer and Discrete Optimization by Differential Evolution
Algorithm  594
24.4 Illustrative Example 602
24.4.1 Mutation Scheme 603
24.4.2 Selection 606
24.5 Experimentation 606
24.5.1 Parameter Setting 607
24.6 Summary 609
References 610
Chapter 25: Evaluation of Form Errors to Large Measurement Data
Sets Using Scatter Search 613
Mu-Chen Chen and Kai-Ying Chen
25.1  Introduction       613
25.2  Mathematical Models for Roundness    615
25.2.1  Roundness       615
25.2.2  The maximum inscribed circle     616
25.2.3  The minimum circumscribed circle    617
25.2.4  The minimum zone circle     617
25.3  Mathematical Models for Sphericity    618
25.3.1  Sphericity       618
25.3.2  Maximum inscribed sphere     618
25.3.3  Minimum circumscribed sphere     619
25.3.4  Minimum zone sphere      620
25.4  Scatter Search       620
25.4.1  Overview of scatter search     620
25.4.2  Scatter search template      622
25.4.3  The scatter search procedure     624
25.5  Computational Experience     625
25.5.1  Roundness measurement      625
25.5.2  Sphericity measurement      626
25.6  Summary       627
 References       630
Chapter 26: Mechanical engineering problem optimization by SOMA 633
Ivan Zelinka and Jouni Lampinen
26.1  Mechanical engineering problem optimization by SOMA  633
26.1.1  Designing a gear train      634
26.1.2  Designing a pressure vessel     638
26.1.3  Designing a coil compression spring    644
26.2  Conclusion       650
 References       652
Chapter 27: Scheduling and Production & Control: MA   655
Pablo Moscato, Alexandre Mendes and Carlos Cotta
27.1  Introduction       655
27.2  The single machine scheduling problem    656
27.2.1  The test instances      658
27.2.2  The memetic algorithm approach     660
27.2.3  The SMS computational results     662
27.3  The parallel machine scheduling problem    665
27.3.1  The test instances      667
27.3.2  The memetic algorithm approach     667
27.3.3  The PMS computational results     668
27.4  The flowshop scheduling problem     670
Contents XXI
Part II.5: Mechanical/Aeronautical Engineering
XXII Contents
27.4.1 The test instances 672
27.4.2 The memetic algorithm approach 673
27.4.3 The flowshop computational results 674
27.5 Discussion 677
References 679
Chapter 28: Determination of Optimal Machining Conditions Using
ScatterSearch 681
Mu-Chen Chen and Kai-Ying Chen
28.1 Introduction 681
28.2 Fundamentals of CNC Turning 682
28.2.1 CNC turning machine axes 683
28.2.2 CNC turning operations 683
28.2.3 CNC turning conditions 683
28.3 Literature Review 685
28.3.1 Machining optimization for turning operations 685
28.3.2 Review of machining optimization techniques 686
28.4 Notations in Machining Model 689
28.5 The Multi-Pass Turning Model 691
28.5.1 The cost function 691
28.5.2 Turning condition constraints 694
28.6 Computational Experience 696
28.7 Conclusions 698
References 700
Part III: Extended Frontiers
Chapter 29: Extended Frontiers in optimization techniques 703
Sergiy Butenko and Panos M Pardalos
29.1 Recent Progress in Optimization Techniques 703
29.2 Heuristic Approaches 706
29.2.1 Parallel Metaheuristics 707
29.3 Emerging Application Areas of Optimization 708
29.4 Concluding Remarks 709
References 710

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Presently, general-purpose optimization techniques such as Simulated Annealing, and Genetic Algorithms, have become standard optimization techniques. These optimization techniques commence with a single solution and then find the best from several moves made, and generally, past history is not carried forward into the present.
Many researchers agree that firstly, having a population of initial solutions increases the possibility of converging to an optimum solution, and secondly, updating the current information of the search strategy from previous history is a natural tendency. Accordingly, attempts have been made by researchers to restructure these standard optimization techniques in order to achieve the two goals mentioned.
To achieve these two goals, researchers have made concerted efforts in the last one-decade in order to invent novel optimization techniques for solving real life problems, which have the attributes of memory update and population-based search solutions. This book describes these novel optimization techniques, which in most cases outperform their counterpart standard optimization techniques in
many application areas. Despite these already promising results, these novel optimization techniques are still in their infancy and can most probably be improved.
To date, researchers are still carrying out studies on sound theoretical basis and analysis to determine why some of these novel optimization techniques converge so well compared to their counterpart standard optimization techniques.

Interestingly, most books that have reported the applications and results of these novel optimization techniques have done so without sufficiently considering practical problems in the different engineering disciplines. This book,New Optimization Techniques in Engineeringhas three main objectives: (i) to discuss in the clearest way possible, these novel optimization techniques, (ii) to apply these novel optimization techniques in the conventional engineering disciplines, and (iii)
to suggest and incorporate the improvements in these novel optimization techniques that are feasible as and when it is possible in the application areas chosen.
To achieve the first objective, Part I containing seven chapters have been written by the inventors of these novel optimization techniques or experts who have done considerable work in the areas (Genetic Algorithm, Memetic Algorithm, Scatter Search, Ant Colony Optimization, Differential Evolution, Self-Organizing Migrating Algorithm, Particle Swarm Optimization). Genetic Algorithm has been included for completeness since it is the progenitor of Memetic Algorithm.
The contributors for Genetic Algorithm and Particle Swarm Optimization have been chosen, not as the inventors, but due to their expertise and contributions in these areas of optimization. To achieve the second objective, Part II contains several chapters in which researchers have applied these novel optimization techniques to different Engineering disciplines such as Chemical/Metallurgical Engineering, Civil/Environmental Engineering/Interdisciplinary, Electrical/Electronics Engineering, Manufacturing/Industrial Engineering, and Mechanical/Aeronautical Engineering. Firstly, the Engineering background is sufficiently given concerning the problem-domain, and then a novel optimization technique is applied.
Consequently, Part II makes it easy for engineers and scientists to understand the link between theory and application of a particular novel optimization technique. To achieve the third objective, the possible improvements in these novel optimization techniques are identified, suggested and applied to some of the engineering problems successfully. Part III discusses newer areas, which are considered as extended frontiers.
The text serves as an instructional material for upper division undergraduates, entry-level graduate student, and a resource material for practicing engineers, research scientists, operations researchers, computer scientists, applied mathematicians, and management scientists. Those to purchase the book include upper division undergraduates or entry-level graduate students, academics, professionals and researchers of disciplines listed above, and libraries.

Chapter 1: Introduction 1
Godfrey C Onwubolu and B V Babu
1.1 Optimization 1
1.2 Stochastic Optimization Technique 4
1.2.1 Local Search 4
1.2.2 Population-based Search 5
1.3  Framework for Well-Established Optimization Techniques   6
1.4  New & Novel Optimization Techniques     7
1.5  The Structure of the Book      9
1.6 Summary 10
References 11
Part I: New Optimization Techniques
Chapter 2: Introduction to Genetic Algorithms for Engineering Optimization
Kalyanmoy Deb       13
2.1 Introduction 13
2.2  Classical Search and Optimization Techniques    14
2.3 Motivation from Nature 16
2.4 Genetic Algorithms 17
2.4.1 Working Principle 17
2.4.2 An Illustration 22
2.4.3 A Hand Calculation 27
2.4.4 Constraint Handling 31
2.5  Car Suspension Design Using Genetic Algorithms    34
2.5.1 Two-dimensional model 34
2.5.2 Three-dimensional model 37
2.6 Real-Parameter Genetic Algorithms 40
2.7  A Combined Genetic Algorithm      43
2.7.1 Gear Train Design 44
2.8 A Spring Design 45
2.9 Advanced Genetic Algorithms 47
2.10 Conclusions 48
References 49
Chapter 3: Memetic Algorithms 53
Pablo Moscato, Carlos Cotta and Alexandre Mendes
3.1 Introduction 53
3.2 The MA Search Template 54
3.3 Design of Effective MAs 60
3.4 Applications of MAs 65
3.4.1  NP-hard Combinatorial Optimization problems    66
3.4.2 Telecomunications and networking 66
3.4.3  Scheduling and Timetabling Problems     67
3.4.4 Machine Learning and Robotics 67
3.4.5  Engineering, Electronics and Electromagnetics    68
3.4.6  Problems involving optimization in molecules    68
3.4.7 Other Applications 69
3.5 Conclusions and Future Directions 69
References 72
Chapter 4: Scatter Search and Path Relinking: Foundations and
AdvancedDesigns   87 Fred Glover, Manuel Laguna and Rafael Martí    
4.1 Introduction 87
4.2 Foundations 89
4.2.1 Scatter Search 89
4.2.2 Path Relinking 91
4.3 Advanced Strategies 93
4.3.1 Scatter Search 93
4.3.2 Path Relinking 96
References 99
Chapter 5: Ant Colony Optimization     101
Vittorio Maniezzo, Luca Maria Gambardella, Fabio de Luigi
5.1  Introduction       101
5.2  Ant Colony Optimization      102
5.2.1  Ant System       103
5.2.2  Ant Colony System      105
5.2.3  ANTS        107
5.3  Significant problems      109
5.3.1  Sequential ordering problem     110
5.3.2  Vehicle routing problems      111
5.3.3  Quadratic Assignment Problem     113
5.3.4  Other problems       114
5.4  Convergence proofs      115
5.5  Conclusions       116
References       117
Chapter 6: Differential Evolution     123
Jouni Lampinen and Rainer Storn
6.1  Introduction       123
6.2  Mixed integer-discrete-continuous non-linear programming  124
6.3  Differential Evolution      125
6.3.1  Initialization       127
6.3.2  Mutation and Crossover      128
6.3.3  Selection       130
6.3.4  DE dynamics       132
6.4  Constraint handling      138
6.4.1  Boundary constraints      138
6.4.2  Constraint functions      139
6.4  Handling integer and discrete variables    142
6.5.1  Methods       142
6.5.2  An Illustrative Example      143
6.6  Numerical examples      144
6.6.1  Example 1: Designing a gear train     146
6.6.2  Example 2: Designing a pressure vessel    149
6.6.3  Example 3: Designing a coil compression spring   153
6.7  DE’s Sensitivity to Its Control Variables    157
6.8  Conclusions       160
References       163
Chapter 7: SOMA - Self-Organizing Migrating Algorithm  167
Ivan Zelinka
7.1  Introduction       167
7.2  Function domain of SOMA     168
7.3  Population       169
7.4  Mutation       170
7.5  Crossover       171
7.6  Parameters and Terminology     172
7.7  Principles of SOMA      175
7.8  Variations of SOMA      179
7.9  SOMA dependence on control parameters    180
7.10  On the SOMA classification and some additional information 182
7.11  Constraint Handling      184
7.11.1  Boundary constraints      185
7.11.2  Constraint functions      186
7.11.3  Handling of Integer and Discrete Variables    187
7.12  Selected Applications and Open Projects    189
7.13  Gallery of test functions      192
7.14  SOMA on tested functions     200
7.15  Conclusion       212
References       215
Chapter 8: Discrete Particle Swarm Optimization, illustrated by the
Traveling Salesman Problem 219
Maurice Clerc
8.1  Introduction       219
8.2  A few words about “classical” PSO    219
8.3  Discrete PSO       221
8.4  PSO elements for TSP      222
8.4.1  Positions and state space      222
8.4.2  Objective function      222
8.4.3  Velocity       223
8.4.4  Opposite of a velocity      223
8.4.5  Move (addition) “position plusvelocity”    223
8.4.6 Subtraction “position minusposition”    224
8.4.7 Addition “velocity plusvelocity”     224
8.4.8 Multiplication “coefficient timesvelocity”    224
8.4.9  Distance between two positions     225
8.5  The algorithm “PSO for TSP”. Core and variations   225
8.5.1  Equations       225
8.5.2  NoHope tests       226
8.5.3  ReHope process       227
8.5.4  Adaptive ReHope Method (ARM)     228
8.5.5  Queens        228
8.5.6  Extra-best particle      228
8.5.7  Parallel and sequential versions     229
8.6  Examples and results      229
8.6.1  Parameters choice      229
8.6.2  A toy example as illustration     230
8.6.3  Some results, and discussion     235
Appendix      236
References       238
Part II: Applications of New Optimization Techniques in Engineering
Part II.1: Chemical/Metallurgical Engineering
Chapter 9: Applications in Heat Transfer    241
B V Babu
9.1  Introduction       241
9.2  Heat Transfer Parameters in Trickle Bed Reactor   244
9.2.1  Orthogonal collocation      247
9.2.2  Experimental setup and procedure     249
9.2.3  Results and discussions      251
9.2.4  Conclusions       258
Contents XV
9.3 Design of Shell-and-Tube Heat Exchanger 259
9.3.1 The Optimal HED problem 259
9.3.2 Problem Formulation 262
9.3.3 Results & Discussions 263
9.3.4 Conclusions 276
Nomenclature 277
References 281
Chapter 10: Applications in Mass Transfer 287
BVBabu
10.1 Introduction 287
10.2 Optimization of Liquid Extraction Process 287
10.2.1 Process Model 290
10.2.2 Objective function 291
10.2.3 Inequality constraints 291
10.2.4 Results & Discussion 292
10.2.5 Conclusions 294
10.3 Optimization of a Separation Train of Distillation Columns 295
10.3.1 Problem formulation 295
10.3.2 Results & Discussion 298
10.3.3 Conclusions 300
10.4 Optimization and Synthesis of Heat Integrated Distillation Column
Sequences 300
10.4.1 Problem formulation 301
10.4.2 Synthesis of Distillation system 303
10.4.3 Results & Discussion 305
10.4.4 Conclusions 308
References 309
Chapter 11: Applications in Fluid Mechanics 313
BVBabu
11.1 Introduction 313
11.2 Gas Transmission Network 314
11.2.1 Problem Formulation 315
11.2.2 Results & Discussion 320
11.3 Water Pumping System 327
11.3.1 Differential Evolution Strategies 327
11.3.2 Problem Formulation 331
11.3.3 Results & Discussion 332
11.4 Conclusions 334
References 336
Chapter 12: Applications in Reaction Engineering 341
BVBabu
12.1 Introduction 341
XVI Contents
12.2 Design of Auto-Thermal Ammonia Synthesis Reactor 343
12.2.1 Ammonia Synthesis Reactor 343
12.2.2 Problem Formulation 345
12.2.3 Simulated Results & Discussion 345
12.2.4 Optimization 352
12.2.5 Conclusions 356
12.3 Thermal Cracking Operation 356
12.3.1 Thermal Cracking 357
12.3.2 Problem Description 357
12.3.3 Problem Reformulation 360
12.3.4 Simulated Results and Discussion 361
12.3.5 Conclusions 362
References 363
Part II.2: Civil/Environmental Engineering/ Interdisciplinary
Chapter 13: New Ideas and Applications of Scatter Search and Path
Relinking 367
Fred Glover, Manuel Laguna and Rafael Martí
13.1 Introduction 367
13.2 Scatter Search Applications 368
13.2.1 Neural Network Training 368
13.2.2 Multi-Objective Routing Problem 369
13.2.3 OptQuest: A Commercial Implementation 371
13.2.4 A Context-Independent Method for Permutation Problems 373
13.2.5 Classical Vehicle Routing 375
13.3 Path Relinking Applications 378
13.3.1 Matrix Bandwidth Minimization 378
13.3.2 Arc Crossing Minimization 379
References 382
Chapter 14: Improvement of Search Process in Genetic Algorithms:
An Application of PCB Assembly Sequencing Problem   385
Nguyen Van Hop and Mario T Tabucanon
14.1 Introduction 385
14.2 Guided Genetic Algorithm (GGA) 388
14.2.1 Coding scheme 389
14.2.2 Fitness function 390
14.2.3 Genetic Operators 390
14.2.4 Input parameters 394
14.2.5 Guided Genetic Algorithm (GGA) 395
14.3 The GGA for the PCB Assembly Sequencing Problem 396
14.3.1 The PCB Sequencing Problem on Multiple Non-identical Parallel
Machines 396
14.3.2 Related works 399
14.3.3 The GGA Solution 401
14.3.4 Experimental Results 403
14.4 Concluding Remarks 407
References 408
Chapter 15: An ANTS Heuristic for the Long-Term Car Pooling
Problem:ACO 411
Vittorio Maniezzo, Antonella Carbonaro, Hanno Hildmann
15.1 Introduction 411
15.2 Problem Definition and Formulation 413
15.2.1 The objective function 414
15.2.2 A four-indices mathematical formulation 416
15.2.3 A set partitioning formulation 418
15.2.4 Reduction rules 418
15.3 The ANTS metaheuristic 420
15.3.1 Attractiveness 421
15.3.2 Trail update 421
15.4 ANTS approaches for the LCPP 422
15.4.1 Attractiveness quantification 422
15.4.2 Local optimization 423
15.5 A DSS for the LCPP 424
15.6 Computational results 426
15.7 Conclusions 429
References 430
Chapter 16: Genetic Algorithms in Irrigation Planning: A Case Study
of Sri Ram Sagar Project, India 431
K Srinivasa Raju and D Nagesh Kumar
16.1 Introduction 431
16.1.1 Working Principle of Genetic Algorithms 432
16.1.2 Necessity of Mathematical Modeling in Irrigation Planning 433
16.2 Literature Review 433
16.3 Irrigation System and Mathematical Modeling 434
16.3.1 Continuity equation 436
16.3.2 Crop area restrictions 436
16.3.3 Crop water diversions 436
16.3.4 Canal capacity restrictions 437
16.3.5 Live storage restrictions 437
16.3.6 Crop diversification considerations 437
16.4 Results and Discussion 437
16.5 Conclusions 441
References 443
Chapter 17: Optimization of Helical Antenna Electromagnetic Pattern Field
Ivan Zelinka        445
17.1  Introduction       445
17.2  Problem description      445
17.3  Simulations       448
17.4  Software support      451
17.5  Conclusion       452
References       453
Chapter 18: VLSI design: Gate Matrix Layout Problem   455
Pablo Moscato, Alexandre Mendes and Alexandre Linhares
18.1  Introduction       455
18.2  The gate matrix layout problem     456
18.3  The memetic algorithm      458
18.3.1  Population structure      458
18.3.2  Representation and crossover     459
18.3.3  Mutation       461
18.3.4  Local search       462
18.3.5  Selection for recombination     466
18.3.6  Offspring insertion      467
18.3.7  Pseudo-code of the MA      468
18.3.8  Migration policies      469
18.4  Computational experiments     471
18.5  Discussion       475
References       477
Chapter 19: Parametric Optimization of a Fuzzy Logic Controller
for Nonlinear Dynamical Systems using Evolutionary Computation 479
Laxmidhar Behera
19.1  Introduction       480
19.2  Differential Evolution      482
19.3  Simple Genetic Algorithm with Search Space Smoothing  483
19.4  Simple Genetic Algorithm Vs Differential Evolution  485
19.5  pH Neutralization Process     486
19.6  Simulation       488
19.7  Experiments & Results      490
19.8  The Univariate Marginal Distribution Algorithm   493
19.9  Robot arm control      493
19.9.1  Control Architecture      493
19.9.2  Inverse Dynamics Model      494
19.9.3  Feedback fuzzy PD Controller     497
19.10  Conclusions       499
References       500
Part II.3: Electrical/Electronics Engineering
Chapter 20: DNA Coded GA: Rule Base Optimization of FLC
for Mobile Robot 503
Prahlad Vadakkepat, Xiao Peng and Lee Tong Heng
20.1 Introduction 503
20.2 DNA Computing 504
20.3 The Khepera Robot and Webots Software 506
20.3.1 The Khepera Robot 506
20.3.2 The Webots Software 507
20.4 The Fuzzy logic controller 508
20.5 DNA coded Genetic Algorithm for FLC 510
20.6 Simulation Results 512
20.7 Discussion 514
References 515
Part II.4: Manufacturing/Industrial Engineering
Chapter 21: TRIBES application to the flow shop scheduling problem 517
Godfrey C Onwubolu
21.1 Introduction 517
21.2 Flow-shop scheduling problem (FSP) 518
21.3 TRIBES approach 519
21.3.1 Terminology and concepts 519
21.3.2 Informers 520
21.3.3 Hyper-spheres, and promising areas 520
21.3.4 Adaptations 525
21.3.5 Adaptive scheme 527
21.3.6 Transformer 527
21.3.7 Local search 528
21.3.8 The transformer-local search scheme 528
21.3.9 Illustrating Tribes 529
21.4 The TRIBES Algorithm 530
21.5 Experimental results 533
21.5.1 Parameter setting 533
21.5.2 Comparison with other heuristics 534
21.6 Conclusion 534
References 536
Chapter 22: Optimizing CNC Drilling Machine Operations:
TravelingSalesman Problem-Differential Evolution Approach 537
Godfrey C Onwubolu
22.1 Introduction 537
22.2 Travelling Salesman Problem (TSP) 539
22.3 TSP using Closest Insertion Algorithm 540
22.4 TSP using Differential Evolution 544
22.4.1 Differential Evolution Method 544
22.4.2 Differential Evolution Method for TSP 551
22.4.3 Parameter Setting 554
22.4.4 An Example 554
22.4.5 Experimentation 555
22.5 TSP/Differential Evolution Application in CNC Drilling of PCB 556
22.5.1 PCB Manufacturing 557
22.5.2 Automated Drilling Location and Hit Sequencing using DE 560
22.6 Summary 562
References 564
Chapter 23: Particle swarm optimization for the assignment of facilities
to locations 567
Godfrey C Onwubolu and Anuraganand Sharma
23.1 Introduction 567
23.2 Problem Formulation 568
23.3 Application of the PSO to the QAP 569
23.3.1 Explosion Control 572
23.3.2 Particle Swarm Optimization Operators 573
23.3.3 Particle Swarm Optimization Neighborhood 576
23.3.4 Particle Swarm Optimization Improvement Strategies 577
23.4 Experimentation 580
23.4.1 Parameter settings 580
23.4.2 Computational results 580
23.5 Conclusion 581
References 582
Chapter 24: Differential Evolution for the Flow Shop Scheduling Problem585
Godfrey C Onwubolu
24.1 Introduction 585
24.2 Problem Formulation for the flow shop schedules 587
24.3 Differential Evolution 589
24.3.1 Constraint Handling 592
24.3.2 Integer and Discrete Optimization by Differential Evolution
Algorithm  594
24.4 Illustrative Example 602
24.4.1 Mutation Scheme 603
24.4.2 Selection 606
24.5 Experimentation 606
24.5.1 Parameter Setting 607
24.6 Summary 609
References 610
Chapter 25: Evaluation of Form Errors to Large Measurement Data
Sets Using Scatter Search 613
Mu-Chen Chen and Kai-Ying Chen
25.1  Introduction       613
25.2  Mathematical Models for Roundness    615
25.2.1  Roundness       615
25.2.2  The maximum inscribed circle     616
25.2.3  The minimum circumscribed circle    617
25.2.4  The minimum zone circle     617
25.3  Mathematical Models for Sphericity    618
25.3.1  Sphericity       618
25.3.2  Maximum inscribed sphere     618
25.3.3  Minimum circumscribed sphere     619
25.3.4  Minimum zone sphere      620
25.4  Scatter Search       620
25.4.1  Overview of scatter search     620
25.4.2  Scatter search template      622
25.4.3  The scatter search procedure     624
25.5  Computational Experience     625
25.5.1  Roundness measurement      625
25.5.2  Sphericity measurement      626
25.6  Summary       627
 References       630
Chapter 26: Mechanical engineering problem optimization by SOMA 633
Ivan Zelinka and Jouni Lampinen
26.1  Mechanical engineering problem optimization by SOMA  633
26.1.1  Designing a gear train      634
26.1.2  Designing a pressure vessel     638
26.1.3  Designing a coil compression spring    644
26.2  Conclusion       650
 References       652
Chapter 27: Scheduling and Production & Control: MA   655
Pablo Moscato, Alexandre Mendes and Carlos Cotta
27.1  Introduction       655
27.2  The single machine scheduling problem    656
27.2.1  The test instances      658
27.2.2  The memetic algorithm approach     660
27.2.3  The SMS computational results     662
27.3  The parallel machine scheduling problem    665
27.3.1  The test instances      667
27.3.2  The memetic algorithm approach     667
27.3.3  The PMS computational results     668
27.4  The flowshop scheduling problem     670
Contents XXI
Part II.5: Mechanical/Aeronautical Engineering
XXII Contents
27.4.1 The test instances 672
27.4.2 The memetic algorithm approach 673
27.4.3 The flowshop computational results 674
27.5 Discussion 677
References 679
Chapter 28: Determination of Optimal Machining Conditions Using
ScatterSearch 681
Mu-Chen Chen and Kai-Ying Chen
28.1 Introduction 681
28.2 Fundamentals of CNC Turning 682
28.2.1 CNC turning machine axes 683
28.2.2 CNC turning operations 683
28.2.3 CNC turning conditions 683
28.3 Literature Review 685
28.3.1 Machining optimization for turning operations 685
28.3.2 Review of machining optimization techniques 686
28.4 Notations in Machining Model 689
28.5 The Multi-Pass Turning Model 691
28.5.1 The cost function 691
28.5.2 Turning condition constraints 694
28.6 Computational Experience 696
28.7 Conclusions 698
References 700
Part III: Extended Frontiers
Chapter 29: Extended Frontiers in optimization techniques 703
Sergiy Butenko and Panos M Pardalos
29.1 Recent Progress in Optimization Techniques 703
29.2 Heuristic Approaches 706
29.2.1 Parallel Metaheuristics 707
29.3 Emerging Application Areas of Optimization 708
29.4 Concluding Remarks 709
References 710

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Chuyên mục: C. Bài giảng C. Bài giảng chuyên ngành Cơ khí - Chế tạo máy (Mechanical & Manufacturing Engineering) C. Bài giảng kỹ thuật