Preface xxi
Contributors xxvii
Part 1 Theory of Modern Heuristic Optimization 1
1 Introduction to Evolutionary Computation 3
David B. Fogel
1.1 Introduction 3
1.2 Advantages of Evolutionary Computation 4
1.3 Current Developments 12
1.4 Conclusions 19
2 Fundamentals of Genetic Algorithms 25
Alexandre P. Alves da Silva and Djalma M. Falcao
2.1 Introduction 25
2.2 Modern Heuristic Search Techniques 25
2.3 Introduction to GAs 27
2.4 Encoding 28
2.5 Fitness Function 30
2.6 Basic Operators 33
2.7 Niching Methods 38
2.8 Parallel Genetic Algorithms 39
2.9 Final Comments 40
3 Fundamentals of Evolution Strategies and Evolutionary Programming 43
Vladimiro Miranda
3.1 Introduction 43
3.2 Evolution Strategies 46
3.3 Evolutionary Programming 60
3.4 Common Features 63
3.5 Conclusions 68
4 Fundamentals of Particle Swarm Optimization Techniques 71
Yoshikazu Fukuyama
4.1 Introduction 71
4.2 Basic Particle Swarm Optimization 72
4.3 Variations of Particle Swarm Optimization 76
4.4 Research Areas and Applications 82
4.5 Conclusions 83
5 Fundamentals of Ant Colony Search Algorithms 89
Yong-Hua Song, Haiyan Lu, Kwang Y. Lee, and I. K. Yu
5.1 Introduction 89
5.2 Ant Colony Search Algorithm 90
5.3 Conclusions 99
6 Fundamentals of Tabu Search 101
Alcir J. Monticelli, Rub©n Romero, and Eduardo Nobuhiro Asada
6.1 Introduction 101
6.2 Functions and Strategies in Tabu Search 110
6.3 Applications of Tabu Search 119
6.4 Conclusions 120
7 Fundamentals of Simulated Annealing 123
Alcir J. Monticelli, Rub©n Romero, and Eduardo Nobuhiro Asada
7.1 Introduction 123
7.2 Basic Principles 125
7.3 Cooling Schedule 127
7.4 SA Algorithm for the Traveling Salesman Problem 131
7.5 SA for Transmission Network Expansion Problem 134
7.6 Parallel Simulated Annealing 140
7.7 Applications of Simulated Annealing 143
7.8 Conclusions 144
8 Fuzzy Systems 147
Germano Lambert-Torres
8.1 Motivation and Definitions 147
8.2 Integration of Fuzzy Systems with Evolutionary Techniques 150
8.3 An Illustrative Example of a Hybrid System 152
8.4 Conclusions 167
9 Differential Evolution, an Alternative Approach to Evolutionary Algorithm 171
Kit Po Wong and ZhaoYang Dong
9.1 Introduction 171
9.2 Evolutionary Algorithms 172
9.3 Differential Evolution 176
9.4 Key Operators for Differential Evolution 181
9.5 An Optimization Example 184
9.6 Conclusions 186
10 Pareto Multiobjective Optimization 189
Patrick N. Ngatchou, Anahita Zarei, Warren L. J. Fox, and Mohamed A. El-Sharkawi
10.1 Introduction 189
10.2 Basic Principles 190
10.3 Solution Approaches 194
10.4 Performance Analysis 202
10.5 Conclusions 205
11 Trust-Tech Paradigm for Computing High-Quality Optimal Solutions: Method and Theory 209
Hsiao-Dong Chiang and Jaewook Lee
11.1 Introduction 209
11.2 Problem Preliminaries 210
11.3 A Trust-Tech Paradigm 213
11.4 Theoretical Analysis of Trust-Tech Method 218
11.5 A Numerical Trust-Tech Method 221
11.6 Hybrid Trust-Tech Methods 225
11.7 Numerical Schemes 227
11.8 Numerical Studies 228
11.9 Conclusions Remarks 231
Part 2 Selected Applications of Modern Heuristic Optimization In Power Systems 235
12 Overview of Applications in Power Systems 237
Alexandre P. Alves da Silva, Djalma M. Falc£o, and Kwang Y. Lee
12.1 Introduction 237
12.2 Optimization 237
12.3 Power System Applications 238
12.4 Model Identification 239
12.5 Control 242
12.6 Distribution System Applications 244
12.7 Conclusions 249
13 Application of Evolutionary Technique to Power System Vulnerability Assessment 261
Mingoo Kim, Mohamed A. El-Sharkawi, Robert J. Marks, and Ioannis N. Kassabalidis
13.1 Introduction 261
13.2 Vulnerability Assessment and Control 263
13.3 Vulnerability Assessment Challenges 264
13.4 Conclusions 281
14 Applications to System Planning 285
Eduardo Nobuhiro Asada, Youngjae Jeon, Kwang Y. Lee, Vladimiro Miranda, Alcir J. Monticelli, Koichi Nara, Jong-Bae Park, Rub©n Romero, and Yong-Hua Song
14.1 Introduction 285
14.2 Generation Expansion 286
14.3 Transmission Network Expansion 297
14.4 Distribution Network Expansion 311
14.5 Reactive Power Planning at Generationâ"Transmission Level 320
14.6 Reactive Power Planning at Distribution Level 326
14.7 Conclusions 330
15 Applications to Power System Scheduling 337
Koay Chin Aik, Loi Lei Lai, Kwang Y. Lee, Haiyan Lu, Jong-Bae Park, Yong-Hua Song, Dipti Srinivasan, John G. Vlachogiannis, and I. K. Yu
15.1 Introduction 337
15.2 Economic Dispatch 337
15.3 Maintenance Scheduling 354
15.4 Cogeneration Scheduling 366
15.5 Short-Term Generation Scheduling of Thermal Units 380
15.6 Constrained Load Flow Problem 385
16 Power System Controls 403
Yoshikazu Fukuyama, Hamid Ghezelayagh, Kwang Y. Lee, Chen-Ching Liu, Yong-Hua Song, and Ying Xiao
16.1 Introduction 403
16.2 Power System Controls: Particle Swarm Technique 404
16.3 Power Plant Controller Design with GA 417
16.4 Evolutionary Programming Optimizer and Application in Intelligent Predictive Control 427
16.5 An Interactive Compromise Programming-Based MO Approach to FACTS Control 444
17 Genetic Algorithms for Solving Optimal Power Flow Problems 471
Loi Lei Lai and Nidul Sinha
17.1 Introduction 471
17.2 Genetic Algorithms 473
17.3 Load Flow Problem 478
17.4 Optimal Power Flow Problem 483
17.5 OPF with FACTS Devices 488
17.6 Conclusions 499
18 An Interactive Compromise Programming-Based Multiobjective Approach to FACTS Control 501
Ying Xiao, Yong-Hua Song, and Chen-Ching Liu
18.1 Introduction 501
18.2 Review of Multiobjective Optimization Techniques 503
18.3 Formulated MO Optimization Model 506
18.4 Proposed Interactive Displaced Worst Compromise Programming Method 511
18.5 Proposed Interactive Procedure with WC Displacement 513
18.6 Implementation 516
18.7 Numerical Results 516
18.8 Conclusions 521
19 Hybrid Systems 525
Vladimiro Miranda
19.1 Introduction 525
19.2 Capacitor Sizing and Location and Analytical Sensitivities 527
19.3 Unit Commitment Fuzzy Sets and Cleverer Chromosomes 538
19.4 Voltage/Var Control and Loss Reduction in Distribution Networks with an Evolutionary Self-Adaptive Particle Swarm Optimization Algorithm: EPSO 550
19.5 Conclusions 559
References 560
Index 563
