Differential Evolution: Fundamentals and Applications in Electrical Engineering

✍ Scribed by Anyong Qing

• Publisher: Wiley-IEEE Press
• Year: 2009
• Tongue: English
• Leaves: 451
• Edition: 1
• Category: Library

✦ Synopsis

Differential evolution is a very simple but very powerful stochastic optimizer. Since its inception, it has proved very efficient and robust in function optimization and has been applied to solve problems in many scientific and engineering fields. In Differential Evolution , Dr. Qing begins with an overview of optimization, followed by a state-of-the-art review of differential evolution, including its fundamentals and up-to-date advances. He goes on to explore the relationship between differential evolution strategies, intrinsic control parameters, non-intrinsic control parameters, and problem features through a parametric study. Findings and recommendations on the selection of strategies and intrinsic control parameter values are presented. Lastly, after an introductory review of reported applications in electrical and electronic engineering fields, different research groups demonstrate how the methods can be applied to such areas as: multicast routing, multisite mapping in grid environments, antenna arrays, analog electric circuit sizing, electricity markets, stochastic tracking in video sequences, and color quantization.Contains a systematic and comprehensive overview of differential evolutionReviews the latest differential evolution researchDescribes a comprehensive parametric study conducted over a large test bedShows how methods can be practically applied to mobile communicationsgrid computingcircuitsimage processingpower engineeringSample applications demonstrated by research groups in the United Kingdom, Australia, Italy, Turkey, China, and Eastern EuropeProvides access to companion website with code examples for downloadDifferential Evolution is ideal for application engineers, who can use the methods described to solve specific engineering problems. It is also a valuable reference for post-graduates and researchers working in evolutionary computation, design optimization and artificial intelligence. Researchers in the optimization field or engineers and managers involved in operations research will also find the book a helpful introduction to the topic.

✦ Table of Contents

1 Where Did the Idea of a Parametric Study on Differential Evolution Come From?......Page 14
3 What is Academic Novelty?......Page 15
4 What is in this Book?......Page 16
5 What is Available from the Companion Website......Page 18
1.1 A General Optimization Problem......Page 39
1.2 Deterministic Optimization Algorithms......Page 45
1.3 Stochastic Optimization Algorithms......Page 56
1.4 Evolutionary Algorithms......Page 59
2.1 Differential Evolution at a Glimpse......Page 79
2.2 Classic Differential Evolution......Page 90
2.3 Intrinsic Control Parameters of Differential Evolution......Page 94
2.4 Differential Evolution as an Evolutionary Algorithm......Page 96
3.1 Handling Mixed Optimization Parameters......Page 99
3.2 Advanced Differential Evolution Strategies......Page 100
3.3 Multi-objective Differential Evolution......Page 110
3.4 Parametric Study on Differential Evolution......Page 116
3.5 Adaptation of Intrinsic Control Parameters of Differential Evolution......Page 118
4.2 Objectives......Page 127
4.3 Scope......Page 128
4.4 Implementation Terminologies......Page 130
4.5 Performance Indicators......Page 131
4.7 Similar Works......Page 132
4.8 A Comparative Study......Page 133
5.2 A Survey on Test Problems......Page 143
5.3 Generating New Test Problems......Page 158
5.4 Tentative Benchmark Test Bed......Page 160
5.5 An Overview of Numerical Simulation......Page 165
6.1 Sphere Function......Page 175
6.2 Step Function 2......Page 186
6.3 Hyper-ellipsoid Function......Page 192
6.4 Qing Function......Page 195
6.5 Schwefel Function 2.22......Page 202
6.6 Schwefel Function 2.26......Page 206
6.7 Schwefel Function 1.2......Page 213
6.8 Rastrigin Function......Page 218
6.9 Ackley Function......Page 222
7.1 Sphere Function......Page 229
7.2 Step Function 2......Page 238
7.3 Hyper-ellipsoid Function......Page 245
7.4 Qing Function......Page 252
7.5 Schwefel Function 2.22......Page 259
7.6 Schwefel Function 2.26......Page 265
7.7 Schwefel Function 1.2......Page 272
7.8 Rastrigin Function......Page 279
7.9 Ackley Function......Page 284
8.1 Introduction......Page 293
8.2 Alternative Search Space......Page 294
8.3 Performance of Differential Evolution......Page 295
8.4 Optimal Population Size and Safeguard Zone......Page 304
8.5 Optimal Mutation Intensity and Crossover Probability for Sphere Function......Page 315
9.1 Communication......Page 324
9.2 Computer Engineering......Page 326
9.3 Control Theory and Engineering......Page 327
9.5 Electromagnetics......Page 328
9.6 Electronics......Page 332
9.8 Power Engineering......Page 334
9.9 Signal and Information Processing......Page 337
10.2 Mathematical Model......Page 348
10.3 Performance Evaluation......Page 353
10.5 Acknowledgement......Page 355
11.1 Introduction......Page 357
11.2 Working Environment......Page 359
11.3 Differential Evolution for Grid Mapping......Page 360
11.4 Experiments in Predetermined Conditions......Page 362
11.5 More Realistic Experiments......Page 366
11.6 Conclusions......Page 368
12.1 Introduction......Page 371
12.2 Antenna Arrays......Page 372
12.3 Synthesis of Multiple Patterns from Time-Modulated Arrays......Page 375
12.4 Pattern Synthesis of Time-Modulated Planar Arrays......Page 378
12.5 Adaptive Nulling with Time-Modulated Antenna Arrays......Page 380
13.1 Introduction......Page 388
13.2 Cost Function......Page 389
13.3 Hybrid Differential Evolution......Page 390
13.4 Device Sizing......Page 394
13.5 Conclusions......Page 401
14.1 Electrical Energy Market......Page 403
14.2 Bidding Strategies in an Electricity Market......Page 407
14.3 Application of Differential Evolution to Strategic Bidding Systems......Page 411
14.4 Case Study......Page 417
14.5 Conclusions......Page 420
15.1 Introduction......Page 423
15.2 3D License Plate Tracking Acquisition Setup......Page 424
15.3 Statistical Bayesian Estimation and Particle Filtering......Page 425
15.4 3D License Plate Tracking Using DEMC Particle Filter......Page 428
15.5 Comparison......Page 429
15.6 Conclusions......Page 431
16.1 Introduction......Page 433
16.4 Experimental Results......Page 434
16.5 Conclusions......Page 439