多目的問題解決のための進化アルゴリズム<br>Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic Algorithms and Evolutionary Computation)

多目的問題解決のための進化アルゴリズム
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic Algorithms and Evolutionary Computation)

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  • 製本 Hardcover:ハードカバー版/ページ数 610 p.
  • 言語 ENG
  • 商品コード 9780306467622
  • DDC分類 005.1

基本説明

1. Basic Concepts. 2. Evolutionary Algorithm MOP Approaches. 3. MOEA Test Suites. 4. MOEA Testing and Analysis. 5. MOEA Theory and Issues. 6. Applications. 7. MOEA Parallelization. 8. Multi-Criteria Decision Making. 9. Special Topics.

Full Description


The solving of multi-objective problems (MOPs) has been a continuing effort by humans in many diverse areas, including computer science, engineering, economics, finance, industry, physics, chemistry, and ecology, among others. Many powerful and deterministic and stochastic techniques for solving these large dimensional optimization problems have risen out of operations research, decision science, engineering, computer science and other related disciplines. The explosion in computing power continues to arouse extraordinary interest in stochastic search algorithms that require high computational speed and very large memories. A generic stochastic approach is that of evolutionary algorithms (EA). Such algorithms have been demonstrated to be very powerful and generally applicable for solving different single objective problems. Their fundamental algorithmic structures can also be applied to solving many multi-objective problems. In this book, the various features of multi-objective evolutionary algorithms (MOEAs) are presented in an innovative and unique fashion, with detailed customized forms suggested for a variety of applications.Also, extensive MOEA discussion questions and possible research directions are presented at the end of each chapter.

Table of Contents

        List of Figures                            xxiii
List of Tables xxxi
Basic Concepts 1 (58)
Introduction 1 (2)
Definitions 3 (13)
Global Optimization 3 (1)
The Multiobjective Optimization Problem 4 (1)
Decision Variables 4 (1)
Constraints 4 (1)
Commensurable vs Non-Commensurable 5 (1)
Attributes, Criteria, Goals and Objectives 5 (1)
General MOP 6 (1)
Types of MOPs 7 (2)
Ideal Vector 9 (1)
Convexity and Concavity 9 (1)
Pareto Optimum 9 (1)
Pareto Optimality 10 (1)
Pareto Dominance and Pareto Optimal Set 11 (1)
Pareto Front 11 (3)
Weak and Strong Nondominance 14 (1)
Kuhn-Tucker Conditions 15 (1)
MOP Global Minimum 15 (1)
An Example 16 (1)
General Optimization Algorithm Overview 17 (4)
EA Basics 21 (5)
Origins of Multiobjective Optimization 26 (3)
Mathematical Foundations 28 (1)
Early Applications 29 (1)
Classifying Techniques 29 (21)
A priori Preference Articulation 30 (1)
Global Criterion Method 30 (2)
Goal Programming 32 (2)
Goal-Attainment Method 34 (2)
Lexicographic Method 36 (1)
Min-Max Optimization 37 (1)
Multiattribute Utility Theory 38 (2)
Surrogate Worth Trade-Off 40 (1)
Electre 41 (2)
Promethee 43 (2)
A Posteriori Preference Articulation 45 (1)
Linear Combination of Weights 45 (1)
The ε-Constraint Method 45 (1)
Progressive Preference Articulation 46 (1)
Probabilistic Trade-Off Development Method 46 (1)
STEP Method 47 (1)
Sequential Multiobjective Problem Solving 48 (2)
Method
Using Evolutionary Algorithms 50 (4)
Pareto Notation 52 (1)
MOEA Classification 53 (1)
Summary 54 (1)
Discussion Questions 55 (4)
Evolutionary Algorithm Mop Approaches 59 (42)
Introduction 59 (1)
MOEA Research Quantitative Analysis 60 (31)
MOEA Citations 60 (2)
A priori Techniques 62 (1)
Lexicographic Ordering 63 (1)
Criticism of Lexicographic Ordering 63 (1)
Linear Aggregating Functions 64 (1)
Criticism of Linear Aggregating Functions 65 (1)
Nonlinear Aggregating Functions 65 (1)
Criticism of Nonlinear Aggregating 66 (1)
Functions
Criticism of A priori Techniques 66 (1)
Progressive Techniques 67 (1)
Criticism of Progressive Techniques 67 (1)
A posteriori Techniques 67 (1)
Independent Sampling Techniques 68 (1)
Criticism of Independent Sampling 68 (1)
Techniques
Criterion Selection Techniques 68 (2)
Criticism of Criterion Selection 70 (1)
Techniques
Aggregation Selection Techniques 70 (1)
Criticism of Aggregation Selection 70 (1)
Techniques
Pareto Sampling 71 (14)
Criticism of Pareto Sampling Techniques 85 (2)
Criticism of A posteriori Techniques 87 (1)
Other MOEA-related Topics 87 (4)
MOEA Research Qualitative Analysis 91 (2)
Constraint-Handling 93 (1)
MOEA Overview Discussion 94 (1)
Summary 95 (1)
Possible Research Ideas 96 (1)
Discussion Questions 97 (4)
MOEA Test Suites 101 (40)
Introduction 101 (1)
MOEA Test Function Suite Issues 102 (3)
MOP Domain Feature Classification 105 (34)
Unconstrained Numeric MOEA Test Functions 109 (5)
Side-Constrained Numeric MOEA Test 114 (6)
Functions
MOP Test Function Generators 120 (2)
Numerical Considerations---Generated MOPs 122 (2)
Two Objective Generated MOPs 124 (3)
Scalable Generated MOPs 127 (3)
Combinatorial MOEA Test Functions 130 (3)
Real-World MOEA Test Functions 133 (6)
Summary 139 (1)
Possible Research Ideas 139 (1)
Discussion Questions 140 (1)
MOEA Testing And Analysis 141 (38)
Introduction 141 (1)
MOEA Experiments: Motivation and Objectives 142 (1)
Experimental Methodology 143 (11)
MOP Pareto Front Determination 143 (2)
MOEA Test Algorithms 145 (5)
Key Algorithmic Parameters 150 (4)
MOEA Statistical Testing Approaches 154 (10)
MOEA Experimental Metrics 155 (7)
Statistical Testing Techniques 162 (2)
Methods for Presentation of MOEA Results 164 (1)
MOEA Test Results and Analysis 164 (12)
Unconstrained Numerical Test Functions 164 (3)
Side-Constrained Numerical Test Functions 167 (4)
MOEA Performance for 3 Objective Function 171 (2)
MOPs
N P-Complete Test Problems 173 (1)
Application Test Problems 174 (2)
Summary 176 (1)
Possible Research Ideas 176 (1)
Discussion Questions 176 (3)
MOEA Theory and Issues 179 (28)
Introduction 179 (1)
Pareto-Related Theoretical Contributions 180 (10)
Partially Ordered Sets 180 (1)
Pareto Optimal Set Minimal Cardinality 181 (3)
MOEA Convergence 184 (6)
MOEA Theoretical Issues 190 (14)
Fitness Functions 191 (2)
Pareto Ranking 193 (3)
Pareto Niching and Fitness Sharing 196 (5)
Mating Restriction 201 (1)
Solution Stability and Robustness 202 (1)
MOEA Complexity 202 (2)
MOEA Computational ``Cost'' 204 (1)
Summary 204 (1)
Possible Research Ideas 204 (1)
Discussion Questions 205 (2)
Applications 207 (86)
Introduction 207 (2)
Engineering Applications 209 (44)
Environmental, Naval and Hydraulic 210 (6)
Engineering
Electrical and Electronics Engineering 216 (8)
Telecommunications and Network 224 (2)
Optimization
Robotics and Control Engineering 226 (10)
Structural and Mechanical Engineering 236 (7)
Civil and Construction Engineering 243 (1)
Transport Engineering 244 (3)
Aeronautical Engineering 247 (6)
Scientific Applications 253 (14)
Geography 254 (1)
Chemistry 255 (1)
Physics 256 (1)
Medicine 257 (2)
Ecology 259 (1)
Computer Science and Computer Engineering 260 (7)
Industrial Applications 267 (17)
Design and Manufacture 268 (7)
Scheduling 275 (6)
Management 281 (2)
Grouping and Packing 283 (1)
Miscellaneous Applications 284 (5)
Finance 285 (1)
Classification and Prediction 286 (3)
Future Applications 289 (1)
Summary 290 (1)
Possible Research Ideas 290 (1)
Discussion Questions 291 (2)
MOEA Parallelization 293 (28)
Introduction 293 (1)
Parallel MOEA Philosophy 294 (3)
Parallel MOEA Task Decomposition 294 (2)
Parallel MOEA Objective Function 296 (1)
Decomposition
Parallel MOEA Data Decomposition 297 (1)
Parallel MOEA Paradigms 297 (3)
Master-Slave Model 297 (2)
Island Model 299 (1)
Diffusion Model 300 (1)
Parallel MOEA Examples 300 (11)
Master-Slave MOEAs 301 (3)
Island MOEAs 304 (6)
Diffusion MOEAs 310 (1)
Parallel MOEA Analyses and Issues 311 (4)
Parallel MOEA Quantitative Analysis 312 (1)
Parallel MOEA Qualitative Analysis 313 (2)
Parallel MOEA Development & Testing 315 (3)
Specific Developmental Issues 317 (1)
Summary 318 (1)
Possible Research Ideas 318 (1)
Discussion Questions 319 (2)
Multi-Criteria Decision Making 321 (28)
Introduction 321 (1)
Multi-Criteria Decision Making 322 (4)
Operational Attitude of the Decision Maker 324 (1)
When to Get the Preference Information? 324 (2)
Incorporation of Preferences in MOEAs 326 (14)
Definition of Desired Goals 329 (3)
Criticism of Definition of Desired Goals 332 (1)
Utility Functions 332 (1)
Criticism of Utility Functions 333 (1)
Preference Relations 334 (2)
Criticism of Preference Relations 336 (1)
Outranking 336 (2)
Criticism of Outranking 338 (1)
Fuzzy Logic 338 (1)
Criticism of Fuzzy Logic 339 (1)
Compromise Programming 339 (1)
Criticism of Compromise Programming 339 (1)
Issues Deserving Attention 340 (4)
Preserving Dominance 340 (1)
Transitivity 340 (1)
Scalability 341 (1)
Group Decision Making 341 (2)
Other important issues 343 (1)
Summary 344 (1)
Possible Research Ideas 344 (2)
Discussion Questions 346 (3)
Special Topics 349 (40)
Introduction 349 (1)
Simulated Annealing 350 (7)
Basic Concepts 350 (6)
Advantages and Disadvantages of Simulated 356 (1)
Annealing
Tabu Search and Scatter Search 357 (6)
Basic Concepts 358 (4)
Advantages and Disadvantages of Tabu 362 (1)
Search and Scatter Search
Ant System 363 (7)
Basic Concepts 363 (6)
Advantages and Disadvantages of the Ant 369 (1)
System
Distributed Reinforcement Learning 370 (2)
Basic Concepts 370 (2)
Advantages and Disadvantages of 372 (1)
Distributed Reinforcement Learning
Memetic Algorithms 372 (4)
Basic Concepts 373 (3)
Advantages and Disadvantages of Memetic 376 (1)
Algorithms
Other Heuristics 376 (8)
Particle Swarm Optimization 376 (2)
Cultural Algorithms 378 (2)
Immune System 380 (3)
Cooperative Search 383 (1)
Summary 384 (1)
Possible Research Ideas 385 (1)
Discussion Questions 386 (3)
Epilog 389 (4)
Appendix A: MOEA CLASSIFICATION AND TECHNIQUE 393 (62)
ANALYSIS
1 Introduction 393 (1)
1.1 Mathematical Notation 393 (1)
1.2 Presentation Layout 394 (1)
2 A priori MOEA Techniques 394 (12)
2.1 Lexicographic Techniques 394 (2)
2.2 Linear Fitness Combination Techniques 396 (6)
2.3 Nonlinear Fitness Combination 402 (1)
Techniques
2.3.1 Multiplicative Fitness Combination 402 (1)
Techniques
2.3.2 Target Vector Fitness Combination 403 (2)
Techniques
2.3.3 Minimax Fitness Combination 405 (1)
Techniques
3 Progressive MOEA Techniques 406 (2)
4 A posteriori MOEA Techniques 408 (33)
4.1 Independent Sampling Techniques 408 (2)
4.2 Criterion Selection Techniques 410 (2)
4.3 Aggregation Selection Techniques 412 (3)
4.4 Pareto SamplingTechniques 415 (1)
4.4.1 Pareto-Based Selection 416 (7)
4.4.2 Pareto Rank-and Niche-Based 423 (12)
Selection
4.4.3 Pareto Deme-Based Selection 435 (2)
4.4.4 Pareto Elitist-Based Selection 437 (3)
4.5 Hybrid Selection Techniques 440 (1)
5 MOEA Comparisons and Theory 441 (10)
5.1 MOEA Technique Comparisons 441 (9)
5.2 MOEA Theory and Reviews 450 (1)
6 Alternative Multiobjective Techniques 451 (4)
Appendix B: MOPs IN THE LITERATURE 455 (6)
Appendix C: Ptrue & PFtrue FOR SELECTED NUMERIC 461 (10)
MOPs
Appendix D: Ptrue & PFtrue FOR SIDE-CONSTRAINED 471 (6)
MOPs
Appendix E: MOEA SOFTWARE AVAILABILITY 477 (4)
1 Introduction 477 (4)
Appendix F: MOEA-RELATED INFORMATION 481 (8)
1 Introduction 481 (1)
2 Websites of Interest 482 (1)
3 Conferences 482 (1)
4 Journals 482 (1)
5 Researchers 483 (3)
6 Distribution Lists 486 (3)
Index 489 (26)
References 515