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Handbook of Metaheuristics  
Handbook of Metaheuristics
von: Michel Gendreau, Jean-Yves Potvin
Springer-Verlag, 2010
ISBN: 9781441916655
648 Seiten, Download: 8866 KB
 
Format:  PDF
geeignet für: Apple iPad, Android Tablet PC's Online-Lesen PC, MAC, Laptop

Typ: B (paralleler Zugriff)

 

 
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Inhaltsverzeichnis

  Preface 5  
  Preface to First Edition 7  
  Contents 10  
  Contributors 12  
  1 Simulated Annealing 17  
     Alexander G. Nikolaev and Sheldon H. Jacobson 17  
        1.1 Background Survey 17  
           1.1.1 History and Motivation 18  
           1.1.2 Definition of Terms 18  
           1.1.3 Statement of Algorithm 20  
           1.1.4 Discrete Versus Continuous Problems 20  
           1.1.5 Single-objective Versus Multi-objective Problems 21  
        1.2 Convergence Results 23  
           1.2.1 Asymptotic Performance 23  
           1.2.2 Finite-Time Performance 31  
        1.3 Relationship to Other Local Search Algorithms 33  
           1.3.1 Threshold Accepting 33  
           1.3.2 Noising Method 34  
           1.3.3 Tabu Search 35  
           1.3.4 Genetic Algorithms 35  
           1.3.5 Generalized Hill-Climbing Algorithms 36  
        1.4 Practical Guidelines 38  
           1.4.1 Problem-Specific Choices 38  
           1.4.2 Generic Choices 40  
           1.4.3 Domains---Types of Problems with Examples 44  
        1.5 Summary 48  
        References 49  
  2 Tabu Search 56  
     Michel Gendreau and Jean-Yves Potvin 56  
        2.1 Introduction 56  
        2.2 The Classical Vehicle Routing Problem 57  
        2.3 Basic Concepts 58  
           2.3.1 Historical Background 58  
           2.3.2 Tabu Search 59  
           2.3.3 Search Space and Neighborhood Structure 59  
           2.3.4 Tabus 61  
           2.3.5 Aspiration Criteria 62  
           2.3.6 A Template for Simple Tabu Search 62  
           2.3.7 Termination Criteria 63  
           2.3.8 Probabilistic TS and Candidate Lists 63  
        2.4 Intermediate Concepts 64  
           2.4.1 Intensification 64  
           2.4.2 Diversification 65  
           2.4.3 Allowing Infeasible Solutions 65  
           2.4.4 Surrogate and Auxiliary Objectives 66  
        2.5 Advanced Concepts and Recent Trends 67  
        2.6 Key References 68  
        2.7 Tricks of the Trade 68  
           2.7.1 Getting Started 68  
           2.7.2 More Tips 69  
           2.7.3 Additional Tips for Probabilistic TS 69  
           2.7.4 Parameter Calibration and Computational Testing 70  
        2.8 Conclusion 71  
        References 71  
  3 Variable Neighborhood Search 75  
     Pierre Hansen, Nenad Mladenovic, Jack Brimberg and José A. Moreno Pérez 75  
        3.1 Introduction 76  
        3.2 Basic Schemes 77  
        3.3 Some Extensions 82  
        3.4 Variable Neighborhood Formulation Space Search 84  
        3.5 Primal--Dual VNS 85  
        3.6 Variable Neighborhood Branching---VNS for Mixed Integer Linear Programming 86  
        3.7 Variable Neighborhood Search for Continuous Global Optimization 89  
        3.8 Mixed Integer Nonlinear Programming (MINLP) Problem 91  
        3.9 Discovery Science 93  
        3.10 Conclusions 95  
        References 96  
  4 Scatter Search and Path-Relinking: Fundamentals, Advances, and Applications 101  
     Mauricio G.C. Resende, Celso C. Ribeiro, Fred Glover and Rafael Martí 101  
        4.1 Introduction 102  
        4.2 Scatter Search 103  
           4.2.1 New Strategies in Global Optimization 105  
           4.2.2 New Strategies in Combinatorial Optimization 106  
        4.3 Path-Relinking 108  
           4.3.1 Mechanics of Path-Relinking 108  
           4.3.2 Minimum Distance Required for Path-Relinking 112  
           4.3.3 Randomization in Path-Relinking 112  
           4.3.4 Hybridization with a Pool of Elite Solutions 114  
           4.3.5 Evolutionary Path-Relinking 115  
           4.3.6 Progressive Crossover: Hybridization with Genetic Algorithms 116  
           4.3.7 Hybridization of Path-Relinking with Other Heuristics 117  
        4.4 Applications and Concluding Remarks 120  
        References 120  
  5 Genetic Algorithms 122  
     Colin R. Reeves 122  
        5.1 Introduction 122  
        5.2 Basic Concepts 124  
        5.3 Why Does It Work? 127  
           5.3.1 The `Traditional' View 127  
           5.3.2 Other Approaches 129  
        5.4 Applications and Sources 130  
        5.5 Initial Population 131  
        5.6 Termination 132  
        5.7 Crossover Condition 133  
        5.8 Selection 133  
           5.8.1 Ranking 135  
           5.8.2 Tournament Selection 136  
        5.9 Crossover 137  
           5.9.1 Non-linear Crossover 138  
        5.10 Mutation 139  
        5.11 New Population 140  
           5.11.1 Diversity Maintenance 141  
        5.12 Representation 142  
           5.12.1 Binary Problems 142  
           5.12.2 Discrete (but Not Binary) Problems 143  
           5.12.3 Permutation Problems 144  
           5.12.4 Non-binary Problems 145  
        5.13 Random Numbers 145  
        5.14 Conclusions 145  
        References 146  
  6 A Modern Introduction to Memetic Algorithms 153  
     Pablo Moscato and Carlos Cotta 153  
        6.1 Introduction and Historical Notes 153  
        6.2 Memetic Algorithms 155  
           6.2.1 Basic Concepts 155  
           6.2.2 Search Landscapes 157  
           6.2.3 Local vs. Population-Based Search 160  
           6.2.4 Recombination 161  
           6.2.5 A Memetic Algorithm Template 164  
           6.2.6 Designing an Effective Memetic Algorithm 167  
        6.3 Algorithmic Extensions of Memetic Algorithms 169  
           6.3.1 Multiobjective Memetic Algorithms 169  
           6.3.2 Adaptive Memetic Algorithms 170  
           6.3.3 Complete Memetic Algorithms 171  
        6.4 Applications of Memetic Algorithms 172  
        6.5 Challenges and Future Directions 175  
           6.5.1 Learning from Experience 175  
           6.5.2 Exploiting FPT results 176  
           6.5.3 Belief Search in Memetic Algorithms 177  
        6.6 Conclusions 178  
        References 179  
  7 Genetic Programming 196  
     William B. Langdon, Robert I. McKay and Lee Spector 196  
        7.1 Introduction 196  
           7.1.1 Overview 197  
        7.2 Representation, Initialization and Operators in Tree-Based GP 198  
           7.2.1 Representation 198  
           7.2.2 Initializing the Population 199  
           7.2.3 Selection 201  
           7.2.4 Recombination and Mutation 202  
        7.3 Getting Ready to Run Genetic Programming 204  
           7.3.1 Step 1: Terminal Set 204  
           7.3.2 Step 2: Function Set 204  
           7.3.3 Step 3: Fitness Function 206  
           7.3.4 Step 4: GP Parameters 208  
           7.3.5 Step 5: When to Stop and How to Decide Who is the Solution 209  
        7.4 Guiding GP with A Priori Knowledge 209  
           7.4.1 Context-Free Grammars in GP 210  
           7.4.2 Variants of Grammar-Based GP 212  
        7.5 Expanding the Search Space in Genetic Programming 213  
           7.5.1 Evolving Data Structures and Their Use 214  
           7.5.2 Evolving Program and Control Structure 215  
           7.5.3 Evolving Development 216  
           7.5.4 Evolving Evolutionary Mechanisms 217  
        7.6 Applications 217  
           7.6.1 Where GP Has Done Well 218  
           7.6.2 Curve Fitting, Data Modelling and Symbolic Regression 218  
           7.6.3 Image and Signal Processing 221  
           7.6.4 Financial Trading, Time Series Prediction and Economic Modelling 221  
           7.6.5 Industrial Process Control 222  
           7.6.6 Medicine, Biology and Bioinformatics 222  
           7.6.7 GP to Create Searchers and Solvers---Hyper-heuristics 223  
           7.6.8 Entertainment and Computer Games 223  
           7.6.9 The Arts 224  
           7.6.10 Human Competitive Results: The Humies 224  
        7.7 Trouble-Shooting GP 226  
           7.7.1 Can You Trust Your Results? 226  
           7.7.2 Study Your Populations 226  
           7.7.3 Studying Your Programs 227  
           7.7.4 Encourage Diversity 228  
           7.7.5 Approximate Solutions Are Better than No Solution 229  
           7.7.6 Control Bloat 229  
           7.7.7 Convince Your Customers 229  
        7.8 Conclusions 230  
        References 230  
  8 Ant Colony Optimization: Overview and Recent Advances 237  
     Marco Dorigo and Thomas Stützle 237  
        8.1 Introduction 237  
        8.2 Approximate Approaches 238  
           8.2.1 Construction Algorithms 239  
           8.2.2 Local Search Algorithms 240  
        8.3 The ACO Metaheuristic 241  
           8.3.1 Problem representation 242  
           8.3.2 The Metaheuristic 243  
        8.4 History 245  
           8.4.1 Biological Analogy 245  
           8.4.2 Historical Development 246  
        8.5 Applications 250  
           8.5.1 Example 1: The Single Machine Total Weighted Tardiness Scheduling Problem (SMTWTP) 251  
           8.5.2 Example 2: The Set Covering Problem (SCP) 252  
           8.5.3 Example 3: AntNet for Network Routing Applications 253  
           8.5.4 Applications of the ACO Metaheuristic 254  
           8.5.5 Main Application Principles 256  
        8.6 Developments 258  
           8.6.1 Non-standard Applications of ACO 258  
           8.6.2 Algorithmic Developments 260  
           8.6.3 Parallel Implementations 262  
           8.6.4 Theoretical Results 263  
        8.7 Conclusions 264  
        References 265  
  9 Advanced Multi-start Methods 274  
     R. Martí, J. Marcos Moreno-Vega, and A. Duarte 274  
        9.1 Introduction 274  
        9.2 An Overview 275  
           9.2.1 Memory-Based Designs 276  
           9.2.2 GRASP 278  
           9.2.3 Theoretical Analysis 279  
           9.2.4 Constructive Designs 280  
           9.2.5 Hybrid Designs 281  
        9.3 A Classification 282  
        9.4 The Maximum Diversity Problem 283  
           9.4.1 Multi-start Without Memory (MSWoM) 284  
           9.4.2 Multi-start with Memory (MSWM) 285  
           9.4.3 Experimental Results 287  
        9.5 Conclusion 288  
        References 288  
  10 Greedy Randomized Adaptive Search Procedures: Advances, Hybridizations, and Applications 291  
     Mauricio G.C. Resende and Celso C. Ribeiro 291  
        10.1 Introduction 291  
        10.2 Construction of the Restricted Candidate List 294  
        10.3 Alternative Construction Mechanisms 298  
           10.3.1 Random Plus Greedy and Sampled Greedy Construction 298  
           10.3.2 Reactive GRASP 299  
           10.3.3 Cost Perturbations 299  
           10.3.4 Bias Functions 300  
           10.3.5 Intelligent Construction: Memory and Learning 301  
           10.3.6 POP in Construction 302  
        10.4 Path-Relinking 302  
           10.4.1 Forward Path-Relinking 305  
           10.4.2 Backward Path-Relinking 305  
           10.4.3 Back and Forward Path-Relinking 305  
           10.4.4 Mixed Path-Relinking 306  
           10.4.5 Truncated Path-Relinking 306  
           10.4.6 Greedy Randomized Adaptive Path-Relinking 306  
           10.4.7 Evolutionary Path-Relinking 308  
        10.5 Extensions 309  
        10.6 Parallel GRASP 310  
           10.6.1 Cluster Computing 311  
           10.6.2 Grid Computing 315  
        10.7 Applications 317  
        10.8 Concluding Remarks 318  
        References 319  
  11 Guided Local Search 328  
     Christos Voudouris, Edward P.K. Tsang and Abdullah Alsheddy 328  
        11.1 Introduction 328  
        11.2 Background 329  
        11.3 Guided Local Search 330  
        11.4 Implementing Guided Local Search 331  
           11.4.1 Pseudo-code for Guided Local Search 331  
           11.4.2 Guidelines for Implementing the GLS Pseudo-code 332  
        11.5 Guided Fast Local Search 335  
        11.6 Implementing Guided Fast Local Search 336  
           11.6.1 Pseudo-code for Fast Local Search 336  
           11.6.2 Guidelines for Implementing the FLS Pseudo-code 337  
           11.6.3 Pseudo-code for Guided Fast Local Search 339  
           11.6.4 Guidelines for Implementing the GFLS Pseudo-code 340  
        11.7 GLS and Other Metaheuristics 340  
           11.7.1 GLS and Tabu Search 340  
           11.7.2 GLS and Genetic Algorithms 341  
           11.7.3 GLS Hybrids 341  
           11.7.4 Variations and Extensions 343  
        11.8 Overview of Applications 343  
           11.8.1 Radio Link Frequency Assignment Problem 343  
           11.8.2 Workforce Scheduling Problem 344  
           11.8.3 Travelling Salesman Problem 344  
           11.8.4 Function Optimization 344  
           11.8.5 Satisfiability and Max-SAT Problem 345  
           11.8.6 Generalized Assignment Problem 345  
           11.8.7 Processor Configuration Problem 345  
           11.8.8 Vehicle Routing Problem 346  
           11.8.9 Constrained Logic Programming 346  
           11.8.10 Other Applications of GENET and GLS 346  
        11.9 Useful Features for Common Applications 347  
           11.9.1 Routing/Scheduling Problems 347  
           11.9.2 Assignment Problems 348  
           11.9.3 Resource Allocation Problems 348  
           11.9.4 Constrained Optimization Problems 349  
        11.10 Travelling Salesman Problem (TSP) 349  
           11.10.1 Problem Description 349  
           11.10.2 Local Search 350  
           11.10.3 Guided Local Search 351  
           11.10.4 Guided Fast Local Search 352  
        11.11 Quadratic Assignment Problem (QAP) 353  
           11.11.1 Problem Description 353  
           11.11.2 Local Search 353  
           11.11.3 Guided Local Search 354  
        11.12 Workforce Scheduling Problem 355  
           11.12.1 Problem Description 355  
           11.12.2 Local Search 356  
           11.12.3 Guided Local Search 357  
           11.12.4 Guided Fast Local Search 357  
        11.13 Radio Link Frequency Assignment Problem 358  
           11.13.1 Problem Description 358  
           11.13.2 Local Search 359  
           11.13.3 Guided Local Search 361  
           11.13.4 Guided Fast Local Search 361  
        11.14 Summary and Conclusions 362  
        References 364  
  12 Iterated Local Search: Framework and Applications 369  
     Helena R. Lourenço, Olivier C. Martin and Thomas Stützle 369  
        12.1 Introduction 369  
        12.2 Iterating a Local Search 371  
           12.2.1 General Framework 371  
           12.2.2 Random Restart 372  
           12.2.3 Searching in S* 372  
           12.2.4 Iterated Local Search 374  
        12.3 Getting High Performance 376  
           12.3.1 Initial Solution 377  
           12.3.2 Perturbation 378  
           12.3.3 Acceptance Criterion 383  
           12.3.4 Local Search 386  
           12.3.5 Global Optimization of ILS 387  
        12.4 Selected Applications of ILS 389  
           12.4.1 ILS for the TSP 389  
           12.4.2 ILS for Other Problems 391  
           12.4.3 Summary 394  
        12.5 Relation to Other Metaheuristics 395  
           12.5.1 Neighborhood-Based Metaheuristics 395  
           12.5.2 Multi-start-Based Metaheuristics 396  
        12.6 Conclusions 398  
        References 399  
  13 Large Neighborhood Search 404  
     David Pisinger and Stefan Ropke 404  
        13.1 Introduction 404  
           13.1.1 Example Problems 405  
           13.1.2 Neighborhood Search 406  
           13.1.3 Very Large-Scale Neighborhood Search 407  
        13.2 Large Neighborhood Search 411  
           13.2.1 Adaptive Large Neighborhood Search 414  
           13.2.2 Designing an ALNS Algorithm 416  
           13.2.3 Properties of the ALNS Framework 418  
        13.3 Applications of LNS 419  
           13.3.1 Routing Problems 420  
           13.3.2 Scheduling Problems 421  
        13.4 Conclusion 421  
        References 422  
  14 Artificial Immune Systems 425  
     Julie Greensmith, Amanda Whitbrook and Uwe Aickelin 425  
        14.1 Introduction 425  
        14.2 Immunological Inspiration 426  
           14.2.1 Classical Immunology 427  
           14.2.2 The Immunologists' `Dirty Little Secret' 428  
           14.2.3 Costimulation, Infectious Nonself and The Danger Theory 429  
           14.2.4 Idiotypic Networks: Interantibody Interactions 430  
           14.2.5 Summary 431  
        14.3 The Evolution of Artificial Immune Systems 431  
           14.3.1 Computational and Theoretical Immunology 432  
           14.3.2 Negative Selection Approaches 434  
           14.3.3 Clonal Selection Approaches 437  
           14.3.4 Idiotypic Network Approaches 439  
           14.3.5 Danger Theory Approaches 440  
           14.3.6 Conceptual Framework Approaches 442  
           14.3.7 Summary 443  
        14.4 Case Study 1: The Idiotypic Network Approach 443  
        14.5 Case Study 2: The Dendritic Cell Algorithm (DCA) 446  
        14.6 Conclusions 448  
           14.6.1 Future Trends in AIS 449  
        References 449  
  15 A Classification of Hyper-heuristic Approaches 453  
     Edmund K. Burke, Matthew Hyde, Graham Kendall, Gabriela Ochoa, Edmund K. Burke, Matthew Hyde, Graham Kendall, Gabriela Ochoa,  
     453 453  
        15.1 Introduction 453  
        15.2 Previous Classifications 454  
        15.3 The Proposed Classification and New Definition 455  
        15.4 Heuristic Selection Methodologies 457  
           15.4.1 Approaches Based on Construction Low-Level Heuristics 458  
           15.4.2 Approaches Based on Perturbation Low-Level Heuristics 461  
        15.5 Heuristic Generation Methodologies 465  
           15.5.1 Representative Examples 466  
        15.6 Summary and Discussion 468  
        References 469  
  16 Metaheuristic Hybrids 473  
     Günther R. Raidl, Jakob Puchinger and Christian Blum 473  
        16.1 Introduction 473  
        16.2 Classification 475  
        16.3 Finding Initial or Improved Solutions by Embedded Methods 478  
        16.4 Multi-stage Approaches 479  
        16.5 Decoder-Based Approaches 482  
        16.6 Solution Merging 483  
        16.7 Strategic Guidance of Metaheuristics by Other Techniques 485  
           16.7.1 Using Information Gathered by Other Algorithms 485  
           16.7.2 Enhancing the Functionality of Metaheuristics 487  
        16.8 Strategic Guidance of Other Techniques by Metaheuristics 488  
        16.9 Decomposition Approaches 490  
           16.9.1 Exploring Large Neighborhoods 490  
           16.9.2 Cut and Column Generation by Metaheuristics 492  
           16.9.3 Using Metaheuristics for Constraint Propagation 493  
        16.10 Summary and Conclusions 494  
        References 495  
  17 Parallel Meta-heuristics 501  
     Teodor Gabriel Crainic and Michel Toulouse 501  
        17.1 Introduction 501  
        17.2 Meta-heuristics and Parallelism 502  
           17.2.1 Heuristics and Meta-heuristics 503  
           17.2.2 Sources of Parallelism 506  
           17.2.3 Parallel Meta-heuristics Strategies 507  
        17.3 Low-Level 1-Control Parallelization Strategies 509  
           17.3.1 Neighborhood-Based 1C/RS/SPSS Meta-heuristics 511  
           17.3.2 Population-Based 1C/RS/SPSS Meta-heuristics 512  
           17.3.3 Remarks 513  
        17.4 Domain Decomposition 514  
        17.5 Independent Multi-search 517  
        17.6 Cooperative Search Strategies 519  
           17.6.1 pC/KS Synchronous Cooperative Strategies 524  
           17.6.2 pC/C Asynchronous Cooperative Strategies 526  
           17.6.3 pC/KC Asynchronous Cooperative Strategies 530  
        17.7 Perspectives 535  
        References 538  
  18 Reactive Search Optimization: Learning While Optimizing 546  
     Roberto Battiti and Mauro Brunato 546  
        18.1 Introduction 546  
        18.2 Different Reaction Possibilities 550  
           18.2.1 Reactive Prohibitions 550  
           18.2.2 Reacting on the Neighborhood 553  
           18.2.3 Reacting on the Annealing Schedule 556  
           18.2.4 Reacting on the Objective Function 558  
        18.3 Applications of Reactive Search Optimization 560  
           18.3.1 Classic Combinatorial Tasks 561  
           18.3.2 Neural Networks and VLSI Systems with Learning Capabilities 563  
           18.3.3 Continuous Optimization 564  
           18.3.4 Real-World Applications 565  
        References 567  
  19 Stochastic Search in Metaheuristics 575  
     Walter J. Gutjahr 575  
        19.1 Introduction 575  
        19.2 General Framework 576  
        19.3 Convergence Results 579  
        19.4 Runtime Results 581  
           19.4.1 Some Methods for Runtime Analysis 581  
           19.4.2 Instance Difficulty and Phase Transitions 584  
           19.4.3 Some Notes on Special Runtime Results 585  
        19.5 Parameter Choice 587  
        19.6 No-Free-Lunch Theorems 589  
        19.7 Fitness Landscape Analysis 591  
        19.8 Black-Box Optimization 592  
        19.9 Stochastic Search Under Noise 594  
        19.10 Conclusions 595  
        References 596  
  20 An Introduction to Fitness Landscape Analysis and Cost Models for Local Search 600  
     Jean-Paul Watson 600  
        20.1 Introduction 600  
        20.2 Combinatorial Optimization, Local Search, and the Fitness Landscape 602  
           20.2.1 The State Space and the Objective Function 603  
           20.2.2 The Move Operator 603  
           20.2.3 The Navigation Strategy 604  
           20.2.4 The Fitness Landscape 605  
        20.3 Landscape Analysis and Cost Models: Goals and Classification 608  
           20.3.1 Static Cost Models 608  
           20.3.2 Quasi-dynamic Cost Models 609  
           20.3.3 Dynamic Cost Models 610  
           20.3.4 Descriptive Versus Predictive Cost Models 611  
        20.4 Fitness Landscape Features and Static Cost Models 611  
           20.4.1 The Number of Optimal Solutions 612  
           20.4.2 The Distance Between Local Optima 614  
           20.4.3 The Distance Between Local and Global Optima 614  
           20.4.4 Fitness-Distance Correlation 616  
           20.4.5 Solution Backbones 617  
           20.4.6 Landscape Correlation Length 617  
           20.4.7 Phase Transitions 618  
        20.5 Fitness Landscapes and Run-Time Dynamics 618  
        20.6 Conclusions 622  
        References 623  
  21 Comparison of Metaheuristics 625  
     John Silberholz and Bruce Golden 625  
        21.1 Introduction 625  
        21.2 The Testbed 626  
           21.2.1 Using Existing Testbeds 626  
           21.2.2 Developing New Testbeds 626  
           21.2.3 Problem Instance Classification 628  
        21.3 Parameters 628  
           21.3.1 Parameter Space Visualization and Tuning 629  
           21.3.2 Parameter Interactions 631  
           21.3.3 Fair Testing Involving Parameters 633  
        21.4 Solution Quality Comparisons 633  
           21.4.1 Solution Quality Metrics 634  
           21.4.2 Multiobjective Solution Quality Comparisons 635  
        21.5 Runtime Comparisons 635  
           21.5.1 The Best Runtime Comparison Solution 635  
           21.5.2 Other Comparison Methods 636  
           21.5.3 Runtime Growth Rate 637  
           21.5.4 An Alternative to Runtime Comparisons 638  
        21.6 Conclusion 638  
        References 638  
  Subject Index 641  


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