Evolutionary algorithms for solving multi-objective problems:
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| Main Authors: | , , |
|---|---|
| Format: | Kit Book |
| Language: | English |
| Published: |
New York, NY
Springer
2007
|
| Edition: | Second edition |
| Series: | Genetic and evolutionary computation series
|
| Subjects: | |
| Online Access: | Inhaltstext Inhaltsverzeichnis |
| Physical Description: | XXI, 800 S. graph. Darst. |
| ISBN: | 0387332545 9780387332543 9780387367972 |
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| 245 | 1 | 0 | |a Evolutionary algorithms for solving multi-objective problems |c Carlos A. Coello Coello ; Gary B. Lamont ; David A. van Veldhuizen |
| 250 | |a Second edition | ||
| 264 | 1 | |a New York, NY |b Springer |c 2007 | |
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| adam_text | CARLOS A. COELLO COELLO GARY B. LAMONT DAVID A. VAN VELDHUIZEN
EVOLUTIONARY ALGORITHMS FOR SOLVING MULTI-OBJECTIVE PROBLEMS SECOND
EDITION 5)SPRI RINGER CONTENTS 1 BASIC CONCEPTS 1 1.1 INTRODUCTION 1 1.2
DEFINITIONS 3 1.2.1 SINGLE-OBJECTIVE OPTIMIZATION 4 1.2.2 THE
MULTIOBJECTIVE OPTIMIZATION PROBLEM 5 1.2.3 MULTIOBJECTIVE OPTIMIZATION
PROBLEM 7 1.2.4 DEFINITION OF MOEA PROGRESS 14 1.2.5 COMPUTATIONAL
DOMAIN IMPACT 14 1.2.6 PARETO EPSILON MODEL 17 1.2.7 DECISION MAKER
IMPACT 18 1.3 AN EXAMPLE 19 1.4 GENERAL OPTIMIZATION ALGORITHM OVERVIEW
21 1.5 EA BASICS 24 1.6 ORIGINS OF MULTIOBJECTIVE OPTIMIZATION 29 1.6.1
MATHEMATICAL FOUNDATIONS 30 1.6.2 EARLY APPLICATIONS 30 1.7 CLASSIFYING
TECHNIQUES 31 1.7.1 A PRIORI PREFERENCE ARTICULATION 32 1.7.2 A
POSTERIORI PREFERENCE ARTICULATION 46 1.7.3 PROGRESSIVE PREFERENCE
ARTICULATION 47 1.8 USING EVOLUTIONARY ALGORITHMS 51 1.8.1 PARETO
NOTATION 53 1.8.2 MOEA CLASSIFICATION 54 1.9 SUMMARY 55 FURTHER
EXPLORATIONS 57 2 MOP EVOLUTIONARY ALGORITHM APPROACHES 6 1 2.1
INTRODUCTION 61 2.2 MOEA TECHNIQUES 63 2.2.1 A PRIORI TECHNIQUES 65
CONTENTS 2.2.2 PROGRESSIVE TECHNIQUES 70 2.2.3 A POSTERIORI TECHNIQUES
71 2.2.4 GENERIC MOEA GOALS AND OPERATOR DESIGN 77 2.3 STRUCTURES OF
VARIOUS MOEAS 88 2.3.1 MULTI-OBJECTIVE GENETIC ALGORITHM (MOGA) 88 2.3.2
NONDOMINATED SORTING GENETIC ALGORITHM (NSGA) 91 2.3.3 NICHED-PARETO
GENETIC ALGORITHM (NPGA) 94 2.3.4 PARETO ARCHIVED EVOLUTION STRATEGY
(PAES) 95 2.3.5 STRENGTH PARETO EVOLUTIONARY ALGORITHM (SPEA) 97 2.3.6
MULTIOBJECTIVE MESSY GENETIC ALGORITHM (MOMGA) ... 99 2.3.7 PARETO
ENVELOPE-BASED SELECTION ALGORITHM (PESA) ... 101 2.3.8 THE
MICRO-GENETIC ALGORITHM FOR MULTIOBJECTIVE OPTIMIZATION 102 2.3.9
MULTIOBJECTIVE STRUGGLE GA (MOSGA) 105 2.3.10 ORTHOGONAL MULTI-OBJECTIVE
EVOLUTIONARY ALGORITHM (OMOEA) 106 2.3.11 GENERAL MULTIOBJECTIVE
EVOLUTIONARY ALGORITHM (GENMOP) 108 2.3.12 CRITICISM TO PARETO SAMPLING
TECHNIQUES 111 2.4 CONSTRAINT-HANDLING TECHNIQUES 113 2.5 CRITICAL MOEA
ELEMENTS 116 2.5.1 MOEA COMPARISONS 116 2.5.2 MOEA THEORY 116 2.5.3 MOEA
FITNESS FUNCTIONS 117 2.5.4 MOEA CHROMOSOMAL REPRESENTATIONS 117 2.5.5
MOEA PROBLEM DOMAINS 119 2.6 MOEA DESIGN RECAPITULATION 120 2.7 SUMMARY
121 FURTHER EXPLORATIONS 123 3 MOEA LOCAL SEARCH AND COEVOLUTION 131 3.1
INTRODUCTION 131 3.2 MOEA LOCAL SEARCH TECHNIQUES 131 3.2.1 HYBRID MOEA
TECHNIQUES 134 3.2.2 COMMENTS ON HYBRID MOEA TECHNIQUES 143 3.3 MOEA
COEVOLUTIONARY TECHNIQUES 144 3.4 COEVOLUTION AND SYMBIOSIS IN EAS 147
3.4.1 COEVOLUTIONARY ALGORITHMS 147 3.4.2 COOPERATIVE COEVOLUTIONARY
GENETIC ALGORITHMS 149 3.4.3 SYMBIOGENETIC COEVOLUTION 150 3.5
COEVOLUTION AND SYMBIOSIS IN MOEAS 152 3.5.1 ELITIST RECOMBINATIVE MOGA
WITH COEVOLUTIONARY SHARING 152 3.5.2 PARMEE S CO-EVOLUTIONARY MOEA 154
CONTENTS XVII 3.5.3 GENETIC SYMBIOSIS ALGORITHM 155 3.5.4 INTERACTIVE GA
WITH CO-EVOLVING WEIGHTING FACTORS .... 157 3.5.5 MULTIOBJECTIVE
CO-OPERATIVE CO-EVOLUTIONARY GA 158 3.5.6 LOHN S COEVOLUTIONARY GENETIC
ALGORITHM 159 3.5.7 DISTRIBUTED COOPERATIVE COEVOLUTIONARY ALGORITHM
.... 161 3.5.8 COELLO S COEVOLUTIONARY MOEA 163 3.5.9 NONDOMINATED
SORTING COOPERATIVE COEVOLUTIONARY GA 165 3.6 APPLYING COEVOLUTIONARY
MOEAS 165 3.6.1 COEVOLVING MULTIPLE MOEAS 166 3.6.2 COEVOLVING MOEAS
WITH OTHER SEARCH ALGORITHMS 167 3.6.3 COEVOLVING DENSITY ESTIMATORS 167
3.6.4 COEVOLVING TARGET SOLUTIONS 167 3.6.5 COEVOLVING COMPETING
POPULATIONS 168 3.7 FINAL COMMENTS ON COEVOLUTIONARY MOEAS 168 FURTHER
EXPLORATIONS 171 4 MOEA TEST SUITES 175 4.1 INTRODUCTION 175 4.2 MOEA
TEST FUNCTION SUITE ISSUES 176 4.3 MOP DOMAIN FEATURE CLASSIFICATION 179
4.3.1 UNCONSTRAINED NUMERIC MOEA TEST FUNCTIONS 182 4.3.2
SIDE-CONSTRAINED NUMERIC MOEA TEST FUNCTIONS 187 4.3.3 MOP TEST FUNCTION
GENERATORS 193 4.4 GENERIC SCALABLE MOP TEST PROBLEMS 199 4.4.1 OKABE S
TEST FUNCTIONS 207 4.4.2 HUBAND S TEST FUNCTIONS 209 4.5 COMBINATORIAL
MOEA TEST FUNCTIONS 220 4.6 REAL-WORLD MOEA TEST FUNCTIONS. 222 4.7
SUMMARY 228 FURTHER EXPLORATIONS 229 5 MOEA TESTING AND ANALYSIS 233 5.1
INTRODUCTION 233 5.2 MOEA EXPERIMENTS: MOTIVATION AND OBJECTIVES 235 5.3
EXPERIMENTAL METHODOLOGY 236 5.3.1 MOP PARETO FRONT DETERMINATION 236
5.3.2 MOEA ALGORITHMS TESTING 238 5.3.3 KEY MOEA ALGORITHMIC PARAMETERS
239 5.4 MOEA EXPERIMENTAL MEASUREMENTS 243 5.4.1 SELECTION OF MOEA
COMPARISON MEASURES 245 5.4.2 GENERIC ATTAINMENT FUNCTION 245 5.4.3
DOMINANCE RELATIONS 250 5.4.4 PRIMARY QUALITY INDICATORS 254 XVIII
CONTENTS 5.4.5 OTHER MOEA QUALITY INDICATORS 263 5.4.6 MOEA EXPERIMENTAL
METRICS SUMMARY 267 5.5 MOEA STATISTICAL TESTING APPROACHES 268 5.5.1
STATISTICAL TESTING TECHNIQUES 268 5.5.2 NON-PARAMETRIC STATISTICS
(ANALYSIS OF VARIANCE) 270 5.5.3 METHODS FOR PRESENTATION OF MOEA
RESULTS 272 5.5.4 VISUALIZATION OF TEST RESULTS 272 5.6 SOFTWARE SUPPORT
OF MOEA TESTING 273 5.7 SUMMARY 276 FURTHER EXPLORATIONS 277 6 MOEA
THEORY AND ISSUES 283 6.1 INTRODUCTION 283 6.2 PARETO-RELATED
THEORETICAL CONTRIBUTIONS 284 6.2.1 PARTIALLY ORDERED SETS 284 6.2.2
MOEA CONVERGENCE 288 6.3 MOEA THEORETICAL ISSUES 300 6.3.1 FITNESS
LANDSCAPES 300 6.3.2 FITNESS FUNCTIONS 305 6.3.3 PARETO RANKING 307
6.3.4 PARETO NICHING AND FITNESS SHARING 310 6.3.5 RECOMBINATION
OPERATORS 314 6.3.6 MATING RESTRICTIONS 315 6.3.7 SOLUTION STABILITY AND
ROBUSTNESS 317 6.3.8 MOEA COMPLEXITY 317 6.3.9 MOEA SCALABILITY 319
6.3.10 RUNNING TIME ANALYSIS 320 6.3.11 MOEA COMPUTATIONAL COST 326
6.3.12 NFL-THEOREM FOR MULTIOBJECTIVE OPTIMIZATION ALGORITHMS 326 6.3.13
ALTERNATIVE DEFMITIONS OF OPTIMALITY 327 6.3.14 LOCAL SEARCH 329 6.4
SUMMARY 333 FURTHER EXPLORATIONS 335 7 APPLICATIONS 339 7.1 INTRODUCTION
339 7.2 ENGINEERING APPLICATIONS 340 7.2.1 ENVIRONMENTAL, NAVAL AND
HYDRAULIC ENGINEERING 340 7.2.2 ELECTRICAL AND ELECTRONICS ENGINEERING
347 7.2.3 TELECONIMUNICATIONS AND NETWORK OPTIMIZATION 356 7.2.4
ROBOTICS AND CONTROL ENGINEERING 360 7.2.5 STRUCTURAL AND MECHANICAL
ENGINEERING 369 CONTENTS XIX 7.2.6 CIVIL AND CONSTRUCTION ENGINEERING
376 7.2.7 TRANSPORT ENGINEERING 377 7.2.8 AERONAUTICAL ENGINEERING 381
7.3 SCIENTIFIC APPLICATIONS 388 7.3.1 GEOGRAPHY 388 7.3.2 CHEMISTRY 389
7.3.3 PHYSICS 391 7.3.4 MEDICINE 393 7.3.5 ECOLOGY 396 7.3.6 COMPUTER
SCIENCE AND COMPUTER ENGINEERING 397 7.4 INDUSTRIAL APPLICATIONS 407
7.4.1 DESIGN AND MANUFACTURE 408 7.4.2 SCHEDULING 416 7.4.3 MANAGEMENT
424 7.4.4 GROUPING AND PACKING 426 7.5 MISCELLANEOUS APPLICATIONS 428
7.5.1 FINANCE 428 7.5.2 CLASSIFICATION AND PREDICTION 430 7.6 FUTURE
APPLICATIONS 434 7.7 SUMMARY 435 FURTHER EXPLORATIONS 437 8 MOEA
PARALLELIZATION 443 8.1 INTRODUCTION 443 8.2 PMOEA FUNDAMENTAL
BACKGROUND 445 8.2.1 PMOEA NOTATION 445 8.2.2 PMOEA MOTIVATION AND
ISSUES 446 8.3 PMOEA PARADIGMS 450 8.3.1 MASTER-SLAVE PMOEA MODEL 452
8.3.2 ISLAND PMOEA MODELS 455 8.3.3 DIFFUSION PMOEA MODEL 458 8.3.4
HIERARCHICAL HYBRID PMOEA MODELS 459 8.4 PMOEAS FROM THE LITERATURE 460
8.4.1 MASTER-SLAVE PMOEAS 460 8.4.2 ISLAND PMOEAS 465 8.4.3 DIFFUSION
PMOEAS 473 8.5 PMOEA ANALYSES AND ISSUES 475 8.5.1 PMOEA OBSERVATIONS
476 8.5.2 PMOEA SUITABILITY ISSUES 476 8.5.3 PMOEA HARDWARE AND SOFTWARE
ARCHITECTURE ISSUES . . . 477 8.5.4 PMOEA TEST FUNCTION ISSUES 480 8.5.5
PMOEA METRIC/PARAMETER ISSUES 484 8.6 PMOEA DEVELOPMENT ISSUES 488 8.6.1
PMOEA CREATION OPTIONS 490 XX CONTENTS 8.6.2 MASTER-SLAVE IMPLEMENTATION
ISSUES 491 8.6.3 ISLAND IMPLEMENTATION ISSUES 493 8.6.4 DIFFUSION
IMPLEMENTATION ISSUES 499 8.6.5 PARALLEL NICHING ISSUES 500 8.6.6
PARALLEL ARCHIVING ISSUES 502 8.6.7 PMOEA THEORY ISSUES 503 8.7 A
GENERIC PMOEA 503 8.7.1 ENGINEERING A PMOEA 504 8.7.2 GENERICIZING A
PMOEA 507 8.8 CONCLUSIONS 507 FURTHER EXPLORATIONS 509 9 MULTI-CRITERIA
DECISION MAKING 515 9.1 INTRODUCTION 515 9.2 MULTI-CRITERIA DECISION
MAKING 516 9.2.1 OPERATIONAL ATTITUEDE OF THE DECISION MAKER 517 9.2.2
WHEN TO GET THE PREFERENCE INFORMATION? 518 9.3 INCORPORATION OF
PREFERENCES IN MOEAS 520 9.3.1 DEFINITION OF DESIRED GOALS 522 9.3.2
UTILITY FUNCTIONS 526 9.3.3 PREFERENCE RELATIONS 528 9.3.4 OUTRANKING
531 9.3.5 FUZZY LOGIC 533 9.3.6 COMPROMISE PROGRAMMING 535 9.4 ISSUES
DESERVING ATTENTION 536 9.4.1 PRESERVING DOMINANCE 537 9.4.2
TRANSITIVITY 537 9.4.3 SCALABILITY 537 9.4.4 GROUP DECISION MAKING 537
9.4.5 OTHER IMPORTANT ISSUES 539 9.5 SUMMARY 540 FURTHER EXPLORATIONS
541 10 ALTERNATIVE METAHEURISTICS 54 7 10.1 INTRODUCTION 547 10.2
SIMULATED ANNEALING 548 10.2.1 BASIC CONCEPTS 548 10.2.2 MULTIOBJECTIVE
VERSIONS 550 10.2.3 ADVANTAGES AND DISADVANTAGES OF SIMULATED ANNEALING
. 556 10.3 TABU SEARCH AND SCATTER SEARCH 557 10.3.1 BASIC CONCEPTS 558
10.3.2 MULTIOBJECTIVE VERSIONS 559 CONTENTS XXI 10.3.3 ADVANTAGES AND
DISADVANTAGES OF TABU SEARCH AND SCATTER SEARCH 571 10.4 ANT SYSTEM :
572 10.4.1 BASIC CONCEPTS 572 10.4.2 MULTIOBJECTIVE VERSIONS 575 10.4.3
ADVANTAGES AND DISADVANTAGES OF THE ANT SYSTEM 581 10.5 DISTRIBUTED
REINFORCEMENT LEARNING 582 10.5.1 BASIC CONCEPTS 582 10.5.2 ADVANTAGES
AND DISADVANTAGES OF DISTRIBUTED REINFORCEMENT LEARNING 583 10.6
PARTICLE SWARM OPTIMIZATION 584 10.6.1 BASIC CONCEPTS 584 10.6.2
MULTIOBJECTIVE VERSIONS 585 10.6.3 ADVANTAGES AND DISADVANTAGES OF
PARTICLE SWARM OPTIMIZATION 593 10.7 DIFFERENTIAL EVOLUTION 594 10.7.1
MULTIOBJECTIVE VERSIONS 596 10.7.2 ADVANTAGES AND DISADVANTAGES OF
DIFFERENTIAL EVOLUTION 604 10.8 ARTIFICIAL IMMUNE SYSTEMS 604 10.8.1
BASIC CONCEPTS 605 10.8.2 MULTIOBJECTIVE VERSIONS 606 10.8.3 ADVANTAGES
AND DISADVANTAGES OF ARTIFICIAL IMMUNE SYSTEMS 611 10.9 OTHER HEURISTICS
612 10.9.1 CULTURAL ALGORITHMS 612 10.9.2 COOPERATIVE SEARCH 614
LO.LOSUMMARY 616 FURTHER EXPLORATIONS 617 EPILOG 623 REFERENCES 627
INDEX 761
|
| adam_txt |
CARLOS A. COELLO COELLO GARY B. LAMONT DAVID A. VAN VELDHUIZEN
EVOLUTIONARY ALGORITHMS FOR SOLVING MULTI-OBJECTIVE PROBLEMS SECOND
EDITION 5)SPRI RINGER CONTENTS 1 BASIC CONCEPTS 1 1.1 INTRODUCTION 1 1.2
DEFINITIONS 3 1.2.1 SINGLE-OBJECTIVE OPTIMIZATION 4 1.2.2 THE
MULTIOBJECTIVE OPTIMIZATION PROBLEM 5 1.2.3 MULTIOBJECTIVE OPTIMIZATION
PROBLEM 7 1.2.4 DEFINITION OF MOEA PROGRESS 14 1.2.5 COMPUTATIONAL
DOMAIN IMPACT 14 1.2.6 PARETO EPSILON MODEL 17 1.2.7 DECISION MAKER
IMPACT 18 1.3 AN EXAMPLE 19 1.4 GENERAL OPTIMIZATION ALGORITHM OVERVIEW
21 1.5 EA BASICS 24 1.6 ORIGINS OF MULTIOBJECTIVE OPTIMIZATION 29 1.6.1
MATHEMATICAL FOUNDATIONS 30 1.6.2 EARLY APPLICATIONS 30 1.7 CLASSIFYING
TECHNIQUES 31 1.7.1 A PRIORI PREFERENCE ARTICULATION 32 1.7.2 A
POSTERIORI PREFERENCE ARTICULATION 46 1.7.3 PROGRESSIVE PREFERENCE
ARTICULATION 47 1.8 USING EVOLUTIONARY ALGORITHMS 51 1.8.1 PARETO
NOTATION 53 1.8.2 MOEA CLASSIFICATION 54 1.9 SUMMARY 55 FURTHER
EXPLORATIONS 57 2 MOP EVOLUTIONARY ALGORITHM APPROACHES 6 1 2.1
INTRODUCTION 61 2.2 MOEA TECHNIQUES 63 2.2.1 A PRIORI TECHNIQUES 65
CONTENTS 2.2.2 PROGRESSIVE TECHNIQUES 70 2.2.3 A POSTERIORI TECHNIQUES
71 2.2.4 GENERIC MOEA GOALS AND OPERATOR DESIGN 77 2.3 STRUCTURES OF
VARIOUS MOEAS 88 2.3.1 MULTI-OBJECTIVE GENETIC ALGORITHM (MOGA) 88 2.3.2
NONDOMINATED SORTING GENETIC ALGORITHM (NSGA) 91 2.3.3 NICHED-PARETO
GENETIC ALGORITHM (NPGA) 94 2.3.4 PARETO ARCHIVED EVOLUTION STRATEGY
(PAES) 95 2.3.5 STRENGTH PARETO EVOLUTIONARY ALGORITHM (SPEA) 97 2.3.6
MULTIOBJECTIVE MESSY GENETIC ALGORITHM (MOMGA) . 99 2.3.7 PARETO
ENVELOPE-BASED SELECTION ALGORITHM (PESA) . 101 2.3.8 THE
MICRO-GENETIC ALGORITHM FOR MULTIOBJECTIVE OPTIMIZATION 102 2.3.9
MULTIOBJECTIVE STRUGGLE GA (MOSGA) 105 2.3.10 ORTHOGONAL MULTI-OBJECTIVE
EVOLUTIONARY ALGORITHM (OMOEA) 106 2.3.11 GENERAL MULTIOBJECTIVE
EVOLUTIONARY ALGORITHM (GENMOP) 108 2.3.12 CRITICISM TO PARETO SAMPLING
TECHNIQUES 111 2.4 CONSTRAINT-HANDLING TECHNIQUES 113 2.5 CRITICAL MOEA
ELEMENTS 116 2.5.1 MOEA COMPARISONS 116 2.5.2 MOEA THEORY 116 2.5.3 MOEA
FITNESS FUNCTIONS 117 2.5.4 MOEA CHROMOSOMAL REPRESENTATIONS 117 2.5.5
MOEA PROBLEM DOMAINS 119 2.6 MOEA DESIGN RECAPITULATION 120 2.7 SUMMARY
121 FURTHER EXPLORATIONS 123 3 MOEA LOCAL SEARCH AND COEVOLUTION 131 3.1
INTRODUCTION 131 3.2 MOEA LOCAL SEARCH TECHNIQUES 131 3.2.1 HYBRID MOEA
TECHNIQUES 134 3.2.2 COMMENTS ON HYBRID MOEA TECHNIQUES 143 3.3 MOEA
COEVOLUTIONARY TECHNIQUES 144 3.4 COEVOLUTION AND SYMBIOSIS IN EAS 147
3.4.1 COEVOLUTIONARY ALGORITHMS 147 3.4.2 COOPERATIVE COEVOLUTIONARY
GENETIC ALGORITHMS 149 3.4.3 SYMBIOGENETIC COEVOLUTION 150 3.5
COEVOLUTION AND SYMBIOSIS IN MOEAS 152 3.5.1 ELITIST RECOMBINATIVE MOGA
WITH COEVOLUTIONARY SHARING 152 3.5.2 PARMEE'S CO-EVOLUTIONARY MOEA 154
CONTENTS XVII 3.5.3 GENETIC SYMBIOSIS ALGORITHM 155 3.5.4 INTERACTIVE GA
WITH CO-EVOLVING WEIGHTING FACTORS . 157 3.5.5 MULTIOBJECTIVE
CO-OPERATIVE CO-EVOLUTIONARY GA 158 3.5.6 LOHN'S COEVOLUTIONARY GENETIC
ALGORITHM 159 3.5.7 DISTRIBUTED COOPERATIVE COEVOLUTIONARY ALGORITHM
. 161 3.5.8 COELLO'S COEVOLUTIONARY MOEA 163 3.5.9 NONDOMINATED
SORTING COOPERATIVE COEVOLUTIONARY GA 165 3.6 APPLYING COEVOLUTIONARY
MOEAS 165 3.6.1 COEVOLVING MULTIPLE MOEAS 166 3.6.2 COEVOLVING MOEAS
WITH OTHER SEARCH ALGORITHMS 167 3.6.3 COEVOLVING DENSITY ESTIMATORS 167
3.6.4 COEVOLVING TARGET SOLUTIONS 167 3.6.5 COEVOLVING COMPETING
POPULATIONS 168 3.7 FINAL COMMENTS ON COEVOLUTIONARY MOEAS 168 FURTHER
EXPLORATIONS 171 4 MOEA TEST SUITES 175 4.1 INTRODUCTION 175 4.2 MOEA
TEST FUNCTION SUITE ISSUES 176 4.3 MOP DOMAIN FEATURE CLASSIFICATION 179
4.3.1 UNCONSTRAINED NUMERIC MOEA TEST FUNCTIONS 182 4.3.2
SIDE-CONSTRAINED NUMERIC MOEA TEST FUNCTIONS 187 4.3.3 MOP TEST FUNCTION
GENERATORS 193 4.4 GENERIC SCALABLE MOP TEST PROBLEMS 199 4.4.1 OKABE'S
TEST FUNCTIONS 207 4.4.2 HUBAND'S TEST FUNCTIONS 209 4.5 COMBINATORIAL
MOEA TEST FUNCTIONS 220 4.6 REAL-WORLD MOEA TEST FUNCTIONS. 222 4.7
SUMMARY 228 FURTHER EXPLORATIONS 229 5 MOEA TESTING AND ANALYSIS 233 5.1
INTRODUCTION 233 5.2 MOEA EXPERIMENTS: MOTIVATION AND OBJECTIVES 235 5.3
EXPERIMENTAL METHODOLOGY 236 5.3.1 MOP PARETO FRONT DETERMINATION 236
5.3.2 MOEA ALGORITHMS TESTING 238 5.3.3 KEY MOEA ALGORITHMIC PARAMETERS
239 5.4 MOEA EXPERIMENTAL MEASUREMENTS 243 5.4.1 SELECTION OF MOEA
COMPARISON MEASURES 245 5.4.2 GENERIC ATTAINMENT FUNCTION 245 5.4.3
DOMINANCE RELATIONS 250 5.4.4 PRIMARY QUALITY INDICATORS 254 XVIII
CONTENTS 5.4.5 OTHER MOEA QUALITY INDICATORS 263 5.4.6 MOEA EXPERIMENTAL
METRICS SUMMARY 267 5.5 MOEA STATISTICAL TESTING APPROACHES 268 5.5.1
STATISTICAL TESTING TECHNIQUES 268 5.5.2 NON-PARAMETRIC STATISTICS
(ANALYSIS OF VARIANCE) 270 5.5.3 METHODS FOR PRESENTATION OF MOEA
RESULTS 272 5.5.4 VISUALIZATION OF TEST RESULTS 272 5.6 SOFTWARE SUPPORT
OF MOEA TESTING 273 5.7 SUMMARY 276 FURTHER EXPLORATIONS 277 6 MOEA
THEORY AND ISSUES 283 6.1 INTRODUCTION 283 6.2 PARETO-RELATED
THEORETICAL CONTRIBUTIONS 284 6.2.1 PARTIALLY ORDERED SETS 284 6.2.2
MOEA CONVERGENCE 288 6.3 MOEA THEORETICAL ISSUES 300 6.3.1 FITNESS
LANDSCAPES 300 6.3.2 FITNESS FUNCTIONS 305 6.3.3 PARETO RANKING 307
6.3.4 PARETO NICHING AND FITNESS SHARING 310 6.3.5 RECOMBINATION
OPERATORS 314 6.3.6 MATING RESTRICTIONS 315 6.3.7 SOLUTION STABILITY AND
ROBUSTNESS 317 6.3.8 MOEA COMPLEXITY 317 6.3.9 MOEA SCALABILITY 319
6.3.10 RUNNING TIME ANALYSIS 320 6.3.11 MOEA COMPUTATIONAL "COST" 326
6.3.12 NFL-THEOREM FOR MULTIOBJECTIVE OPTIMIZATION ALGORITHMS 326 6.3.13
ALTERNATIVE DEFMITIONS OF OPTIMALITY 327 6.3.14 LOCAL SEARCH 329 6.4
SUMMARY 333 FURTHER EXPLORATIONS 335 7 APPLICATIONS 339 7.1 INTRODUCTION
339 7.2 ENGINEERING APPLICATIONS 340 7.2.1 ENVIRONMENTAL, NAVAL AND
HYDRAULIC ENGINEERING 340 7.2.2 ELECTRICAL AND ELECTRONICS ENGINEERING
347 7.2.3 TELECONIMUNICATIONS AND NETWORK OPTIMIZATION 356 7.2.4
ROBOTICS AND CONTROL ENGINEERING 360 7.2.5 STRUCTURAL AND MECHANICAL
ENGINEERING 369 CONTENTS XIX 7.2.6 CIVIL AND CONSTRUCTION ENGINEERING
376 7.2.7 TRANSPORT ENGINEERING 377 7.2.8 AERONAUTICAL ENGINEERING 381
7.3 SCIENTIFIC APPLICATIONS 388 7.3.1 GEOGRAPHY 388 7.3.2 CHEMISTRY 389
7.3.3 PHYSICS 391 7.3.4 MEDICINE 393 7.3.5 ECOLOGY 396 7.3.6 COMPUTER
SCIENCE AND COMPUTER ENGINEERING 397 7.4 INDUSTRIAL APPLICATIONS 407
7.4.1 DESIGN AND MANUFACTURE 408 7.4.2 SCHEDULING 416 7.4.3 MANAGEMENT
424 7.4.4 GROUPING AND PACKING 426 7.5 MISCELLANEOUS APPLICATIONS 428
7.5.1 FINANCE 428 7.5.2 CLASSIFICATION AND PREDICTION 430 7.6 FUTURE
APPLICATIONS 434 7.7 SUMMARY 435 FURTHER EXPLORATIONS 437 8 MOEA
PARALLELIZATION 443 8.1 INTRODUCTION 443 8.2 PMOEA FUNDAMENTAL
BACKGROUND 445 8.2.1 PMOEA NOTATION 445 8.2.2 PMOEA MOTIVATION AND
ISSUES 446 8.3 PMOEA PARADIGMS 450 8.3.1 MASTER-SLAVE PMOEA MODEL 452
8.3.2 ISLAND PMOEA MODELS 455 8.3.3 DIFFUSION PMOEA MODEL 458 8.3.4
HIERARCHICAL HYBRID PMOEA MODELS 459 8.4 PMOEAS FROM THE LITERATURE 460
8.4.1 MASTER-SLAVE PMOEAS 460 8.4.2 ISLAND PMOEAS 465 8.4.3 DIFFUSION
PMOEAS 473 8.5 PMOEA ANALYSES AND ISSUES 475 8.5.1 PMOEA OBSERVATIONS
476 8.5.2 PMOEA SUITABILITY ISSUES 476 8.5.3 PMOEA HARDWARE AND SOFTWARE
ARCHITECTURE ISSUES . . . 477 8.5.4 PMOEA TEST FUNCTION ISSUES 480 8.5.5
PMOEA METRIC/PARAMETER ISSUES 484 8.6 PMOEA DEVELOPMENT ISSUES 488 8.6.1
PMOEA CREATION OPTIONS 490 XX CONTENTS 8.6.2 MASTER-SLAVE IMPLEMENTATION
ISSUES 491 8.6.3 ISLAND IMPLEMENTATION ISSUES 493 8.6.4 DIFFUSION
IMPLEMENTATION ISSUES 499 8.6.5 PARALLEL NICHING ISSUES 500 8.6.6
PARALLEL ARCHIVING ISSUES 502 8.6.7 PMOEA THEORY ISSUES 503 8.7 A
"GENERIC" PMOEA 503 8.7.1 ENGINEERING A PMOEA 504 8.7.2 "GENERICIZING" A
PMOEA 507 8.8 CONCLUSIONS 507 FURTHER EXPLORATIONS 509 9 MULTI-CRITERIA
DECISION MAKING 515 9.1 INTRODUCTION 515 9.2 MULTI-CRITERIA DECISION
MAKING 516 9.2.1 OPERATIONAL ATTITUEDE OF THE DECISION MAKER 517 9.2.2
WHEN TO GET THE PREFERENCE INFORMATION? 518 9.3 INCORPORATION OF
PREFERENCES IN MOEAS 520 9.3.1 DEFINITION OF DESIRED GOALS 522 9.3.2
UTILITY FUNCTIONS 526 9.3.3 PREFERENCE RELATIONS 528 9.3.4 OUTRANKING
531 9.3.5 FUZZY LOGIC 533 9.3.6 COMPROMISE PROGRAMMING 535 9.4 ISSUES
DESERVING ATTENTION 536 9.4.1 PRESERVING DOMINANCE 537 9.4.2
TRANSITIVITY 537 9.4.3 SCALABILITY 537 9.4.4 GROUP DECISION MAKING 537
9.4.5 OTHER IMPORTANT ISSUES 539 9.5 SUMMARY 540 FURTHER EXPLORATIONS
541 10 ALTERNATIVE METAHEURISTICS 54 7 10.1 INTRODUCTION 547 10.2
SIMULATED ANNEALING 548 10.2.1 BASIC CONCEPTS 548 10.2.2 MULTIOBJECTIVE
VERSIONS 550 10.2.3 ADVANTAGES AND DISADVANTAGES OF SIMULATED ANNEALING
. 556 10.3 TABU SEARCH AND SCATTER SEARCH 557 10.3.1 BASIC CONCEPTS 558
10.3.2 MULTIOBJECTIVE VERSIONS 559 CONTENTS XXI 10.3.3 ADVANTAGES AND
DISADVANTAGES OF TABU SEARCH AND SCATTER SEARCH 571 10.4 ANT SYSTEM :
572 10.4.1 BASIC CONCEPTS 572 10.4.2 MULTIOBJECTIVE VERSIONS 575 10.4.3
ADVANTAGES AND DISADVANTAGES OF THE ANT SYSTEM 581 10.5 DISTRIBUTED
REINFORCEMENT LEARNING 582 10.5.1 BASIC CONCEPTS 582 10.5.2 ADVANTAGES
AND DISADVANTAGES OF DISTRIBUTED REINFORCEMENT LEARNING 583 10.6
PARTICLE SWARM OPTIMIZATION 584 10.6.1 BASIC CONCEPTS 584 10.6.2
MULTIOBJECTIVE VERSIONS 585 10.6.3 ADVANTAGES AND DISADVANTAGES OF
PARTICLE SWARM OPTIMIZATION 593 10.7 DIFFERENTIAL EVOLUTION 594 10.7.1
MULTIOBJECTIVE VERSIONS 596 10.7.2 ADVANTAGES AND DISADVANTAGES OF
DIFFERENTIAL EVOLUTION 604 10.8 ARTIFICIAL IMMUNE SYSTEMS 604 10.8.1
BASIC CONCEPTS 605 10.8.2 MULTIOBJECTIVE VERSIONS 606 10.8.3 ADVANTAGES
AND DISADVANTAGES OF ARTIFICIAL IMMUNE SYSTEMS 611 10.9 OTHER HEURISTICS
612 10.9.1 CULTURAL ALGORITHMS 612 10.9.2 COOPERATIVE SEARCH 614
LO.LOSUMMARY 616 FURTHER EXPLORATIONS 617 EPILOG 623 REFERENCES 627
INDEX 761 |
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| author | Coello Coello, Carlos A. 1967- Lamont, Gary B. Van Veldhuizen, David A. |
| author_GND | (DE-588)1241502579 (DE-588)1067793607 |
| author_facet | Coello Coello, Carlos A. 1967- Lamont, Gary B. Van Veldhuizen, David A. |
| author_role | aut aut aut |
| author_sort | Coello Coello, Carlos A. 1967- |
| author_variant | c c a c cca ccac g b l gb gbl v d a v vda vdav |
| building | Verbundindex |
| bvnumber | BV022656115 |
| classification_rvk | QH 424 ST 134 |
| classification_tum | MAT 910f DAT 536f DAT 537f |
| ctrlnum | (OCoLC)635071989 (DE-599)BVBBV022656115 |
| discipline | Informatik Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Informatik Mathematik Wirtschaftswissenschaften |
| edition | Second edition |
| format | Kit Book |
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| id | DE-604.BV022656115 |
| illustrated | Not Illustrated |
| index_date | 2024-07-02T18:23:18Z |
| indexdate | 2024-07-09T21:02:43Z |
| institution | BVB |
| isbn | 0387332545 9780387332543 9780387367972 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-015862047 |
| oclc_num | 635071989 |
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| owner_facet | DE-703 DE-29T DE-384 DE-91G DE-BY-TUM DE-91 DE-BY-TUM DE-11 |
| physical | XXI, 800 S. graph. Darst. |
| publishDate | 2007 |
| publishDateSearch | 2007 |
| publishDateSort | 2007 |
| publisher | Springer |
| record_format | marc |
| series2 | Genetic and evolutionary computation series |
| spelling | Coello Coello, Carlos A. 1967- Verfasser (DE-588)1241502579 aut Evolutionary algorithms for solving multi-objective problems Carlos A. Coello Coello ; Gary B. Lamont ; David A. van Veldhuizen Second edition New York, NY Springer 2007 XXI, 800 S. graph. Darst. Genetic and evolutionary computation series Evolutionärer Algorithmus (DE-588)4366912-8 gnd rswk-swf Mehrkriterielle Optimierung (DE-588)4610682-0 gnd rswk-swf Evolutionäre Systementwicklung (DE-588)4672442-4 gnd rswk-swf Evolutionärer Algorithmus (DE-588)4366912-8 s Mehrkriterielle Optimierung (DE-588)4610682-0 s DE-604 Evolutionäre Systementwicklung (DE-588)4672442-4 s 1\p DE-604 Lamont, Gary B. Verfasser aut Van Veldhuizen, David A. Verfasser (DE-588)1067793607 aut text/html http://deposit.dnb.de/cgi-bin/dokserv?id=2813140&prov=M&dok_var=1&dok_ext=htm Inhaltstext GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015862047&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Coello Coello, Carlos A. 1967- Lamont, Gary B. Van Veldhuizen, David A. Evolutionary algorithms for solving multi-objective problems Evolutionärer Algorithmus (DE-588)4366912-8 gnd Mehrkriterielle Optimierung (DE-588)4610682-0 gnd Evolutionäre Systementwicklung (DE-588)4672442-4 gnd |
| subject_GND | (DE-588)4366912-8 (DE-588)4610682-0 (DE-588)4672442-4 |
| title | Evolutionary algorithms for solving multi-objective problems |
| title_auth | Evolutionary algorithms for solving multi-objective problems |
| title_exact_search | Evolutionary algorithms for solving multi-objective problems |
| title_exact_search_txtP | Evolutionary algorithms for solving multi-objective problems |
| title_full | Evolutionary algorithms for solving multi-objective problems Carlos A. Coello Coello ; Gary B. Lamont ; David A. van Veldhuizen |
| title_fullStr | Evolutionary algorithms for solving multi-objective problems Carlos A. Coello Coello ; Gary B. Lamont ; David A. van Veldhuizen |
| title_full_unstemmed | Evolutionary algorithms for solving multi-objective problems Carlos A. Coello Coello ; Gary B. Lamont ; David A. van Veldhuizen |
| title_short | Evolutionary algorithms for solving multi-objective problems |
| title_sort | evolutionary algorithms for solving multi objective problems |
| topic | Evolutionärer Algorithmus (DE-588)4366912-8 gnd Mehrkriterielle Optimierung (DE-588)4610682-0 gnd Evolutionäre Systementwicklung (DE-588)4672442-4 gnd |
| topic_facet | Evolutionärer Algorithmus Mehrkriterielle Optimierung Evolutionäre Systementwicklung |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=2813140&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015862047&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT coellocoellocarlosa evolutionaryalgorithmsforsolvingmultiobjectiveproblems AT lamontgaryb evolutionaryalgorithmsforsolvingmultiobjectiveproblems AT vanveldhuizendavida evolutionaryalgorithmsforsolvingmultiobjectiveproblems |