Multiobjective Evolutionary Algorithms and ApplicationsSpringer Science & Business Media, 04.05.2005 - 295 Seiten Multiobjective Evolutionary Algorithms and Applications provides comprehensive treatment on the design of multiobjective evolutionary algorithms and their applications in domains covering areas such as control and scheduling. Emphasizing both the theoretical developments and the practical implementation of multiobjective evolutionary algorithms, a profound mathematical knowledge is not required. Written for a wide readership, engineers, researchers, senior undergraduates and graduate students interested in the field of evolutionary algorithms and multiobjective optimization with some basic knowledge of evolutionary computation will find this book a useful addition to their book case. |
Inhalt
Introduction | 1 |
12 Multiobjective Optimization | 5 |
13 Preview of Chapters | 6 |
Review of MOEAs | 9 |
22 Survey of MOEAs | 10 |
23 Development Trends | 14 |
24 Outline of Algorithms | 15 |
25 Conclusions | 29 |
834 Test Problem of TLK2 | 133 |
84 Simulation Studies | 134 |
85 Conclusions | 148 |
A Multiobjective Evolutionary Algorithm Toolbox | 151 |
92 Roles and Features of MOEA Toolbox | 152 |
922 Advanced Settings | 159 |
923 Model File | 162 |
93 Conclusions | 164 |
Conceptual Framework and Distribution Preservation Mechanisms for MOEAs | 31 |
321 Individual Assessment | 32 |
322 Elitism | 34 |
323 Density Assessment | 36 |
33 Distribution Preservation Mechanisms | 38 |
332 Evaluation and Comparison | 42 |
34 Conclusions | 49 |
Decision Supports and Advanced Features for MOEAs | 51 |
421 Paretobased Domination with Goal Information | 52 |
422 GoalSequence Domination Scheme with SoftHard Priority Specifications | 53 |
423 Optimization with SoftHard Constraints | 57 |
424 Logical Connectives Among Goal and Priority Specifications | 58 |
43 A Multiobjective Evolutionary Algorithm | 59 |
432 MOEA Program Flowchart | 61 |
433 Convergence Trace for MO Optimization | 63 |
44 Simulation Studies | 64 |
45 Conclusions | 73 |
Dynamic Population Size and Local Exploration for MOEAs | 75 |
52 Incrementing Multiobjective Evolutionary Algorithm | 76 |
522 Fuzzy Boundary Local Perturbation | 77 |
523 Program Flowchart of IMOEA | 81 |
53 Simulation Studies | 83 |
54 Conclusions | 89 |
A Distributed Cooperative Coevolutionary Multiobjective Algorithm | 91 |
62 A Cooperative Coevolutionary Algorithm | 92 |
622 Adaptation of Cooperative Coevolution for MO Optimization | 93 |
623 Extending Operator | 95 |
624 Flowchart of CCEA | 96 |
63 A Distributed Cooperative Coevolutionary Algorithm | 97 |
632 A Distributed CCEA DCCEA | 98 |
633 Implementation of DCCEA | 99 |
634 Workload Balancing | 102 |
642 MO Test Problems | 103 |
644 Simulation Results of DCCEA | 107 |
65 Conclusions | 110 |
Learning the Search Range in Dynamic Environments | 111 |
72 Adaptive Search Space | 112 |
73 Simulation Studies | 114 |
732 MO Optimization I | 119 |
733 MO Optimization II | 120 |
74 Conclusions | 122 |
Performance Assessment and Comparison of MOEAs | 125 |
83 MO Test Problems | 127 |
831 Test Problems of ZDT1 ZDT2 ZDT3 ZDT4 and ZDT6 | 129 |
832 Test Problems of FON KUR and POL | 131 |
833 Test Problem of TLK | 132 |
Evolutionary ComputerAided Control System Design | 165 |
102 Performancebased Design Unification and Automation | 166 |
1022 Control System Formulation | 167 |
1023 Performance Specifications | 168 |
103 Evolutionary ULTIC Design Application | 173 |
104 Conclusions | 182 |
Evolutionary Design Automation of Multivariable QFT Control System | 183 |
112 Problem Formulation | 185 |
1122 MO QFT Design Formulation | 187 |
113 MIMO QFT Control Problem | 193 |
114 Conclusions | 202 |
Evolutionary Design of HDD Servo Control System | 203 |
122 The Physical HDD Model | 204 |
123 Design of HDD Servo Control System | 206 |
1232 Evolutionary Design | 208 |
1233 Conventional Controllers | 211 |
1234 Robustness Validation | 213 |
1235 RealTime Implementation | 216 |
124 Conclusions | 217 |
Evolutionary Scheduling VRPTW | 219 |
132 The Problem Formulation | 221 |
1322 Solomons 56 Benchmark Problems for VRPTW | 224 |
133 A Hybrid Multiobjective Evolutionary Algorithm | 226 |
1331 Multiobjective Evolutionary Algorithms in Combinatorial Applications | 227 |
1333 VariableLength Chromosome Representation | 229 |
1334 Specialized Genetic Operators | 230 |
1335 Pareto Fitness Ranking | 232 |
1336 Local Search Exploitation | 234 |
134 Simulation Results and Comparisons | 235 |
1343 Specialized Operators and Hybrid Local Search Performance | 239 |
1344 Performance Comparisons | 241 |
135 Conclusions | 247 |
Evolutionary SchedulingTTVRP | 249 |
142 The Problem Scenario | 250 |
1421 Modeling the Problem Scenarios | 251 |
1422 Mathematical Model | 253 |
1423 Generation of Test Cases | 256 |
143 Computation Results | 258 |
1431 MO Optimization Performance | 259 |
1432 Computation Results for TEPC and LTTC | 265 |
1433 Comparison Results | 268 |
144 Conclusions | 271 |
Bibliography | 273 |
293 | |
Andere Ausgaben - Alle anzeigen
Multiobjective Evolutionary Algorithms and Applications Kay Chen Tan,Eik Fun Khor,Tong Heng Lee Eingeschränkte Leseprobe - 2005 |
Multiobjective Evolutionary Algorithms and Applications Kay Chen Tan,Eik Fun Khor,Tong Heng Lee Keine Leseprobe verfügbar - 2010 |
Häufige Begriffe und Wortgruppen
approach archive Box plots CCEA PAES PESA chromosome closed-loop constraints convergence trace crossover customers decision variable dynamic sharing evaluations evolution Evolutionary Computation FBLP Fonseca and Fleming frequency genetic algorithm genetic operators goal setting grid heuristic HLGA MOGA NPGA HMOEA local search Matlab Maximum Spread metric minimization MO optimization MOEA toolbox multiobjective evolutionary algorithm multiobjective optimization multiple mutation niche nondominated individuals NPGA PAES NSGAII SPEA2 IMOEA number of trailers number of trucks number of vehicles objective components objective domain objective functions optimization problem PAES PESA NSGAII parameter Pareto dominance Pareto front Pareto optimal peers performance perturbation PESA NSGAII SPEA2 PID controller population distribution priority progress ratio robust routing cost search space selection Setup sharing distance shown in Fig simulation results Simulink step response subpopulations TEPS test problems tion tracking tradeoff curve tradeoff surface TTVRP UD-G UD-N vector VEGA HLGA vehicle routing problem VRPTW ZDT3 ZDT4 ZDT6 Zitzler
Beliebte Passagen
Seite 290 - Zames G (1966) On the input-output stability of time- varying nonlinear feedback systems- Parts I and II.
Seite 279 - State-space formulae for all stabilizing controllers that satisfy an -//.^ norm bound and relations to risk sensitivity.
Seite 282 - Roucairol (Eds.), Meta-heuristics, Advances and Trends in Local Search Paradigms for Optimization, Kluwer Academic Publishers, 1999, pp.
Verweise auf dieses Buch
Multi-Objective Memetic Algorithms Chi-Keong Goh,Yew Soon Ong,Kay Chen Tan Eingeschränkte Leseprobe - 2009 |