Advanced Information and Knowledge Processing

By: Kay Chen Tan, Eik Fun Khor, Tong Heng Lee

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Hardcover | 31 May 2005

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Hardcover
312 Pages

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24.13 x 15.24 x 1.91

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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.

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Programming & Software Development Algorithms & Data Structures Computer Science Artificial Intelligence Neural Networks & Fuzzy Systems Mathematics Optimisation Databases Data Capture & Analysis Information Retrieval

Politics & Government Public Administration Mathematical Theory of Computation Business Applications Information Technology General Issue

Introduction	p. 1
Definition	p. 1
Multiobjective Optimization	p. 5
Preview of Chapters	p. 6
Review of MOEAs	p. 9
Introduction	p. 9
Survey of MOEAs	p. 10
Development Trends	p. 14
Outline of Algorithms	p. 15
Conclusions	p. 29
Conceptual Framework and Distribution Preservation Mechanisms for MOEAs	p. 31
Introduction	p. 31
A Conceptual Framework	p. 31
Individual Assessment	p. 32
Elitism	p. 34
Density Assessment	p. 36
Distribution Preservation Mechanisms	p. 38
Performance Metrics on Distribution Quality	p. 38
Evaluation and Comparison	p. 42
Conclusions	p. 49
Decision Supports and Advanced Features for MOEAs	p. 51
Introduction	p. 51
Domination Scheme	p. 51
Pareto-based Domination with Goal Information	p. 52
Goal-Sequence Domination Scheme with Soft/Hard Priority Specifications	p. 53
Optimization with Soft/Hard Constraints	p. 57
Logical Connectives Among Goal and Priority Specifications	p. 58
A Multiobjective Evolutionary Algorithm	p. 59
Dynamic Sharing Distance	p. 59
MOEA Program Flowchart	p. 61
Convergence Trace for MO Optimization	p. 63
Simulation Studies	p. 64
Conclusions	p. 73
Dynamic Population Size and Local Exploration for MOEAs	p. 75
Introduction	p. 75
Incrementing Multiobjective Evolutionary Algorithm	p. 76
Dynamic Population Size	p. 76
Fuzzy Boundary Local Perturbation	p. 77
Program Flowchart of IMOEA	p. 81
Simulation Studies	p. 83
Conclusions	p. 89
A Distributed Cooperative Coevolutionary Multiobjective Algorithm	p. 91
Introduction	p. 91
A Cooperative Coevolutionary Algorithm	p. 92
Coevolution Mechanism	p. 92
Adaptation of Cooperative Coevolution for MO Optimization	p. 93
Extending Operator	p. 95
Flowchart of CCEA	p. 96
A Distributed Cooperative Coevolutionary Algorithm	p. 97
Distributed Evolutionary Computing	p. 97
A Distributed CCEA (DCCEA)	p. 98
Implementation of DCCEA	p. 99
Workload Balancing	p. 102
Simulation Studies	p. 102
Performance Metrics	p. 102
MO Test Problems	p. 103
Simulation Results of CCEA	p. 103
Simulation Results of DCCEA	p. 107
Conclusions	p. 110
Learning the Search Range in Dynamic Environments	p. 111
Introduction	p. 111
Adaptive Search Space	p. 112
Simulation Studies	p. 114
Single-Objective Optimization	p. 114
Multiobjective Optimization I	p. 119
Multiobjective Optimization II	p. 120
Conclusions	p. 122
Performance Assessment and Comparison of MOEAs	p. 125
Introduction	p. 125
MO Performance Metrics	p. 125
MO Test Problems	p. 127
Test Problems of ZDT1, ZDT2, ZDT3, ZDT4, and ZDT6	p. 129
Test Problems of FON, KUR, and POL	p. 131
Test Problem of TLK	p. 132
Test Problem of TLK2	p. 133
Simulation Studies	p. 134
Conclusions	p. 148
A Multiobjective Evolutionary Algorithm Toolbox	p. 151
Introduction	p. 151
Roles and Features of MOEA Toolbox	p. 152
GUIs of MOEA Toolbox	p. 152
Advanced Settings	p. 159
"Model" File	p. 162
Conclusions	p. 164
Evolutionary Computer-Aided Control System Design	p. 165
Introduction	p. 165
Performance-based Design Unification and Automation	p. 166
Design Architecture	p. 166
Control System Formulation	p. 167
Performance Specifications	p. 168
Evolutionary ULTIC Design Application	p. 173
Conclusions	p. 182
Evolutionary Design Automation of Multivariable QFT Control System	p. 183
Introduction	p. 183
Problem Formulation	p. 185
Overview of Tracking and Cross-Coupling Specifications	p. 185
MO QFT Design Formulation	p. 187
MIMO QFT Control Problem	p. 193
Conclusions	p. 202
Evolutionary Design of HDD Servo Control System	p. 203
Introduction	p. 203
The Physical HDD Model	p. 204
Design of HDD Servo Control System	p. 206
The HDD Design Specifications	p. 206
Evolutionary Design	p. 208
Conventional Controllers	p. 211
Robustness Validation	p. 213
Real-Time Implementation	p. 216
Conclusions	p. 217
Evolutionary Scheduling - VRPTW	p. 219
Introduction	p. 219
The Problem Formulation	p. 221
Problem Modeling of VRPTW	p. 221
Solomon's 56 Benchmark Problems for VRPTW	p. 224
A Hybrid Multiobjective Evolutionary Algorithm	p. 226
Multiobjective Evolutionary Algorithms in Combinatorial Applications	p. 227
Program Flowchart of HMOEA	p. 227
Variable-Length Chromosome Representation	p. 229
Specialized Genetic Operators	p. 230
Pareto Fitness Ranking	p. 232
Local Search Exploitation	p. 234
Simulation Results and Comparisons	p. 235
System Specification	p. 235
MO Optimization Performance	p. 235
Specialized Operators and Hybrid Local Search Performance	p. 239
Performance Comparisons	p. 241
Conclusions	p. 247
Evolutionary Scheduling - TTVRP	p. 249
Introduction	p. 249
The Problem Scenario	p. 250
Modeling the Problem Scenarios	p. 251
Mathematical Model	p. 253
Generation of Test Cases	p. 256
Computation Results	p. 258
MO Optimization Performance	p. 259
Computation Results for TEPC and LTTC	p. 265
Comparison Results	p. 268
Conclusions	p. 271
Bibliography	p. 273
Index	p. 293
Table of Contents provided by Ingram. All Rights Reserved.

Advanced Information and Knowledge Processing

At a Glance

Hardcover

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How to return your order

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