Studies in Computational Intelligence

Evolutionary Techniques in Reliability Analysis and Optimization

By: Gregory Levitin (Editor)

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Hardcover | 26 October 2006

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This two-volume book covers the recent applications of computational intel- gence techniques in reliability engineering. Research in the area of computational intelligence is growing rapidly due to the many successful applications of these new techniques in very diverse problems. "Computational Intelligence" covers many fields such as neural networks, fuzzy logic, evolutionary computing, and their hybrids and derivatives. Many industries have benefited from adopting this technology. The increased number of patents and diverse range of products dev- oped using computational intelligence methods is evidence of this fact. These techniques have attracted increasing attention in recent years for solving many complex problems. They are inspired by nature, biology, statistical te- niques, physics and neuroscience. They have been successfully applied in solving many complex problems where traditional problem-solving methods have failed. The book aims to be a repository for the current and cutting-edge applications of computational intelligent techniques in reliability analysis and optimization.

Industry Reviews

From the reviews:

"This book is the first volume which covers ... metaheuristic approaches in multiobjective reliability optimization and application of genetic algorithms in reliability optimization. ... the reviewer would like to recommend this book to all reliability researchers to have a serious look at the material presented in the book and to reliability post graduate engineering students and mathematicians. ... would like to congratulate the editor for compiling a voluminous 'repository of the current and cutting edge computational intelligent techniques in reliability analysis and optimization'." (Krishna B. Misra, International Journal of Performability Engineering, Vol. 4 (3), 2008)

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Artificial Intelligence Engineering & Technology Technology in General Engineering in General Mathematics Applied Mathematics Electronics & Communications Engineering Electronics Engineering Maths for Engineers

Mechanical Engineering & Materials Production & Industrial Engineering Industrial Quality Control

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Paperback

Published: 19th November 2010

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Recent Advances in Optimal Reliability Allocation	p. 1
Introduction	p. 3
Problem Formulations	p. 6
Brief Review of Advances In P[subscript 1]-P[subscript 4]	p. 8
Traditional Reliability-Redundancy Allocation Problem (P[subscript 1])	p. 8
Active and Cold-Standby Redundancy	p. 9
Fault-Tolerance Mechanism	p. 10
Percentile Life Optimization Problem (P[subscript 2])	p. 11
MSS Optimization (P[subscript 3])	p. 12
Multi-Objective Optimization (P[subscript 4])	p. 16
Developments in Optimization Techniques	p. 18
Meta-Heuristic Methods	p. 19
Ant Colony Optimization Method	p. 19
Hybrid Genetic Algorithm	p. 20
Tabu Search	p. 21
Other Meta-Heuristic Methods	p. 22
Exact Methods	p. 22
Other Optimization Techniques	p. 23
Comparisons and Discussions of Algorithms Reported in Literature	p. 25
Conclusions and Discussions	p. 27
References	p. 29
Multiobjective Metaheuristic Approaches to Reliability Optimization	p. 37
Introduction	p. 37
Metaheuristics and Multiobjective Optimization	p. 39
Metaheuristics	p. 39
Metaheuristic Approaches to Multiobjective Optimization	p. 41
Multiobjective Tabu Search and Reliability Optimization	p. 42
The Multinomial Tabu Search Algorithm to Solve Redundancy Allocation Problem	p. 42
Multiobjective System Redundancy Allocation Problem	p. 42
Multinomial Tabu Search Algorithm	p. 43
Computational Experiments	p. 45
Multiobjective Genetic Algorithms	p. 47
Multiobjective GA Approaches to Reliability Optimization	p. 48
Multiobjective Optimization of Telecommunication Networks Considering Reliability	p. 50
Network Survivability and Reliability	p. 50
Multiobjective Elitist GA with Restricted Mating	p. 52
Problem Encoding	p. 52
Crossover Operator	p. 52
Mutation	p. 53
Overall Algorithm	p. 53
Computational Experiments	p. 55
Other Metaheuristic Techniques to Multiobjective Reliability Optimization	p. 56
Ant Colony Optimization	p. 56
Simulated Annealing	p. 57
Conclusions	p. 57
References	p. 58
Genetic Algorithm Applications in Surveillance and Maintenance Optimization	p. 63
Introduction	p. 63
Analysis of Published Research	p. 67
Overview of Testing and Maintenance	p. 71
RAMS and the role of T&M at NPP	p. 71
Failure types and T&M activities	p. 77
Decision-making Based on RAMS+C	p. 78
Basis for the RAMS+C informed decision-making	p. 78
Quantification models of RAMS+C	p. 80
Optimization Problem	p. 84
Problem Formulation	p. 84
Solution Approaches	p. 84
Genetic Algorithms	p. 86
Origin, fundamentals and first applications	p. 86
Pioneering GA	p. 87
Development of multi-objective GA	p. 89
Research Areas	p. 91
References	p. 94
Genetic Algorithms and Monte Carlo Simulation for the Optimization of System Design and Operation	p. 101
Introduction	p. 101
Motivations for the GA-MC Approach	p. 102
Use of Monte Carlo Simulation for the System Modeling	p. 102
Use of Multi-Objective Genetic Algorithms for the System Optimization	p. 103
Combination of GA-MC	p. 104
Application to System Optimization Under Uncertainty	p. 105
Structure of the Chapter	p. 106
Fundamentals of Monte Carlo Simulation	p. 106
The System Transport Model	p. 107
Monte Carlo Simulation for Reliability Modeling	p. 107
Genetic Algorithms	p. 112
Introduction	p. 112
The Standard GA Procedure	p. 115
Embedding the Monte Carlo Simulation in the Genetic Algorithm Search Engine	p. 117
Optimization of the Design of a Risky Plant	p. 118
Problem Statement	p. 118
Applying the GA-MC Approach	p. 121
Multi-objective Genetic Algorithms	p. 127
Optimizing the Technical Specifications of the Reactor Protection Instrumentation System of a PWR	p. 129
System Description	p. 130
Testing Procedures and TSs of the RPIS	p. 131
Modeling Assumptions	p. 132
Multiobjective Optimization	p. 140
Conclusions	p. 145
References	p. 147
New Evolutionary Methodologies for Integrated Safety System Design and Maintenance Optimization	p. 151
Introduction	p. 151
Evolutionary Algorithms (EA)	p. 155
Multiple Objective Evolutionary Algorithms	p. 155
Evolutionary Algorithms with Mixed Coding Schemes: A Double Loop Approach	p. 157
Flexible Evolutionary Algorithms	p. 163
Integrated Safety Systems Optimization	p. 167
The System Model	p. 167
Components Unavailability and Cost Models	p. 169
Computing the System Model Using Restricted Sampling	p. 171
Coding Schemes	p. 172
Application example: The Containment Spray System of a Nuclear Power Plant	p. 173
System Description	p. 173
Models and Parameters Selected	p. 175
System Model	p. 176
Results	p. 177
Conclusions	p. 184
References	p. 186
Optimal Redundancy Allocation of Multi-State Systems with Genetic Algorithms	p. 191
Introduction	p. 191
Optimal Redundancy Allocation of Multi-state Systems	p. 191
Optimal Redundancy Allocation of Multi-state Systems with Genetic Algorithms	p. 193
Content of this Chapter	p. 195
System Utility Evaluation	p. 195
Multi-state Series-parallel Systems by Barlow and Wu (1978)	p. 196
The Multi-state Load Sharing Series-parallel Systems by Levitin et al (1998)	p. 198
Multi-state k-out-of-n Systems	p. 200
General Comments	p. 201
Optimization Models for Multi-state Systems	p. 201
Single-objective Optimization Models	p. 202
The Multi-objective Optimization Model	p. 203
Implementation of Genetic Algorithms	p. 206
General Framework of GA	p. 206
Encoding and Decoding	p. 207
Fitness Function Value	p. 208
Fitness Function Value	p. 208
An Application Example	p. 208
Concluding Remarks	p. 211
References	p. 212
Intelligent Interactive Multiobjective Optimization of System Reliability	p. 215
Introduction	p. 215
Multiobjective Optimization Problem	p. 217
Problem Formulation	p. 217
Pareto Solution	p. 217
Weighted-sum Method	p. 218
Augment Weighted Tchebycheff Programs	p. 218
Designer's Preference Structure Model	p. 219
Model Parameter Vector	p. 219
General Multiobjective Optimization Procedure	p. 219
Designer's Preference Structure Model	p. 221
Preference Information Elicitation	p. 222
IIMOM Procedure	p. 222
Application of IIMOM to Reliability Optimization Problem	p. 225
Problem Definition	p. 225
The Mapping from Weight Vector to Preference Value	p. 227
Results and Discussions	p. 228
Discussion on the Performances of IIMOM	p. 234
Conclusions	p. 235
References	p. 236
Reliability Assessment of Composite Power Systems Using Genetic Algorithms	p. 237
Introduction	p. 237
Reliability Evaluation of Composite Generation-Transmission Systems	p. 239
Genetic Algorithms Approach for the Assessment of Composite Systems Annualized Indices	p. 241
Construction of System State Array	p. 244
Evolution of a New Generation	p. 246
Stopping Criterion	p. 247
State Evaluation Model	p. 249
Assessment of Composite System Adequacy Indices	p. 251
Case Studies for the Assessment of Annualized Indices	p. 252
Reliability Indices Considering Chronological Load Curves	p. 254
Modeling of Chronological Load Curve	p. 255
Genetic Algorithms Sampling with m Cluster Load Vectors	p. 257
Genetic Algorithms Parallel Sampling	p. 257
Genetic Algorithm Sampling for Maximum Cluster Load Vector with Series State Revaluation	p. 258
State Evaluation Model	p. 260
Case Studies for the Assessment of Annual Indices	p. 262
Fully Correlated Load Buses	p. 262
Partially Correlated Load Buses	p. 264
Calculation of Frequency and Duration Indices	p. 266
Modeling of the Chronological Load	p. 267
Calculating Transition Rates between Load Clusters	p. 268
Calculating Failure State Contribution to System Failure Frequency	p. 269
Non-Sequential Monte Carlo Sampling	p. 271
Genetic Algorithm Sampling for Maximum Load State with Series State Reevaluation	p. 272
Case Studies for the Assessment of Frequency and Duration Indices	p. 273
Consideration of Multi-State Components	p. 276
Representation of Generating Unit Derated States	p. 276
Consideration of Common Mode Failures in Transmission Lines	p. 278
Case Studies with Multi-State Components	p. 279
Generating Unit Derated States	p. 279
Common Mode Outage	p. 281
Summary and Conclusions	p. 282
References	p. 283
The RBTS Test System Data	p. 285
Genetic Optimization of Multidimensional Technological Process Reliability	p. 287
Introduction	p. 287
Statements of the Problems	p. 288
Models of Multidimensional Technological Process Reliability	p. 289
Basic Notions of Genetic Algorithms	p. 291
Genetic Algorithm for Multidimensional Technological Process Optimization	p. 291
Genetic Coding of Variants	p. 292
Initial Population	p. 292
Crossover and Mutation	p. 293
Fitness Function	p. 294
Fast Calculation of the Reliability	p. 295
Selecting Schemes	p. 295
Computational Experiments	p. 295
Conclusions	p. 300
References	p. 300
Scheduling Multiple-version Programs on Multiple Processors	p. 301
Introduction	p. 301
Scheduling Multiprocessor Tasks - Case of the Statistically Independent Failures	p. 304
Problem Formulation	p. 304
Computational Complexity of Multiprocessor Task Scheduling Problems	p. 306
Approximation Algorithms for Solving Multiprocessor Task Scheduling Problems in the m-p Mode	p. 307
Tabu Search Algorithm Implementation	p. 307
Population Learning Algorithm Implementation	p. 309
Computational Experiment Results	p. 312
Scheduling Multiprocessor Tasks in the Presence of Correlated Failures	p. 313
Multiprocessor Task Reliability Model	p. 313
Approximation Algorithms for Solving Multiprocessor Task Scheduling Problems in the Presence of Correlated Failures	p. 316
Island Based Evolution Algorithm - IBEA	p. 317
Neural Network Algorithm - NNA	p. 319
Hybrid 3opt-tabu Search Algorithm-TSA	p. 321
Population Learning Scheme - PLS	p. 322
Numerical Example and the Results of the Computational Experiment	p. 323
Conclusions	p. 325
References	p. 326
Redundancy Optimization Problems with Uncertain Lifetimes	p. 329
Introduction	p. 329
Uncertain Variables	p. 331
Fuzzy Variable	p. 331
Fuzzy Random Variable	p. 336
Random Fuzzy Variable	p. 338
Redundancy Optimization Problem	p. 340
System Performances	p. 341
Fuzzy Simulations	p. 342
Fuzzy Simulation for E[T(x, [xi])]	p. 342
Fuzzy Simulation for Cr{T(x, [xi]) [Greater than or equal to] T[superscript 0]}	p. 344
Fuzzy Simulation fort T	p. 345
Fuzzy Random Simulation	p. 347
Fuzzy Random simulation for E[T(x, [xi])]	p. 347
Fuzzy Random Simulation for T	p. 348
Fuzzy Random Simulation for System Reliability	p. 348
Random Fuzzy Simulation	p. 351
Random Fuzzy simulation for E[T(x, [xi])]	p. 351
Random Fuzzy Simulation for T	p. 351
Random Fuzzy Simulation for System Reliability	p. 352
Redundancy Optimization Models	p. 354
Redundancy EVMs	p. 354
Redundancy CCP	p. 356
Redundancy DCP	p. 357
Genetic Algorithm Based on Simulation	p. 359
Structure Representation	p. 359
Initialization Process	p. 360
Evaluation Function	p. 360
Selection Process	p. 361
Crossover Operation	p. 361
Mutation Operation	p. 362
The Genetic Algorithm Procedure	p. 363
Numerical Experiments	p. 364
References	p. 372
Computational Intelligence Methods in Software Reliability Prediction	p. 375
Introduction	p. 375
Dynamic Evolutionary Neural Network (D-ENN) Learning	p. 380
Recurrent Neural Network with Bayesian Regularization (RNN-BR)	p. 382
Recurrent Neural Network	p. 382
Bayesian Regularization	p. 385
Modeling Rationale	p. 386
Adaptive Support Vector Machine (A-SVM) Learning	p. 387
SVM Learning in Function Approximation	p. 387
Lagrange Multipliers	p. 389
Kernel Function	p. 389
Formulation of the SVM-Predictor	p. 390
Validation of New Approaches	p. 390
Data Sets Description and Pre-processing	p. 391
Experimental Results	p. 391
Discussions and Future Work	p. 393
Data Type Transformation	p. 394
Modeling Long-Term Behavior	p. 394
Assessment of Predictive Accuracy	p. 395
Incorporating Environmental Factors	p. 395
References	p. 396
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Studies in Computational Intelligence

Evolutionary Techniques in Reliability Analysis and Optimization

At a Glance

Hardcover

Industry Reviews

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