Computational Intelligence in Reliability Engineering: Evolutionary Techniques in Reliability Analysis and Optimization

ISBN-13: 9783540373674 / Angielski / Twarda / 2006 / 398 str.

Gregory Levitin

Opis

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.

Recenzje

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)

Spis treści

Recent Advances in Optimal Reliability Allocation.- Multiobjective Metaheuristic Approaches to Reliability Optimization.- Genetic Algorithm Applications in Surveillance and Maintenance Optimization.- Genetic Algorithms and Monte Carlo Simulation for the Optimization of System Design and Operation.- New Evolutionary Methodologies for Integrated Safety System Design and Maintenance Optimization.- Optimal Redundancy Allocation of Multi-State Systems with Genetic Algorithms.- Intelligent Interactive Multiobjective Optimization of System Reliability.- Reliability Assessment of Composite Power Systems Using Genetic Algorithms.- Genetic Optimization of Multidimensional Technological Process Reliability.- Scheduling Multiple-version Programs on Multiple Processors.- Redundancy Optimization Problems with Uncertain Lifetimes.- Computational Intelligence Methods in Software Reliability Prediction.

O autorze

Dr. Gregory Levitin is an 'engineer-expert' in the Reliability and Equipment Department of the R and D Division of the Israel Electric Corporation and an adjunct senior lecturer at Technion, the Israel Institute of Technology. He has held these posts since 1993. He is considered to be an authoritative and experienced researcher in reliability and optimization. In addition to being a senior member of the IEEE, Dr. Levitin is also a member on the editorial board of the journal Reliability Engineering and System Safety. In 2000, Dr. Levitin was awarded a prize in the London Electricity Innovation Challenge. He has also been the organizer of special sessions on multi-state reliability at the Mathematic Methods in Reliability conferences in 2002 and 2004.

Blurb

This book covers the recent applications of computational intelligence techniques in reliability engineering. This volume contains a survey of the contributions made to the optimal reliability design literature in the resent years and chapters devoted to different applications of a genetic algorithm in reliability engineering and to combinations of this algorithm with other computational intelligence techniques. Genetic algorithms are one of the most widely used metaheuristics, inspired by the optimization procedure that exists in nature, the biological phenomenon of evolution.