This volume includes chapters presenting applications of different metaheuristics in reliability engineering, including ant colony optimization, great deluge algorithm, cross-entropy method and particle swarm optimization. It also presents chapters devoted to cellular automata and support vector machines, and applications of artificial neural networks, a powerful adaptive technique that can be used for learning, prediction and optimization. Several chapters describe aspects of imprecise reliability and applications of fuzzy and vague set theory.

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 recent years. It also contains chapters devoted to different applications of a genetic algorithm in reliability engineering and to combinations of this algorithm with other computational intelligence techniques.

Many real-world systems in engineering are composed of multi-state components that have different performance levels and several failure modes. These have effects on the entire system 's performance.

Most books on reliability theory are devoted to traditional binary models that only allow a system either to function perfectly or fail completely.

The Universal Generating Function in Reliability Analysis and Optimization is the first book that gives a comprehensive description of the universal generating function technique and its applications in both binary and multi-state system reliability analysis.

Features:

• an introduction to the basic tools used in multi-state system reliability and optimisation;
• applications of the universal generating function in the most widely used multi-state systems;
• several examples of how the universal generating function can be adapted to different systems in mechanical, industrial and software engineering

The Universal Generating Function in Reliability Analysis and Optimization will be of value to all those interested in multi-state systems in industrial, electrical and nuclear engineering.

This volume includes chapters presenting applications of different metaheuristics in reliability engineering, including ant colony optimization, great deluge algorithm, cross-entropy method and particle swarm optimization. It also presents chapters devoted to cellular automata and support vector machines, and applications of artificial neural networks, a powerful adaptive technique that can be used for learning, prediction and optimization. Several chapters describe aspects of imprecise reliability and applications of fuzzy and vague set theory.

Many real systems are composed of multi-state components with different performance levels and several failure modes. These affect the whole system's performance.

Most books on reliability theory cover binary models that allow a system only to function perfectly or fail completely.

"The Universal Generating Function in Reliability Analysis and Optimization" is the first book that gives a comprehensive description of the universal generating function technique and its applications in binary and multi-state system reliability analysis.

Features:
- an introduction to basic tools of multi-state system reliability and optimization;
- applications of the universal generating function in widely used multi-state systems;
- examples of the adaptation of the universal generating function to different systems in mechanical, industrial and software engineering.

This monograph will be of value to anyone interested in system reliability, performance analysis and optimization in industrial, electrical and nuclear engineering.

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 recent years. It also contains chapters devoted to different applications of a genetic algorithm in reliability engineering and to combinations of this algorithm with other computational intelligence techniques.