Nowadays, competitiveness in all industrial sectors is due to the number of companies and the requirements of regulations and users. Industrial companies focus on development and acquisition of systems
with a high level of dependability. However, it involves many challenges. During the last decades, various solution techniques have been proposed to deal with these challenges. Nature-inspired computing
techniques have proved their effectiveness in solving hard engineering problems. The present work is
part of nature-inspired paradigms in systems—RAMS+C (Reliability, Availability, Maintainability,
Safety, and Cost) & PHM (Prognostics and Health Management).
The book is divided into eight chapters. Chapter 1 deals with the reliability optimization of a safety
system in the power plant using gray wolf optimizer and the shuffled flog-leaping algorithm. Chapter 2
addresses the design optimization of the car side safety system using particle swarm optimization and
gray wolf optimizer. Chapter 3 presents the basic principles of genetic algorithm and its application in
RAMS. Chapter 4 uses evolutionary optimization for resilience-based planning in power distribution
networks. Chapter 5 presents a review of the application of nature-inspired computing in optimal design. Chapter 6 uses artificial neural networks and genetic algorithms for fire safety strategies assessment. Chapter 7 applies artificial neural networks to proton exchange. Finally, Chapter 8 addresses
reliability redundancy allocation problems with uncertainties using genetic algorithms and dualconnection numbers.