Nature-inspired computing paradigms in systems : reliability, availability, maintainability, safety and cost (RAMS+C) and prognostics and health management (PHM)

Front-Matter_2021_Nature-Inspired-Computing-Paradigms-in-Systems
	Front matter
Copyright_2021_Nature-Inspired-Computing-Paradigms-in-Systems
	Copyright
Contributors_2021_Nature-Inspired-Computing-Paradigms-in-Systems
	Contributors
Editor-biographies_2021_Nature-Inspired-Computing-Paradigms-in-Systems
	Editor biographies
Preface_2021_Nature-Inspired-Computing-Paradigms-in-Systems
	Preface
Acknowledgment_2021_Nature-Inspired-Computing-Paradigms-in-Systems
	Acknowledgment
Chapter-1---Reliability-optimization-of-power-plant-_2021_Nature-Inspired-Co
	Reliability optimization of power plant safety system using grey wolf optimizer and shuffled frog-leaping algorith
		Introduction
		Literature review
		Problem description
		Grey wolf optimizer
		Shuffled frog-leaping algorithm
		Results and discussion
		Conclusions
		References
Chapter-2---Design-optimization-of-a-car-side-safe_2021_Nature-Inspired-Comp
	Design optimization of a car side safety system by particle swarm optimization and grey wolf optimizer
		Introduction
		Design optimization of a car side safety system
		Particle swarm optimization
		Grey wolf optimizer
		Results and discussion
		Conclusions
		References
Chapter-3---Genetic-algorithms--Principles_2021_Nature-Inspired-Computing-Pa
	Genetic algorithms: Principles and application in RAMS
		Introduction
		GA construction
			Genetic operators
				Crossover operator
				Mutation operation
			Adaptive and hybrid approaches in the GA
				The GA-PSO framework
		Stop condition
		GA applications
			Reliability-based design optimization
			Reliability allocation problems
			Redundancy allocation problems
				Redundancy allocation for a complex system
				Multilevel redundancy allocation
			Inspection and maintenance planning for one-shot systems
			Joint optimization of spare parts inventory and maintenance policies
		Industry 4.0 and optimization
		Advantages and disadvantages of the GA
		Conclusion
		References
Chapter-4---Evolutionary-optimization-for-resili_2021_Nature-Inspired-Comput
	Evolutionary optimization for resilience-based planning for power distribution networks
		Introduction
		Problem description and formulation
			Power distribution network
			Preventive maintenance actions
			Objective function
			Constraints
				Total number of replacements
				Replacements per period
				Subsequent replacements
			Model
		Solution methodology
			Differential evolution
			Binary differential evolution
			Archiving-based adaptive tradeoff model (ArATM)
		Results
		Conclusions
		References
Chapter-5---Application-of-nature-inspired-computing_2021_Nature-Inspired-Co
	Application of nature-inspired computing paradigms in optimal design of structural engineering problems&mdash
		Introduction
		Nature-inspired algorithms
			Swarm intelligence algorithms
			Bioinspired algorithms
			Physics- and chemistry-based algorithms
		Nature-inspired metaheuristics in optimal design of structural engineering problems
			SI algorithms in optimal design of structural engineering problems
			Bioinspired algorithms in optimal design of structural engineering problems
			Physics- and chemistry-based algorithms in optimal design of structural engineering problems
		Discussion
		Conclusions
		References
Chapter-6---A-data-driven-model-for-fire-safety-stra_2021_Nature-Inspired-Co
	A data-driven model for fire safety strategies assessment using artificial neural networks and genetic algorithms
		Introduction
		Methodology
			Development of ANN-based prediction model
			Optimization using multiobjective-based genetic algorithms
		Results and discussions
			Investigation of fire safety predictors
			Artificial neural network and genetic algorithm
		Conclusions
		Acknowledgments
		References
Chapter-7---Application-of-artificial-neural-netwo_2021_Nature-Inspired-Comp
	Application of artificial neural networks in polymer electrolyte membrane fuel cell system prognostics
		Introduction
		Description of fuel cell test bench and experimental data
		A hybrid approach for PEMFC prognosis
			Effectiveness evaluation of control parameters with BPNN
			Effectiveness evaluation of historical state with ANFIS
			Proposed hybrid approach
		Effectiveness of proposed hybrid approach in PEMFC predictions
			Effectiveness of the proposed hybrid approach at static operating condition
			Effectiveness of proposed hybrid approach at Quasistatic operating condition
		Input parameter optimization using correlation-based analysis
			Correlation-based analysis
			Effectiveness of correlation-based analysis in PEMFC prognosis
		Conclusion
		References
Chapter-8---Reliability-redundancy-allocation-probl_2021_Nature-Inspired-Com
	Reliability redundancy allocation problems under fuzziness using genetic algorithm and dual-connection numbers
		Introduction
		Prerequisite mathematics
		Problem formulation: Reliability redundancy allocation problem (RRAP)
			Notations
			Constraint satisfaction rule
		Solution procedure: Genetic algorithm-based constrained handling approach
		Numerical example
		Concluding remarks
		References
Index_2021_Nature-Inspired-Computing-Paradigms-in-Systems
	Index