Nature-Inspired Computing Paradigms in Systems: Reliability, Availability, Maintainability, Safety and Cost
- Length: 144 pages
- Edition: 1
- Language: English
- Publisher: Academic Press
- Publication Date: 2021-07-02
- ISBN-10: 012823749X
- ISBN-13: 9780128237496
- Sales Rank: #0 (See Top 100 Books)
Nature-Inspired Computing Paradigms in Systems: Reliability, Availability, Maintainability, Safety and Cost (RAMS+C) and Prognostics and Health Management (PHM) covers several areas that include bioinspired techniques and optimization approaches for system dependability.
The book addresses the issue of integration and interaction of the bioinspired techniques in system dependability computing so that intelligent decisions, design, and architectures can be supported. It brings together these emerging areas under the umbrella of bio- and nature-inspired computational intelligence.
The primary audience of this book includes experts and developers who want to deepen their understanding of bioinspired computing in basic theory, algorithms, and applications. The book is also intended to be used as a textbook for masters and doctoral students who want to enhance their knowledge and understanding of the role of bioinspired techniques in system dependability.
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
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