UID:
almahu_9949225686202882
Format:
1 online resource (146 pages) :
,
illustrations (some color)
ISBN:
0-12-823749-X
Series Statement:
Intelligent data centric systems
Note:
Intro -- Nature-Inspired Computing Paradigms in Systems: Reliability, Availability, Maintainability, Safety and Cost (RAMS+C) and ... -- Copyright -- Contents -- Contributors -- Editor biographies -- Preface -- Acknowledgment -- Chapter 1: Reliability optimization of power plant safety system using grey wolf optimizer and shuffled frog-leaping algorith -- 1. Introduction -- 2. Literature review -- 3. Problem description -- 4. Grey wolf optimizer -- 5. Shuffled frog-leaping algorithm -- 6. Results and discussion -- 7. Conclusions -- References -- Chapter 2: Design optimization of a car side safety system by particle swarm optimization and grey wolf optimizer -- 1. Introduction -- 2. Design optimization of a car side safety system -- 3. Particle swarm optimization -- 4. Grey wolf optimizer -- 5. Results and discussion -- 6. Conclusions -- References -- Chapter 3: Genetic algorithms: Principles and application in RAMS -- 1. Introduction -- 2. GA construction -- 2.1. Genetic operators -- 2.1.1. Crossover operator -- 2.1.2. Mutation operation -- 2.2. Adaptive and hybrid approaches in the GA -- 2.2.1. The GA-PSO framework -- 3. Stop condition -- 4. GA applications -- 4.1. Reliability-based design optimization -- 4.2. Reliability allocation problems -- 4.3. Redundancy allocation problems -- 4.3.1. Redundancy allocation for a complex system -- 4.3.2. Multilevel redundancy allocation -- 4.4. Inspection and maintenance planning for one-shot systems -- 4.5. Joint optimization of spare parts inventory and maintenance policies -- 5. Industry 4.0 and optimization -- 6. Advantages and disadvantages of the GA -- 7. Conclusion -- References -- Chapter 4: Evolutionary optimization for resilience-based planning for power distribution networks -- 1. Introduction -- 2. Problem description and formulation -- 2.1. Power distribution network.
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2.2. Preventive maintenance actions -- 2.3. Objective function -- 2.4. Constraints -- 2.4.1. Total number of replacements -- 2.4.2. Replacements per period -- 2.4.3. Subsequent replacements -- 2.5. Model -- 3. Solution methodology -- 3.1. Differential evolution -- 3.2. Binary differential evolution -- 3.3. Archiving-based adaptive tradeoff model (ArATM) -- 4. Results -- 5. Conclusions -- References -- Chapter 5: Application of nature-inspired computing paradigms in optimal design of structural engineering problems -- 1. Introduction -- 2. Nature-inspired algorithms -- 2.1. Swarm intelligence algorithms -- 2.2. Bioinspired algorithms -- 2.3. Physics- and chemistry-based algorithms -- 3. Nature-inspired metaheuristics in optimal design of structural engineering problems -- 3.1. SI algorithms in optimal design of structural engineering problems -- 3.2. Bioinspired algorithms in optimal design of structural engineering problems -- 3.3. Physics- and chemistry-based algorithms in optimal design of structural engineering problems -- 4. Discussion -- 5. Conclusions -- References -- Chapter 6: A data-driven model for fire safety strategies assessment using artificial neural networks and genetic algorithms -- 1. Introduction -- 2. Methodology -- 2.1. Development of ANN-based prediction model -- 2.2. Optimization using multiobjective-based genetic algorithms -- 3. Results and discussions -- 3.1. Investigation of fire safety predictors -- 3.2. Artificial neural network and genetic algorithm -- 4. Conclusions -- Acknowledgments -- References -- Chapter 7: Application of artificial neural networks in polymer electrolyte membrane fuel cell system prognostics -- 1. Introduction -- 2. Description of fuel cell test bench and experimental data -- 3. A hybrid approach for PEMFC prognosis -- 3.1. Effectiveness evaluation of control parameters with BPNN.
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3.2. Effectiveness evaluation of historical state with ANFIS -- 3.3. Proposed hybrid approach -- 4. Effectiveness of proposed hybrid approach in PEMFC predictions -- 4.1. Effectiveness of the proposed hybrid approach at static operating condition -- 4.2. Effectiveness of proposed hybrid approach at Quasistatic operating condition -- 5. Input parameter optimization using correlation-based analysis -- 5.1. Correlation-based analysis -- 5.2. Effectiveness of correlation-based analysis in PEMFC prognosis -- 6. Conclusion -- References -- Chapter 8: Reliability redundancy allocation problems under fuzziness using genetic algorithm and dual-connection numbers -- 1. Introduction -- 2. Prerequisite mathematics -- 3. Problem formulation: Reliability redundancy allocation problem (RRAP) -- 3.1. Notations -- 3.2. Constraint satisfaction rule -- 4. Solution procedure: Genetic algorithm-based constrained handling approach -- 5. Numerical example -- 6. Concluding remarks -- References -- Index.
Additional Edition:
Print version: ISBN 9780128237496
Language:
English
