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Gray wolf optimizer approach to the reliability‐cost optimization of residual heat removal system of a nuclear power plant safety system
Author(s) -
Kumar Anuj,
Pant Sangeeta,
Ram Mangey
Publication year - 2019
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.2499
Subject(s) - particle swarm optimization , nuclear power plant , reliability engineering , residual , reliability (semiconductor) , nuclear power , computer science , multi objective optimization , mathematical optimization , operations research , engineering , algorithm , power (physics) , mathematics , machine learning , ecology , physics , quantum mechanics , nuclear physics , biology
To ensure the safety of nuclear power plants (NPPs), nuclear regulatory agencies set technical specifications (TSs). TSs define the safety‐related operational measures and specify essential requirements and set specific limitations that is necessarily be followed by a nuclear industry to meet the requirements for the safety of an NPP. One of the important bases for the setting of TSs is the estimates of the availability and reliability of various systems and costs associated with an NPP. In this work, authors have presented a framework based upon a hodiernal nature‐inspired metaheuristic called multiobjective gray wolf optimizer (MOGWO) algorithm, which mimic the hierarchal and hunting behavior of gray wolves ( Canis lupus ), for technical specifications optimization of residual heat removal system (RHRS) of an NPP safety system. The efficiency of MOGWO in optimizing the TSs is demonstrated by comparing its results with a very popular swarm‐based optimization technique named multiobjective particle swarm optimization (MOPSO).