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Multi-objective grey wolf optimizer approach to the reliability-cost optimization of life support system in space capsule

Author

Listed:
  • Anuj Kumar

    (University of Petroleum and Energy Studies)

  • Sangeeta Pant

    (University of Petroleum and Energy Studies)

  • Mangey Ram

    (Graphic Era (Deemed to be University))

  • Shshank Chaube

    (University of Petroleum and Energy Studies)

Abstract

The purpose of this paper is to do the reliability-cost optimization of the life support system (LSS) in a space capsule by using a multi-objective gray wolf optimizer algorithm (MOGWO). MOGWO is a population based metaheuristic which mimics the hierarchal & hunting behavior of grey wolves (Canis lupus). An interactive reliability-cost front has been generated by using MOGWO from which decision makers can choose a point of his/her interest. The efficiency of MOGWO in optimizing the reliability-cost of LSS have also been demonstrated by comparing its results with a very popular swarm based optimization technique named multi-objective particle swarm optimization. A framework based upon MOGWO, which is a very new nature inspired metaheuristic, have been presented for reliability-cost optimization of LSS in a space capsule.

Suggested Citation

  • Anuj Kumar & Sangeeta Pant & Mangey Ram & Shshank Chaube, 2019. "Multi-objective grey wolf optimizer approach to the reliability-cost optimization of life support system in space capsule," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(2), pages 276-284, April.
    • Handle: RePEc:spr:ijsaem:v:10:y:2019:i:2:d:10.1007_s13198-019-00781-1
    DOI: 10.1007/s13198-019-00781-1
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    References listed on IDEAS

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    1. Vadlamani Ravi, 2004. "Optimization Of Complex System Reliability By A Modified Great Deluge Algorithm," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 21(04), pages 487-497.
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    Cited by:

    1. Mousumi Banerjee & Vanita Garg & Kusum Deep, 2023. "Solving structural and reliability optimization problems using efficient mutation strategies embedded in sine cosine algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 307-327, March.
    2. Namrata Rani & Vandana Goyal & Deepak Gupta, 2021. "A solution procedure for multi-objective fully quadratic fractional optimization model," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(6), pages 1447-1458, December.
    3. Rajesh Paramanik & Nirmal Kumar & Sanat Kumar Mahato, 2022. "Solution for the optimality of an intuitionistic fuzzy redundancy allocation problem for complex system using Yager’s ranking method of defuzzification with soft computation," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(2), pages 615-624, April.
    4. Namrata Rani & Vandana Goyal & Deepak Gupta, 2022. "FGP approach and Rouben ranking function to bi-level multi-objective quadratic fractional problem with trapezoidal fuzzy numbers and soft fuzzy constraints," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 113-122, February.

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