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Optimization of Redundancy Allocation Problem Using Quantum Particle Swarm Optimization Algorithm Under Uncertain Environment

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Advances in Reliability, Failure and Risk Analysis

Part of the book series: Industrial and Applied Mathematics ((INAMA))

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Abstract

Reliability optimization of a redundancy allocation problem is an important area of research in the literature. The main purpose of this type of problem is to enhance the reliability of the system. In this paper, our target is to maximize the reliability of a redundancy allocation problem with time-dependent component failure rates considering the control parameters as trapezoidal fuzzy numbers. To compute the maximum system reliability of the series–parallel system softly, a novel quantum particle swarm optimization (QPSO) is used. In this QPSO algorithm, the decision variables are assigned by the position of the particles and the fitness value of each particle is evaluated using the reliability function related to this work. All the coding of QPSO algorithm is done in C++. Finally, a numerical example is solved to clarify the sensitivity of the proposed algorithm with respect to the crisp as well as the fuzzy atmospheres.

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Authors and Affiliations

  1. Department of Mathematics, Sidho-Kanho-Birsha University, Purulia, West Bengal, 723104, India

    Rajesh Paramanik, Sanat Kumar Mahato & Nabaranjan Bhattacharyee

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  1. Rajesh Paramanik
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  2. Sanat Kumar Mahato
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  3. Nabaranjan Bhattacharyee
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Correspondence to Sanat Kumar Mahato .

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  1. School of Mathematics, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Harish Garg

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Paramanik, R., Mahato, S.K., Bhattacharyee, N. (2023). Optimization of Redundancy Allocation Problem Using Quantum Particle Swarm Optimization Algorithm Under Uncertain Environment. In: Garg, H. (eds) Advances in Reliability, Failure and Risk Analysis. Industrial and Applied Mathematics. Springer, Singapore. https://doi.org/10.1007/978-981-19-9909-3_8

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