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Hybrid genetic and particle swarm algorithm: redundancy allocation problem

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  • Sarita Devi

    (G. D. Goenka University)

  • Deepika Garg

    (G. D. Goenka University)

Abstract

Redundancy allocation problem (RAP) is a non-linear programming problem which is very difficult to solve through existing heuristic and non-heuristic methods. In this research paper, three algorithms namely heuristic algorithm (HA), constraint optimization genetic algorithm (COGA) and hybrid genetic algorithm combined with particle swarm optimization (HGAPSO) are applied to solve RAP. Results obtained from individual use of genetic algorithm (GA) and particle swarm optimization (PSO) encompass some shortcomings. To overcome the shortcomings with their individual use, HGAPSO is introduced which combines fascinating properties of GA and PSO. Iterative process of GA is used by this hybrid approach after fixing initial best population from PSO. The results obtained from HA, COGA and HGAPSO with respect to increase in reliability are 50.76, 47.30 and 62.31 respectively and results with respect to CPU time obtained are 0.15, 0.209 and 3.07 respectively as shown in Table 3 of this paper. COGA and HGAPSO are programmed by Matlab.

Suggested Citation

  • Sarita Devi & Deepika Garg, 2020. "Hybrid genetic and particle swarm algorithm: redundancy allocation problem," 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. 11(2), pages 313-319, April.
    • Handle: RePEc:spr:ijsaem:v:11:y:2020:i:2:d:10.1007_s13198-019-00858-x
    DOI: 10.1007/s13198-019-00858-x
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    References listed on IDEAS

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    1. Ouyang, Zhiyuan & Liu, Yu & Ruan, Sheng-Jia & Jiang, Tao, 2019. "An improved particle swarm optimization algorithm for reliability-redundancy allocation problem with mixed redundancy strategy and heterogeneous components," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 62-74.
    2. Chang, Wei-Der, 2009. "PID control for chaotic synchronization using particle swarm optimization," Chaos, Solitons & Fractals, Elsevier, vol. 39(2), pages 910-917.
    3. Rashika Gupta & Manju Agarwal, 2006. "Penalty guided genetic search for redundancy optimization in multi-state series-parallel power system," Journal of Combinatorial Optimization, Springer, vol. 12(3), pages 257-277, November.
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    Cited by:

    1. Ding, Yi & Hu, Yishuang & Li, Daqing, 2021. "Redundancy Optimization for Multi-Performance Multi-State Series-Parallel Systems Considering Reliability Requirements," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Yishuang Hu & Yi Ding & Zhiguo Zeng, 2022. "Redundancy optimization for multi-state series-parallel systems using ordinal optimization-based-genetic algorithm," Journal of Risk and Reliability, , vol. 236(1), pages 66-78, February.
    3. Chowdury, Md. Abdul Malek & Nath, Rahul & Shukla, Amit K. & Rauniyar, Amit & Muhuri, Pranab K., 2024. "Multi-task optimization in reliability redundancy allocation problem: A multifactorial evolutionary-based approach," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    4. Nabaranjan Bhattacharyee & Nirmal Kumar & Sanat Kumar Mahato & Puja Supakar, 2022. "Reliability of the illumination of the darkroom with different scenario of the switching methods in uncertain environment," 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(5), pages 2482-2499, October.

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