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Hybrid genetic and particle swarm algorithm: redundancy allocation problem

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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.

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  1. G. D. Goenka University, Gurugram, Haryana, 122103, India

    Sarita Devi & Deepika Garg

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  1. Sarita Devi
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  2. Deepika Garg
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Correspondence to Deepika Garg.

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Devi, S., Garg, D. Hybrid genetic and particle swarm algorithm: redundancy allocation problem. Int J Syst Assur Eng Manag 11, 313–319 (2020). https://doi.org/10.1007/s13198-019-00858-x

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