Hybrid genetic and particle swarm algorithm: redundancy allocation problem

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...

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Bibliographic Details
Published in:International journal of system assurance engineering and management 2020-04, Vol.11 (2), p.313-319
Main Authors: Devi, Sarita, Garg, Deepika
Format: Article
Language:English
Subjects:
Engineering
Engineering Economics
Genetic algorithms
Heuristic
Heuristic methods
Linear programming
Logistics
Marketing
Nonlinear programming
Optimization
Organization
Original Article
Particle swarm optimization
Quality Control
Redundancy
Reliability
Safety and Risk
Scientific papers
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Summary: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.
ISSN:0975-6809
0976-4348
DOI:10.1007/s13198-019-00858-x