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RAP via hybrid genetic simulating annealing algorithm

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Abstract

This paper aims to solve Redundancy allocation problem (RAP). It is a significant complex optimization and non-linear integer programming problem of reliability engineering. RAP includes the choices of components and the suitable amount of redundant subsystems for maximizing reliability of the system under given restrictions like cost, weight, volume etc. It is difficult to solve non-linear complex problems. In this paper, the RAP is solved by the combination of genetic and simulating algorithm that is called Hybrid Genetic Simulating Annealing Algorithm (HGSAA). It can be observed that superiority of both the algorithms are combined and form an adequate algorithm which ignores the individual weakness. Comparative analysis of HGSAA with existing methods such as Heuristic Algorith, Constraint Optimization Genetic Algorithm, Hybrid Particle Swarm Optimization and Constraint Optimization Genetic Algorithm are presented in this study. RAP is also solved by Branch and Bound method to validate the result of HGSAA. The developed algorithm is programmed by Matlab.

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Abbreviations

\(a_{i}\) :

ith component

\(R_{i} \left( {a_{i} } \right)\) :

Reliability of ai

\(Q_{i} \left( {a_{i} } \right)\) :

Unreliability of component-ai

\(R_{s} \left( a \right)\) :

Complete system reliability

\(\rm m_i\) :

Redundancy in ith subunits

\(h_{i} \left( {a_{i} } \right)\) :

jth resource exhausted by ith component

\(m = 7\) :

Overall units

C:

Total cost

\(K(.)\) :

A function which estimate the reliability of overall system

\(COGA_{num}\) :

No. of solutions at the time of execution of iteration in COGA

\(COGA_{PS}\) :

Population size of the particles in COGA

\(COGA_{\max lter}\) :

Upper iteration limit in PSO

\(PSO_{i}\) :

PSO iteration in ongoing execution

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Correspondence to Sarita Devi.

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Garg, D., Devi, S. RAP via hybrid genetic simulating annealing algorithm. Int J Syst Assur Eng Manag 12, 419–425 (2021). https://doi.org/10.1007/s13198-021-01081-3

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