Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

RAP via hybrid genetic simulating annealing algorithm

  • Original Article
  • Published:
International Journal of System Assurance Engineering and Management Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

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

References

  • Angeline PJ (1998) Evolutionary optimization versus particle swarm optimization: philosophy and performance differences. In: Proceedings of the 7th annual conference on evolutionary programming, San Diego, USA, pp. N601–N610

  • Bellman RE, Dreyfus E (1962) Applied dynamic programming. Princeton University Press, New Jersey

    Book  Google Scholar 

  • Busacca PG, Marseguerra M, Zio E (2001) Multi-objective optimization by genetic algorithms: application to safety systems. Reliab Eng Syst Safe 72(1):59–74

    Article  Google Scholar 

  • Černý V (1985) Thermodynamical approach to the traveling salesman problem: an efficient simulation algorithm. J Opt Theory Appl 45(1):41–51

    Article  MathSciNet  Google Scholar 

  • Chambari A, Rahmati SHA, Najafi AA, Karimi A (2012) A bi-objective model to optimize reliability and cost of system with a choice of redundancy strategies. Comput Ind Eng 63:109–119

    Article  Google Scholar 

  • Chen J, Zhu W, Ali MM (2011) A hybrid simulated annealing algorithm for nonslicing VLSI floorplanning. IEEE Trans Syst Man Cybern Part C Appl Rev 41(4):544–553

    Article  Google Scholar 

  • Chen A, Jiang T, Chen Z, Zhang Y (2012) Genetic and simulated annealing combined algorithm for optimization of wideband antenna matching networks. Int J Antennas Propag. https://doi.org/10.1155/2012/251624

    Article  Google Scholar 

  • Clerc M (1999) The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. In: Proceedings of the congress on evolutionary computation. Washington D.C., USA, pp. 1951–1957

  • Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evolut Compt 6:58–73

    Article  Google Scholar 

  • D. Tsou D, C. Macnish C (2003) Adaptive particle swarm optimization for high-dimensional highly convex search spaces. In: Proceedings of IEEE congress on evolutionary computation 2003 (CEC 2003). Canbella, Australia, pp. 783–789

  • Davis L (1987) Genetic algorithms and simulated annealing. Morgan Kaufman Publishers Inc, Los Altos

    MATH  Google Scholar 

  • Dengiz B, Altiparmak F, Smith AE (1997) Local search genetic algorithm for optimal design of reliable networks. IEEE Trans Evol Comput 1:179–188

    Article  Google Scholar 

  • Devi S, Garg D (2017) Redundancy-allocation in neel metal products limited. Ind J of Sc and Tech 10(30):1–5

    Article  Google Scholar 

  • Devi S, Sahu A, Garg D (2017) Redundancy optimization problem via comparative analysis of H-PSOCOGA. IEEE Xplore 18–23.

  • Essadqi M, Idrissi A, Amarir A (2018) An effective oriented genetic algorithm for solving redundancy allocation problem in multi-state power system. Proc Comput Sci 127:170–179

    Article  Google Scholar 

  • Fyffe DE, Hines WW, Lee NK (1968) System reliability allocation and a computational algorithm. IEEE Trans Reliab 17:64–69

    Article  Google Scholar 

  • Garg H (2015) A hybrid GA-GSA algorithm for optimizing the performance of an industrial system by utilizing uncertain data. Handb Res Artif Intell Tech Algorithm IGI Global. https://doi.org/10.4018/978-1-4666-7258-1.ch020

    Article  Google Scholar 

  • Garg D, Kumar K (2009) Reliability analysis of pharmaceutical plant using Matlab-tool. Int J Electronic Eng Res 1(2):127–133

    MathSciNet  Google Scholar 

  • Garg D, Kumar K (2010) Availability optimization for screw plant based on genetic algorithm. Int J Eng Sci Technol 2:658–668

    Google Scholar 

  • Garg D, Kumar K, Pahuja GL (2010) Redundancy-allocation in pharmaceutical plant. Int J Eng Sci Technol 2(5):1088–1097

    Google Scholar 

  • Grimaldi EA, Grimacia F, Mussetta M, Pirinoli P, Zich RE (2004) A new hybrid genetical swarm algorithm for electromagnetic optimization. Proc of Int Conf on Comput Electromagnetics and its Applications, Beijing, China 157–160

  • He Q, Wang L (2007) An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Eng Appl Artif Intell 20:89–99

    Article  Google Scholar 

  • He S, Prempain E, Wu QH (2004) An improved particle swarm optimizer for mechanical design optimization problems. Eng Optim 36(5):585–605

    Article  MathSciNet  Google Scholar 

  • Holland JH (1975) Adaption in natural and artificial system. University of Michigan Press, Ann Arbor

    MATH  Google Scholar 

  • Hu X, Eberhart RC (2002) Adaptive particle swarm optimization: detection and response to dynamicsystems. In: Proceedings of congress on evolutionary computation. Hawaii, USA, pp. 1666–1670

  • Jeong S, Hasegawa S, Shimoyama K, Obayashi S (2009) Development and investigation of efficient GA/PSO-hybrid algorithm applicable to real-world design optimization. IEEE Comput Intell Mag 4(3):36–44

    Article  Google Scholar 

  • Jiao B, Lian Z, Gu X (2008) A dynamic inertia weight particle swarm optimization algorithm. Chaos Solitons Fract 37:698–705

    Article  Google Scholar 

  • Kim H, Bae C and Park S (2004) Simulated annealing algorithm for redundancy optimization with multiple component choices. The 2004 Asian International workshop on Advanced Reliability Modelling, Hiroshima, Japan pp. 237–244

  • Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680

    Article  MathSciNet  Google Scholar 

  • Krink T, Lvbjerg M (2002) The lifecycle model: combining particle swarm optimization, genetic algorithms and hill climbers. In: Proceedings of the parallel problem solving from nature, pp. 621–630

  • Kuo W, Prasad VR (2000) An annotated overview of system reliability optimization. IEEE Trans Reliab 49:176–187

    Article  Google Scholar 

  • NakagawaY MS (1981) Surrogate constraints algorithm for reliability optimization problems with two constraints. IEEE Trans Reliab 30:1981

    Google Scholar 

  • Painton L, Campbell J (1994) Identification of components to optimize improvements in system reliability. Proc SRA PSAM-II Conf Syst Based Methods Des Oper Technol Syst Proces 10:10–20

    Google Scholar 

  • Painton L, Campbell J (1995) Genetic algorithms in optimization of system reliability. IEEE Trans Reliab 44:172–178

    Article  Google Scholar 

  • Ratnaweera A, Halgamuge SK, Watson HC (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evolut Comput 8:240–255

    Article  Google Scholar 

  • Sheikhalishahi M, Ebrahimipour V, Shiri H, Zaman H, Jeihoonian M (2013) A hybrid GA-PSO approach for reliability optimization in redundancy allocation problem. Int J Adv Manuf Technol 68:1–22

    Article  Google Scholar 

  • Shi Y, Eberhart R (1998) A modified particle swarm optimizer evolutionary computation. In: Proceedings of the IEEE world congress on computational intelligence. AK, USA, pp. 69–73

  • Shi Y, Eberhart RC (2001) Fuzzy adaptive particle swarm optimization. In: Proceedings of the congress on evolutionary computation. Seoul, Korea,pp. 101–106

  • Tavakkoli-Moghaddama R, SafarJ SF (2008) Reliability optimization of series-parallel systems with a choice of redundancy strategies using a genetic algorithm. Reliab Eng Syst Saf 93:550–556

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Basic and Applied Sciences, GD Goenka University, Gurugram, India

    Deepika Garg & Sarita Devi

Authors
  1. Deepika Garg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sarita Devi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sarita Devi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-021-01081-3

Keywords

Search

Navigation