Skip to main content
SpringerLink
Log in

Hybrid Evolutionary Algorithm for Optimizing Reliability of Complex Systems

  • Conference paper
  • First Online:
Intelligent Systems Design and Applications (ISDA 2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 941))

  • 1070 Accesses

Abstract

In this paper, we propose a hybrid optimization algorithm of Harmony Search and Differential applied to three reliability complex system with static, extinctive constraint treatment. The proposed hybrid is contrasted with Harmony Search, Improved Modified Harmony Search, Differential Evolution, Modified Differential Evolution and other algorithms previous employed for Reliability of Complex Systems in the literature. We experimentally found that the proposed hybrid i.e. Improved Modified Harmony Search + Modified Differential Evolution needs less function evaluations as to the contrasted algorithms.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bhat, T.R., Venkataramani, D., Ravi, V., Murty, C.V.S.: An improved differential evolution method for efficient parameter estimation in biofilter modeling. Biochem. Eng. J. 28(2), 167–176 (2006)

    Article  Google Scholar 

  2. Chauhan, N., Ravi, V.: Differential evolution and threshold accepting hybrid algorithm for unconstrained optimisation. Int. J. Bio-Inspired Comput. 2(3/4), 169 (2010)

    Article  Google Scholar 

  3. Chen, X., Ong, Y.S., Lim, M.H., Tan, K.C.: A multi-facet survey on memetic computation. IEEE Trans. Evol. Comput. 15(5), 591–607 (2011)

    Article  Google Scholar 

  4. Choudhuri, R., Ravi, V.: A hybrid harmony search and modified great deluge algorithm for unconstrained optimisation. Int. J. Comput. Intell. Res. 6(4), 755–761 (2010)

    Google Scholar 

  5. Colorni, A., Dorigo, M., Maniezzo, V.: Distributed optimization by ant colonies. In: Proceedings of the European Conference on Artificial Life, pp. 134–142 (1991)

    Google Scholar 

  6. Das, S., Mukhopadhyay, A., Roy, A., Abraham, A., Panigrahi, B.K.: Exploratory power of the harmony search algorithm: analysis and improvements for global numerical optimization. IEEE Trans. Syst. Man Cybern. Part B 41(1), 89–106 (2011)

    Article  Google Scholar 

  7. Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. Part B 26(1), 29–41 (1996)

    Article  Google Scholar 

  8. Dueck, G., Scheurer, T.: Threshold accepting: a general purpose optimization algorithm. J. Comput. Phys. 90, 161–175 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  9. Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simulation 76(2), 60–68 (2001)

    Article  Google Scholar 

  10. Gendreau, M., Potvin, J.Y.: Handbook of Metaheuristics. Springer, Heidelberg (2010)

    Google Scholar 

  11. Glover, F.: Tabu search - part II. ORSA J. Comput. 2(1), 4–32 (1989)

    Article  MATH  Google Scholar 

  12. Glover, F.: Tabu search - part I. ORSA J. Comput. 1(3), 190–206 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  13. Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Longman Publishing Co., Boston (1989)

    MATH  Google Scholar 

  14. Horst, R., Pardalos, P.M.: Handbook of Global Optimization. Kluwer Academic Publishers (1995)

    Google Scholar 

  15. Jaya Krishna, G., Vadlamani, R., Nagesh, B.S.: Key generation for plain text in stream cipher via bi-objective evolutionary computing. Appl. Soft Comput. 70, 17 (2018)

    Article  Google Scholar 

  16. Kaveh, A., Talatahari, S.: Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures. Comput. Struct. 87(5–6), 267–283 (2009)

    Article  Google Scholar 

  17. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: International Conference on Neural Networks (ICNN 1995), Piscataway, NJ, pp. 1942–1948. IEEE (1995)

    Google Scholar 

  18. Kim, J.H., Lee, H.M., Jung, D., Sadollah, A.: Performance measures of metaheuristic algorithms (2016)

    Google Scholar 

  19. Kirkpatrick, S., Jtr, C.G., Vecchi, M.: Optimization by simulated annealing (1994)

    Google Scholar 

  20. Jaya Krishna, G., Ravi, V.: Modified harmony search applied to reliability optimization of complex systems. In: Kim, J., Geem, Z. (eds.) Advances in Intelligent Systems and Computing, pp. 169–180. Springer, Berlin, Heidelberg (2015)

    Google Scholar 

  21. Jaya Krishna, G., Ravi, V.: Outlier detection using evolutionary computing. In: Proceedings of the International Conference on Informatics and Analytics – ICIA 2016, pp. 1–6. ACM Press, New York (2016)

    Google Scholar 

  22. Li, H., Li, L.: A novel hybrid particle swarm optimization algorithm combined with harmony search for high dimensional optimization problems. In: The 2007 International Conference on Intelligent Pervasive Computing (IPC 2007), Jeju City, South Korea, pp. 94–97. IEEE (2007)

    Google Scholar 

  23. Maheshkumar, Y., Ravi, V., Abraham, A.: A particle swarm optimization-threshold accepting hybrid algorithm for unconstrained optimization. Neural Netw. World 23(3), 191–221 (2013)

    Article  Google Scholar 

  24. Maheshkumar, Y., Ravi, V.: A modified harmony search threshold accepting hybrid optimization algorithm. In: Sombattheera, C., et al. (eds.) Multi-disciplinary Trends in Artificial Intelligence (MIWAI), pp. 298–308. Springer, Hyderabad (2011)

    Chapter  Google Scholar 

  25. Mohan, C., Shanker, K.: Reliability optimization of complex systems using random search technique. Microelectron. Reliab. 28(4), 513–518 (1988)

    Article  Google Scholar 

  26. Ong, Y.S., Lim, M., Chen, X.: Memetic computation—past, present and future research frontier. IEEE Comput. Intell. Mag. 5(2), 24–31 (2010)

    Article  Google Scholar 

  27. Ravi, V., Reddy, P.J., Zimmermann, H.J.: Fuzzy global optimization of complex system reliability. IEEE Trans. Fuzzy Syst. 8(3), 241–248 (2000)

    Article  Google Scholar 

  28. Ravi, V., Murty, B.S.N., Reddy, J.: Nonequilibrium simulated-annealing algorithm applied to reliability optimization of complex systems. IEEE Trans. Reliab. 46(2), 233–239 (1997)

    Article  Google Scholar 

  29. Ravi, V.: Optimization of complex system reliability by a modified great deluge algorithm. Asia-Pacific J. Oper. Res. 21(04), 487–497 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  30. Sharma, N., Arun, N., Ravi, V.: An ant colony optimisation and Nelder-Mead simplex hybrid algorithm for training neural networks: an application to bankruptcy prediction in banks. Int. J. Inf. Decis. Sci. 5(2), 188 (2013)

    Google Scholar 

  31. Shelokar, P.S., Jayaraman, V.K., Kulkarni, B.D.: Ant algorithm for single and multiobjective reliability optimization problems. Qual. Reliab. Eng. Int. 18(6), 497–514 (2002)

    Article  Google Scholar 

  32. Srinivas, M., Rangaiah, G.P.: Differential evolution with tabu list for global optimization and its application to phase equilibrium and parameter estimation problems. Ind. Eng. Chem. Res. 46(10), 3410–3421 (2007)

    Article  Google Scholar 

  33. Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  34. Tillman, F.A., Hwang, C.L., Kuo, W.: Optimization of Systems Reliability. Marcel Dekker, New York (1980)

    MATH  Google Scholar 

  35. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Center of Excellence in Analytics, Institute for Development and Research in Banking Technology, Hyderabad, 500057, India

    Gutha Jaya Krishna & Vadlamani Ravi

  2. School of Computer and Information Sciences, University of Hyderabad, Hyderabad, 500046, India

    Gutha Jaya Krishna

Authors
  1. Gutha Jaya Krishna
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vadlamani Ravi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vadlamani Ravi .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Aswani Kumar Cherukuri

  3. Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  4. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jaya Krishna, G., Ravi, V. (2020). Hybrid Evolutionary Algorithm for Optimizing Reliability of Complex Systems. In: Abraham, A., Cherukuri, A., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 941. Springer, Cham. https://doi.org/10.1007/978-3-030-16660-1_11

Download citation

Publish with us

Policies and ethics

Search

Navigation