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Optimum Design of Compression Spring According to Minimum Volume Using Grey Wolf Optimization Method

Year 2017, Volume: 3 Issue: 2, 21 - 27, 25.08.2017

Abstract

Optimization of machine elements is both an important issue and an intensive study topic in engineering. Design of compression springs according to minimum weight or volume is a sample problem in this area. Various optimization methods such as particle swarm optimization, genetic algorithm are applied to the problem. Grey Wolf optimization (GWO) method, one of the least nature-inspired algorithms, mimics the hunting and leadership hierarchy of grey wolves. The method has attracted attention for a short time due to its successful performance in engineering applications. In this study, GWO was applied to the design of compression springs with minimum volume. The performance of the GWO was compared with the optimization methods used for solving the same problem in previous studies. The results of the study show that the GWO provides very successful results for the design of compression springs with minimum volume.

References

  • [1] Rao, R.V., Savsani, V.J. ve Vakhaira, D.P. (2011).Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer Aided Design, 43, 303-315.
  • [2] Arora, J.S. (2004). Introduction to Optimum Design, Waltham: Elsevier.
  • [3] Coello, C. A. (2002). Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer Methods in Applied Mechanics and Engineering, 191(11–12), 1245–1287.
  • [4] Deb, K. ve Goyal, M. (1997). Optimizing engineering designs using a combined genetic search. In: Seventh International Conference on Genetic Algorithms, Ed. I. T. Back, 512–528.
  • [5] Jayakumar, N. Subramanian, S. Ganesan, S. Ve Elanchezhian, E.B. (2016). Grey wolf optimization for combined heat and power dispatch with cogeneration systems. Electrical Power and Energy Systems, 74, 252-264.
  • [6] Lampinen, J. ve Zelinka, I. (1999) Mixed integer-discrete-continuous optimization by differential evolution. In: Proceedings of the 5th International Conference on Soft Computing, 71–76.
  • [7] Mirjalili, S., Mirjalili, S.M. ve Lewis, A. (2014). Grey Wolf Optimizer, Advances in Engineering Software, 69, 46-61.
  • [8] Mirjalili, S., Saremia, S., Mirjalili, S.M ce Coelho, L.S. (2016). Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization, Expert System with Application, 47, 106-119.
  • [9] S. He., E. Prempain ve Wu, Q. H. (2004). An improved particle swarm optimizer for mechanical design optimization problems. Engineering Optimization, 36 (5), 585-605, DOI: 10.1080/03052150410001704854
  • [10] Sandgren, E. (1990). Nonlinear integer and discrete programming in mechanical design optimization. Journal of Mechanical Design, 112, 223–229.
  • [11] Trabelsi, H., Yvars, P. A., Louati, J ve Haddar, M. (2015). Interval computation and constraint propagation for the optimal design of a compression spring for a linear vehicle suspension system. Mechanism and Machine Theory, 67–89.
  • [12] Yokota, T., Taguchi ve Gen, M. (1997). A solution method for optimal weight design problem of helical spring using genetic algorithms. Computers Ind. Engineering, 33, 71–76.

Bozkurt Optimizasyon Yöntemi Kullanarak Basınç Yaylarının Minimum Hacme Göre Optimum Tasarımı

Year 2017, Volume: 3 Issue: 2, 21 - 27, 25.08.2017

Abstract

Makine elemanlarının optimizasyonu mühendislikte hem önemli bir problem hemde yoğun bir çalışma alanıdır. Basınç yaylarının minimum hacme veya ağırlığa göre tasarımı bu alandaki örnek problemlerden birisidir. Parçacık sürü optimizasyonu, genetik algoritma gibi çeşitli optimizasyon yöntemleri bu probleme uygulanmıştır. Doğadan esinlenen algoritmaların sonuncularından Bozkurt Optimizasyonu (BO) yöntemi, bozkurtların avlanmaları ve liderlik hiyerarşisinden esinlenmiştir. Bu yöntem, mühendislik uygulamalarındaki başarılı performansıyla kısa sürede dikkatleri çekmiştir. Bu çalışmada BO, basınç yaylarının asgari hacme göre tasarımına uygulanmıştır. BO’nun performansı önceki çalışmalarda aynı problemin çözümü için kullanılan optimizasyon yöntemleriyle karşılaştırılmıştır. Çalışmanın sonuçları BO’nun basınç yaylarının asgari hacme göre tasarımında başarılı sonuçlar verdiğini göstermiştir.

References

  • [1] Rao, R.V., Savsani, V.J. ve Vakhaira, D.P. (2011).Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer Aided Design, 43, 303-315.
  • [2] Arora, J.S. (2004). Introduction to Optimum Design, Waltham: Elsevier.
  • [3] Coello, C. A. (2002). Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer Methods in Applied Mechanics and Engineering, 191(11–12), 1245–1287.
  • [4] Deb, K. ve Goyal, M. (1997). Optimizing engineering designs using a combined genetic search. In: Seventh International Conference on Genetic Algorithms, Ed. I. T. Back, 512–528.
  • [5] Jayakumar, N. Subramanian, S. Ganesan, S. Ve Elanchezhian, E.B. (2016). Grey wolf optimization for combined heat and power dispatch with cogeneration systems. Electrical Power and Energy Systems, 74, 252-264.
  • [6] Lampinen, J. ve Zelinka, I. (1999) Mixed integer-discrete-continuous optimization by differential evolution. In: Proceedings of the 5th International Conference on Soft Computing, 71–76.
  • [7] Mirjalili, S., Mirjalili, S.M. ve Lewis, A. (2014). Grey Wolf Optimizer, Advances in Engineering Software, 69, 46-61.
  • [8] Mirjalili, S., Saremia, S., Mirjalili, S.M ce Coelho, L.S. (2016). Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization, Expert System with Application, 47, 106-119.
  • [9] S. He., E. Prempain ve Wu, Q. H. (2004). An improved particle swarm optimizer for mechanical design optimization problems. Engineering Optimization, 36 (5), 585-605, DOI: 10.1080/03052150410001704854
  • [10] Sandgren, E. (1990). Nonlinear integer and discrete programming in mechanical design optimization. Journal of Mechanical Design, 112, 223–229.
  • [11] Trabelsi, H., Yvars, P. A., Louati, J ve Haddar, M. (2015). Interval computation and constraint propagation for the optimal design of a compression spring for a linear vehicle suspension system. Mechanism and Machine Theory, 67–89.
  • [12] Yokota, T., Taguchi ve Gen, M. (1997). A solution method for optimal weight design problem of helical spring using genetic algorithms. Computers Ind. Engineering, 33, 71–76.
There are 12 citations in total.

Details

Journal Section Research Articles
Authors

İsmail Şahin

Murat Dörterler This is me

Harun Gökçe

Publication Date August 25, 2017
Submission Date September 5, 2017
Acceptance Date July 10, 2017
Published in Issue Year 2017 Volume: 3 Issue: 2

Cite

IEEE İ. Şahin, M. Dörterler, and H. Gökçe, “Bozkurt Optimizasyon Yöntemi Kullanarak Basınç Yaylarının Minimum Hacme Göre Optimum Tasarımı”, GJES, vol. 3, no. 2, pp. 21–27, 2017.

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