Araştırma Makalesi
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Yapay Alg Algoritmasının Tasarım Optimizasyon Problemlerindeki Performansı Üzerine Bir Çalışma: Basınç Yayı Örneği

Yıl 2018, Cilt: 11 Sayı: 4, 349 - 355, 30.10.2018

Ümit Atila Murat Dorterler İsmail Şahin

https://doi.org/10.17671/gazibtd.452992

Öz

- Makine elemanlarının
optimum tasarımı mühendislikte yaygın olarak çalışılan bir araştırma konusudur.
Basınç yaylarının minimum ağırlığa veya hacme göre tasarımını bu alanda en çok
çalışılan problemlerden birisidir. Bu problem ayrıca optimizasyon yöntemleri
için değerlendirme problemi olarak kullanılmaktadır. Yapay Alg Algoritması
(YAA)  bir optimizasyon yöntemidir ve besin
üretmek için ihtiyaç duydukları maddelere erişmek üzere ortam şartlarına uyumda
doğal bir yeteneğe sahip alglerin davranışlarından esinlenmiştir. Bu çalışmada,
basınç yaylarının minimum hacme göre tasarımı YAA ile optimize edilmiştir ve YAA’nın
problem üzerindeki başarımı incelenmiştir. YAA’nın başarımı daha önceki
çalışmalarda probleme uygulanmış optimizasyon yöntemleri ile
karşılaştırılmıştır. Deneysel çalışmalar YAA’nın tasarım optimizasyon
problemini tutarlı ve düşük yakınsama oranıyla birlikte başarıyla çözme
yeteneğinin olduğunu göstermiştir. 

Anahtar Kelimeler

Yapay alg algoritması tasarım optimizasy basınç yayı

Kaynakça

  • [1] J. S. Arora, “Introduction to Optimum Design”, Waltham: Elsevier, 2004.
  • [2] R. V. Rao, V. J. Savsani, D. P. Vakhaira, “Teaching–learningbased optimization: A novel method for constrained mechanical design optimization problems”, Computer Aided Design, 43, 303-315, 2011.
  • [3] S. He, E. Prempain, Q. H. Wu, “An improved particle swarm optimizer for mechanical design optimization problems”, Engineering Optimization, 36(5), 585-605, 2004.
  • [4] M. Dörterler, İ. Şahin, H. Gökçe, “A grey wolf optimizer approach for optimal weight design problem of the spur gear”, Engineering Optimization, 1-15, 2018.
  • [5] S. Kirkpatrick, C. D. Gelatt, M. P. Vecchi, “Optimization by simulated annealing”, Science, 220 (4598), 671–680, 1983.
  • [6] E. Bonebeau, M. Dorigo, G. Theraulaz, Swarm intelligence: From natural to artificial systems, Oxford university press, USA, 1999.
  • [7] M. H. Calp, M. A. Akcayol, “Optimization of Project Scheduling Activities in Dynamic CPM and PERT Networks Using Genetic Algorithms”, Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 22, 615-627, 2018.
  • [8] M. Dener, M. H. Calp, “Solving the exam scheduling problems in central exams with genetic algorithms”, Mugla Journal of Science and Technology, 4, 102-115, 2018.
  • [9] J. Kennedy, R. Eberhart, “Particle swarm optimization, in Neural Networks”, Proc. IEEE International Conf. on Neural Networks, Perth, Australia, 1942–1948, 1995.
  • [10] M. Dorigo , M. Birattari, T. Stutzle, “Ant colony optimization”. Comput Intell Magaz.,1, 28–39, 2006.
  • [11] D. Karaboga, B. Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm”, J Glob Optim., 39, 459-471, 2007.
  • [12] I. Fister Jr., X. S. Yang, I. Fister, J. Brest, D. Fister, “A Brief Review of Nature-Inspired Algorithms for Optimization”, Elektrotehniski Vestnik/Electrotechnical Review, 80(3), 1-7, 2013.
  • [13] S. Mirjalili, S. M. Mirjalili, A. Lewis, “Grey Wolf Optimizer”, Advances in Engineering Software, 69, 46-61, 2014.
  • [14] H. Trabelsi, P. A. Yvars, J. Louati, M. Haddar, “Interval computation and constraint propagation for the optimal design of a compression spring for a linear vehicle suspension system”, Mechanism and Machine Theory, 84, 67–89, 2015.
  • [15] T. Yokota, T. Taguchi, M. Gen, “A solution method for optimal weight design problem of helical spring using genetic algorithms”, Computers Ind. Engineering, 33, 71–76, 1997.
  • [16] E. Sandgren, “Nonlinear integer and discrete programming in mechanical design optimization” Journal of Mechanical Design, 112, 223-229, 1990.
  • [17] K. Deb, M. Goyal, “Optimizing engineering designs using a combined genetic”, In Seventh International Conference on Genetic Algorithms, Ed. I. T. Back, Michigan State University, East Lansing, 512–528, 1997.
  • [18] J. Lampinen, I. Zelinka, “Mixed integer-discrete-continuous optimization by differential evolution”. 5th International Mendel Conference on Soft Computing, Czech Rep., 71–76, 1999.
  • [19] İ. Şahin, M. Dörterler, H. Gökçe, “Optimum Design of Compression Spring According to Minimum Volume Using Grey Wolf Optimization Method”, Gazi Journal of Engineering Sciences, 3(2), 21-27, 2017.
  • [20] S. A. Uymaz, G. Tezel, E. Yel, “Artificial Algae Algorithm (AAA) for nonlinear global optimization”, Applied Soft Computing, 31, 153-171, 2015.
  • [21] H. Faris, I. Aljarah, M. A. Al-Betar, S. Mirjalili, “Grey wolf optimizer: a review of recent variants and applications”, Neural Computing and Applications, 20(2), 413-435, 2018

A Study on the Performance of Artificial Alg Algorithm in Design Optimization Problems: Compressing Spring Example

Yıl 2018, Cilt: 11 Sayı: 4, 349 - 355, 30.10.2018

Ümit Atila Murat Dorterler İsmail Şahin

https://doi.org/10.17671/gazibtd.452992

Öz

Optimal design of machine elements is a research field studying in
engineering commonly. Design of compression springs according to minimum weight
or volume is one of the most studied problems in this field. The problem is
also used as a benchmark problem for the optimization methods. Artificial Algae
Algorithm (AAA) is an optimization technique and inspired by the behaviors of
algae, which have natural skill of adaptation to environmental conditions in
order to obtain substances which they need to produce nutrients. In this study,
the design of compression springs with minimum volume was optimized through AAA
and performance of AAA on the problem was examined.  Performance of AAA were compared with the
results of the optimization methods applied to the problem in previous studies.
Experimental results show that AAA is capable of solving the design
optimization problem successively with consistency and low convergence rate.

Anahtar Kelimeler

Artificial Algea Algorithm design optimization compression spring

Kaynakça

  • [1] J. S. Arora, “Introduction to Optimum Design”, Waltham: Elsevier, 2004.
  • [2] R. V. Rao, V. J. Savsani, D. P. Vakhaira, “Teaching–learningbased optimization: A novel method for constrained mechanical design optimization problems”, Computer Aided Design, 43, 303-315, 2011.
  • [3] S. He, E. Prempain, Q. H. Wu, “An improved particle swarm optimizer for mechanical design optimization problems”, Engineering Optimization, 36(5), 585-605, 2004.
  • [4] M. Dörterler, İ. Şahin, H. Gökçe, “A grey wolf optimizer approach for optimal weight design problem of the spur gear”, Engineering Optimization, 1-15, 2018.
  • [5] S. Kirkpatrick, C. D. Gelatt, M. P. Vecchi, “Optimization by simulated annealing”, Science, 220 (4598), 671–680, 1983.
  • [6] E. Bonebeau, M. Dorigo, G. Theraulaz, Swarm intelligence: From natural to artificial systems, Oxford university press, USA, 1999.
  • [7] M. H. Calp, M. A. Akcayol, “Optimization of Project Scheduling Activities in Dynamic CPM and PERT Networks Using Genetic Algorithms”, Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 22, 615-627, 2018.
  • [8] M. Dener, M. H. Calp, “Solving the exam scheduling problems in central exams with genetic algorithms”, Mugla Journal of Science and Technology, 4, 102-115, 2018.
  • [9] J. Kennedy, R. Eberhart, “Particle swarm optimization, in Neural Networks”, Proc. IEEE International Conf. on Neural Networks, Perth, Australia, 1942–1948, 1995.
  • [10] M. Dorigo , M. Birattari, T. Stutzle, “Ant colony optimization”. Comput Intell Magaz.,1, 28–39, 2006.
  • [11] D. Karaboga, B. Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm”, J Glob Optim., 39, 459-471, 2007.
  • [12] I. Fister Jr., X. S. Yang, I. Fister, J. Brest, D. Fister, “A Brief Review of Nature-Inspired Algorithms for Optimization”, Elektrotehniski Vestnik/Electrotechnical Review, 80(3), 1-7, 2013.
  • [13] S. Mirjalili, S. M. Mirjalili, A. Lewis, “Grey Wolf Optimizer”, Advances in Engineering Software, 69, 46-61, 2014.
  • [14] H. Trabelsi, P. A. Yvars, J. Louati, M. Haddar, “Interval computation and constraint propagation for the optimal design of a compression spring for a linear vehicle suspension system”, Mechanism and Machine Theory, 84, 67–89, 2015.
  • [15] T. Yokota, T. Taguchi, M. Gen, “A solution method for optimal weight design problem of helical spring using genetic algorithms”, Computers Ind. Engineering, 33, 71–76, 1997.
  • [16] E. Sandgren, “Nonlinear integer and discrete programming in mechanical design optimization” Journal of Mechanical Design, 112, 223-229, 1990.
  • [17] K. Deb, M. Goyal, “Optimizing engineering designs using a combined genetic”, In Seventh International Conference on Genetic Algorithms, Ed. I. T. Back, Michigan State University, East Lansing, 512–528, 1997.
  • [18] J. Lampinen, I. Zelinka, “Mixed integer-discrete-continuous optimization by differential evolution”. 5th International Mendel Conference on Soft Computing, Czech Rep., 71–76, 1999.
  • [19] İ. Şahin, M. Dörterler, H. Gökçe, “Optimum Design of Compression Spring According to Minimum Volume Using Grey Wolf Optimization Method”, Gazi Journal of Engineering Sciences, 3(2), 21-27, 2017.
  • [20] S. A. Uymaz, G. Tezel, E. Yel, “Artificial Algae Algorithm (AAA) for nonlinear global optimization”, Applied Soft Computing, 31, 153-171, 2015.
  • [21] H. Faris, I. Aljarah, M. A. Al-Betar, S. Mirjalili, “Grey wolf optimizer: a review of recent variants and applications”, Neural Computing and Applications, 20(2), 413-435, 2018
Toplam 21 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilgisayar Yazılımı
Bölüm Makaleler
Yazarlar

Ümit Atila

Murat Dorterler

İsmail Şahin

Yayımlanma Tarihi 30 Ekim 2018
Gönderilme Tarihi 13 Ağustos 2018
Yayımlandığı Sayı Yıl 2018 Cilt: 11 Sayı: 4

Kaynak Göster

APA Atila, Ü., Dorterler, M., & Şahin, İ. (2018). Yapay Alg Algoritmasının Tasarım Optimizasyon Problemlerindeki Performansı Üzerine Bir Çalışma: Basınç Yayı Örneği. Bilişim Teknolojileri Dergisi, 11(4), 349-355. https://doi.org/10.17671/gazibtd.452992
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