Araştırma Makalesi
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Comparative Weight and Cost Optımızation of Constraıned Engineering Problems

Yıl 2021, Cilt: 62 Sayı: 705, 784 - 805, 08.12.2021
https://doi.org/10.46399/muhendismakina.1034211

Öz

Real-world problems in engineering are often nonlinear or constrained design problems. For many reasons, an engineer wants to get the best design, not just any that works properly. The process of determining the best design is called optimization. With optimization, the best design of the problem is determined to achieve a specific objective function by providing the current constraints. In this study, three real-world engineering design problems are tried to be optimized, namely tension/compression spring, welded beam, and pressure vessel designs with various equalities and inequality constraints. In the optimization process, eight different algorithms are used, the best designs are created, and the optimum variables of the problems are determined. Optimization algorithms are selected from evolutionary-based, swarm-based, mathematics-based, and physics-based algorithms, which are sub-branches of metaheuristic algorithms. In addition, the results of the algorithms are compared with each other with the help of convergence curves and box graphs. The grey wolf algorithm is the algorithm that showed the most successful performance in all three problems. Besides, swarm-based, physics-based, and math-based algorithms performed better than other algorithms in optimizing real engineering problems.

Kaynakça

  • Wolpert, D.H., W.G. Macready, 1997. “No free lunch theorems for optimization,” IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, p.67–82.
  • Dogan, E., A.O. Ciftcioglu, F. Erdal, 2017. Optimum design of cellular beams via bat algorithm with levy flights, In OPTIMA-2017 Conf., .
  • Erdal, F., E. Dogan, M.P. Saka, 2011. “Optimum design of cellular beams using harmony search and particle swarm optimizers,” Journal of Constructional Steel Research, vol. 67, no. 2, p.237–247.
  • Khalilpourazari, S., H. Hashemi Doulabi, A. Özyüksel Çiftçioğlu, G.W. Weber, 2021. “Gradient-based grey wolf optimizer with Gaussian walk: Application in modelling and prediction of the COVID-19 pandemic,” Expert Systems with Applications, vol. 177, .
  • Khalilpourazari, S., B. Naderi, S. Khalilpourazary, 2020. “Multi-Objective Stochastic Fractal Search: a powerful algorithm for solving complex multi-objective optimization problems,” Soft Computing, vol. 24, no. 4, p.3037–3066.
  • Khalilpourazari, S., S. Khalilpourazary, 2019. “An efficient hybrid algorithm based on Water Cycle and Moth-Flame Optimization algorithms for solving numerical and constrained engineering optimization problems,” Soft Computing, vol. 23, no. 5, p.1699–1722.
  • Khalilpourazari, S., S. Khalilpourazary, 2018. “SCWOA: an efficient hybrid algorithm for parameter optimization of multi-pass milling process,” Journal of Industrial and Production Engineering, vol. 35, no. 3, p.135–147.
  • Khalilpourazari, S., S. Khalilpourazary, 2016. “Optimization of production time in the multi-pass milling process via a Robust Grey Wolf Optimizer,” Neural Computing and Applications, vol. 29, no. 12, p.1321–1336.
  • Rather, S.A., N. Sharma, 2018. “Gsa-Bbo Hybridization Algorithm,” no. December,.
  • Mirjalili, S., S.M. Mirjalili, A. Lewis, 2014. “Grey Wolf Optimizer,” Advances in Engineering Software, vol. 69, p.46–61.
  • Dogan, E. 2010. Optimum design of rigid and semi-rigid steel sway frames including soil-structure interaction. PhD Thesis, Middle East Technical University, Ankara.
  • Kennedy, J., R. Eberhart, 1995. Particle Swarm Optimization, In IEEE Int. Conf. Neural Networks, IEEE Press, pp: 1942–1948.
  • Mirjalili, S., A.H. Gandomi, S.Z. Mirjalili, S. Saremi, H. Faris, S.M. Mirjalili, 2017. “Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems,” Advances in Engineering Software, vol. 114, p.163–191.
  • Dorigo, M., M. Birattari, T. Stützle, 2006. “Ant colony optimization: artificial ants as a computational intelligence technique,” IEEE Computational Intelligence Magazine, vol. 1, p.28–39.
  • Mirjalili, S. 2015. “Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm,” Knowledge-Based Systems, vol. 89, p.228–249.
  • Yang, X.S. 2010. A New Metaheuristic Bat-Inspired Algorithm, In Nat. Inspired Coop. Strateg. Optim., Springer, pp: 65–74.
  • Rashedi, E., H. Nezamabadi-pour, S. Saryazdi, 2009. “GSA: A Gravitational Search Algorithm,” Information Sciences, vol. 179, no. 13, p.2232–2248.
  • Erol, O.K., I. Eksin, 2006. “A new optimization method: Big Bang–Big Crunch,” Advances in Engineering Software, vol. 37, no. 2, p.106–111.
  • Mirjalili, S. 2016. “SCA: A Sine Cosine Algorithm for solving optimization problems,” Knowledge-Based Systems, vol. 96, p.120–133.
  • Salimi, H. 2015. “Stochastic Fractal Search: A powerful metaheuristic algorithm,” Knowledge-Based Systems, vol. 75, p.1–18.
  • Bonabeau, E., M. Dorigo, G. Theraulaz, 1999. Swarm intelligence: from natural to artificial systems: OUP.
  • Storn, R., K. Price, 1997. “Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces,” Journal of Global Optimization, vol. 11, no. 4, p.341–359.
  • Ma, H., D. Simon, 2010. Biogeography-Based Optimization with Blended Migration for Constrained Optimization Problems, In Proc. 12th Annu. Conf. Genet. Evol. Comput., Association for Computing Machinery, pp: 417–418.
  • Güler, T., M. Kılıç, 2019. “Klimatik Kontrollü Treyler İçerisindeki Hava Akışının Optimizasyonu Optimization of Air Flow in Refrigerated Semi-Trailer,” vol. 60, no. 697, p.289–302. 25. Tekelioğlu, S., S. Eldek, H. Gümüş, A. Sarıgül, Ş. Ayhan, 2020. “Hidrolik Yüksek Basınç Hattı Filtre Gövdesinin Tasarımı , Optimizasyonu , Üretimi ve Test Edilmesi Design , Optimization and Fabrication of Body of Hydraulic High- Pressure Filter and Experimental Validation,” p.0–2.
  • Alinaghian, M., E.B. Tirkolaee, Z.K. Dezaki, S.R. Hejazi, W. Ding, 2021. “An augmented Tabu search algorithm for the green inventory-routing problem with time windows,” Swarm and Evolutionary Computation, vol. 60, no. November 2020, p.100802.
  • Tirkolaee, E.B., A. Mardani, Z. Dashtian, M. Soltani, G.W. Weber, 2020. “A novel hybrid method using fuzzy decision making and multi-objective programming for sustainable-reliable supplier selection in two-echelon supply chain design,” Journal of Cleaner Production, vol. 250, p.119517.
  • Meneghini, I.R., M.A. Alves, A. Gaspar-Cunha, F.G. Guimarães, 2020. “Scalable and customizable benchmark problems for many-objective optimization,” Applied Soft Computing Journal, vol. 90, p.106139.
  • Varelas, K., O.A. El Hara, D. Brockhoff, N. Hansen, D.M. Nguyen, T. Tušar, A. Auger, 2020. “Benchmarking large-scale continuous optimizers: The bbob-largescale testbed, a COCO software guide and beyond,” Applied Soft Computing Journal, vol. 97, p.106737.
  • Shabani, A., B. Asgarian, M. Salido, S. Asil Gharebaghi, 2020. “Search and rescue optimization algorithm: A new optimization method for solving constrained engineering optimization problems,” Expert Systems with Applications, vol. 161, p.113698.
  • Muthusamy, H., S. Ravindran, S. Yaacob, K. Polat, 2021. “An improved elephant herding optimization using sine–cosine mechanism and opposition based learning for global optimization problems,” Expert Systems with Applications, vol. 172, no. October 2020, p.114607.
  • Gucuyen, E., R.T. Erdem, 2014. “Corrosion effects on structural behaviour of jacket type offshore structures,” Gradjevinar, vol. 66, no. 11, p.981–986.
  • Dagli, B.Y., Y. Tuskan, D. Uncu, 2019. “Investigation of fluid-structure interaction by using solidity ratio,” Eurasian Journal of Civil Engineering and Architecture, vol. 3, no. 2, p.41–47.
  • Rather, S.A., P.S. Bala, 2020. “Hybridization of Constriction Coefficient Based Particle Swarm Optimization and Gravitational Search Algorithm for Function Optimization,” SSRN Electronic Journal, p.1–10.
  • Kanwal, S., A. Hussain, K. Huang, 2021. “Novel Artificial Immune Networks-based optimization of shallow machine learning (ML) classifiers,” Expert Systems with Applications, vol. 165, no. September 2019, p.113834.
  • Jin, X.-B., G.-S. Xie, K. Huang, A. Hussain, 2018. “Accelerating Infinite Ensemble of Clustering by Pivot Features,” Cogn. Comput., vol. 10, no. 6, p.1042–1050.
  • Lee, K.S., Z.W. Geem, 2005. “A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice,” Computer Methods in Applied Mechanics and Engineering, vol. 194, no. 36–38, p.3902–3933.
  • Deb, K. 2000. “An Efficient Constraint Handling Method for Genetic Algorithms,” Computer Methods in Applied Mechanics and Engineering, vol. 186, p.311–338.
  • He, Q., L. Wang, 2007. “An effective co-evolutionary particle swarm optimization for constrained engineering design problems,” Engineering Applications of Artificial Intelligence, vol. 20, no. 1, p.89–99.
  • Mahdavi, M., M. Fesanghary, E. Damangir, 2007. “An improved harmony search algorithm for solving optimization problems,” Applied Mathematics and Computation, vol. 188, no. 2, p.1567–1579.
  • Mezura-Montes, E., C.A.C. Coello, 2008. “An empirical study about the usefulness of evolution strategies to solve constrained optimization problems,” International Journal of General Systems, vol. 37, no. 4, p.443–473.

Kısıtlı Mühendislik Problemlerinin Karşılaştırmalı Ağırlık ve Maliyet Optimizasyonu

Yıl 2021, Cilt: 62 Sayı: 705, 784 - 805, 08.12.2021
https://doi.org/10.46399/muhendismakina.1034211

Öz

Mühendislik alanındaki gerçek dünya problemleri genellikle doğrusal olmayan veya kısıtlı tasarım problemleridir. Pek çok nedenden ötürü, bir mühendis yalnızca uygun şekilde çalışan herhangi bir tasarımı değil, en iyi tasarımı elde etmek ister. En iyi tasarımı belirleme sürecine optimizasyon denir. Optimizasyon ile mevcut kısıtlayıcıları sağlayarak belirli bir amaç fonksiyonunu elde edecek şekilde problemin en iyi tasarımı belirlenir. Bu çalışmada çeşitli eşitlik ve eşitsizlik kısıtlamaları olan çekme/basınç yayı, kaynaklı kiriş ve basınçlı kap tasarımları olmak üzere üç gerçek dünya mühendislik tasarım problemi optimize edilmeye çalışılmış, tasarım problemlerinin optimum değişkenleri belirlenmiştir. Optimizasyon sürecinde sekiz farklı algoritma kullanılmış, gerçek mühendislik problemlerine ait en iyi tasarımlar oluşturulmaya çalışılmıştır. Optimizasyon algoritmaları, meta-sezgisel algoritmaların alt dallarından olan evrimsel tabanlı, sürü tabanlı, matematik tabanlı ve fizik tabanlı algoritmalardan seçilmiştir. Bunların yanı sıra, algoritmaların sonuçları yakınsama eğrileri ve kutu grafikler yardımıyla birbirleri ile kıyaslanmıştır. Gri kurt algoritması her üç problemde de en başarılı performans gösteren algoritma olmuştur. Bunun yanı sıra, sürü tabanlı, fizik tabanlı ve matematik tabanlı algoritmalar gerçek mühendislik problemlerini optimize etmede diğer algoritmalardan daha iyi sonuç vermiştir.

Kaynakça

  • Wolpert, D.H., W.G. Macready, 1997. “No free lunch theorems for optimization,” IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, p.67–82.
  • Dogan, E., A.O. Ciftcioglu, F. Erdal, 2017. Optimum design of cellular beams via bat algorithm with levy flights, In OPTIMA-2017 Conf., .
  • Erdal, F., E. Dogan, M.P. Saka, 2011. “Optimum design of cellular beams using harmony search and particle swarm optimizers,” Journal of Constructional Steel Research, vol. 67, no. 2, p.237–247.
  • Khalilpourazari, S., H. Hashemi Doulabi, A. Özyüksel Çiftçioğlu, G.W. Weber, 2021. “Gradient-based grey wolf optimizer with Gaussian walk: Application in modelling and prediction of the COVID-19 pandemic,” Expert Systems with Applications, vol. 177, .
  • Khalilpourazari, S., B. Naderi, S. Khalilpourazary, 2020. “Multi-Objective Stochastic Fractal Search: a powerful algorithm for solving complex multi-objective optimization problems,” Soft Computing, vol. 24, no. 4, p.3037–3066.
  • Khalilpourazari, S., S. Khalilpourazary, 2019. “An efficient hybrid algorithm based on Water Cycle and Moth-Flame Optimization algorithms for solving numerical and constrained engineering optimization problems,” Soft Computing, vol. 23, no. 5, p.1699–1722.
  • Khalilpourazari, S., S. Khalilpourazary, 2018. “SCWOA: an efficient hybrid algorithm for parameter optimization of multi-pass milling process,” Journal of Industrial and Production Engineering, vol. 35, no. 3, p.135–147.
  • Khalilpourazari, S., S. Khalilpourazary, 2016. “Optimization of production time in the multi-pass milling process via a Robust Grey Wolf Optimizer,” Neural Computing and Applications, vol. 29, no. 12, p.1321–1336.
  • Rather, S.A., N. Sharma, 2018. “Gsa-Bbo Hybridization Algorithm,” no. December,.
  • Mirjalili, S., S.M. Mirjalili, A. Lewis, 2014. “Grey Wolf Optimizer,” Advances in Engineering Software, vol. 69, p.46–61.
  • Dogan, E. 2010. Optimum design of rigid and semi-rigid steel sway frames including soil-structure interaction. PhD Thesis, Middle East Technical University, Ankara.
  • Kennedy, J., R. Eberhart, 1995. Particle Swarm Optimization, In IEEE Int. Conf. Neural Networks, IEEE Press, pp: 1942–1948.
  • Mirjalili, S., A.H. Gandomi, S.Z. Mirjalili, S. Saremi, H. Faris, S.M. Mirjalili, 2017. “Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems,” Advances in Engineering Software, vol. 114, p.163–191.
  • Dorigo, M., M. Birattari, T. Stützle, 2006. “Ant colony optimization: artificial ants as a computational intelligence technique,” IEEE Computational Intelligence Magazine, vol. 1, p.28–39.
  • Mirjalili, S. 2015. “Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm,” Knowledge-Based Systems, vol. 89, p.228–249.
  • Yang, X.S. 2010. A New Metaheuristic Bat-Inspired Algorithm, In Nat. Inspired Coop. Strateg. Optim., Springer, pp: 65–74.
  • Rashedi, E., H. Nezamabadi-pour, S. Saryazdi, 2009. “GSA: A Gravitational Search Algorithm,” Information Sciences, vol. 179, no. 13, p.2232–2248.
  • Erol, O.K., I. Eksin, 2006. “A new optimization method: Big Bang–Big Crunch,” Advances in Engineering Software, vol. 37, no. 2, p.106–111.
  • Mirjalili, S. 2016. “SCA: A Sine Cosine Algorithm for solving optimization problems,” Knowledge-Based Systems, vol. 96, p.120–133.
  • Salimi, H. 2015. “Stochastic Fractal Search: A powerful metaheuristic algorithm,” Knowledge-Based Systems, vol. 75, p.1–18.
  • Bonabeau, E., M. Dorigo, G. Theraulaz, 1999. Swarm intelligence: from natural to artificial systems: OUP.
  • Storn, R., K. Price, 1997. “Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces,” Journal of Global Optimization, vol. 11, no. 4, p.341–359.
  • Ma, H., D. Simon, 2010. Biogeography-Based Optimization with Blended Migration for Constrained Optimization Problems, In Proc. 12th Annu. Conf. Genet. Evol. Comput., Association for Computing Machinery, pp: 417–418.
  • Güler, T., M. Kılıç, 2019. “Klimatik Kontrollü Treyler İçerisindeki Hava Akışının Optimizasyonu Optimization of Air Flow in Refrigerated Semi-Trailer,” vol. 60, no. 697, p.289–302. 25. Tekelioğlu, S., S. Eldek, H. Gümüş, A. Sarıgül, Ş. Ayhan, 2020. “Hidrolik Yüksek Basınç Hattı Filtre Gövdesinin Tasarımı , Optimizasyonu , Üretimi ve Test Edilmesi Design , Optimization and Fabrication of Body of Hydraulic High- Pressure Filter and Experimental Validation,” p.0–2.
  • Alinaghian, M., E.B. Tirkolaee, Z.K. Dezaki, S.R. Hejazi, W. Ding, 2021. “An augmented Tabu search algorithm for the green inventory-routing problem with time windows,” Swarm and Evolutionary Computation, vol. 60, no. November 2020, p.100802.
  • Tirkolaee, E.B., A. Mardani, Z. Dashtian, M. Soltani, G.W. Weber, 2020. “A novel hybrid method using fuzzy decision making and multi-objective programming for sustainable-reliable supplier selection in two-echelon supply chain design,” Journal of Cleaner Production, vol. 250, p.119517.
  • Meneghini, I.R., M.A. Alves, A. Gaspar-Cunha, F.G. Guimarães, 2020. “Scalable and customizable benchmark problems for many-objective optimization,” Applied Soft Computing Journal, vol. 90, p.106139.
  • Varelas, K., O.A. El Hara, D. Brockhoff, N. Hansen, D.M. Nguyen, T. Tušar, A. Auger, 2020. “Benchmarking large-scale continuous optimizers: The bbob-largescale testbed, a COCO software guide and beyond,” Applied Soft Computing Journal, vol. 97, p.106737.
  • Shabani, A., B. Asgarian, M. Salido, S. Asil Gharebaghi, 2020. “Search and rescue optimization algorithm: A new optimization method for solving constrained engineering optimization problems,” Expert Systems with Applications, vol. 161, p.113698.
  • Muthusamy, H., S. Ravindran, S. Yaacob, K. Polat, 2021. “An improved elephant herding optimization using sine–cosine mechanism and opposition based learning for global optimization problems,” Expert Systems with Applications, vol. 172, no. October 2020, p.114607.
  • Gucuyen, E., R.T. Erdem, 2014. “Corrosion effects on structural behaviour of jacket type offshore structures,” Gradjevinar, vol. 66, no. 11, p.981–986.
  • Dagli, B.Y., Y. Tuskan, D. Uncu, 2019. “Investigation of fluid-structure interaction by using solidity ratio,” Eurasian Journal of Civil Engineering and Architecture, vol. 3, no. 2, p.41–47.
  • Rather, S.A., P.S. Bala, 2020. “Hybridization of Constriction Coefficient Based Particle Swarm Optimization and Gravitational Search Algorithm for Function Optimization,” SSRN Electronic Journal, p.1–10.
  • Kanwal, S., A. Hussain, K. Huang, 2021. “Novel Artificial Immune Networks-based optimization of shallow machine learning (ML) classifiers,” Expert Systems with Applications, vol. 165, no. September 2019, p.113834.
  • Jin, X.-B., G.-S. Xie, K. Huang, A. Hussain, 2018. “Accelerating Infinite Ensemble of Clustering by Pivot Features,” Cogn. Comput., vol. 10, no. 6, p.1042–1050.
  • Lee, K.S., Z.W. Geem, 2005. “A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice,” Computer Methods in Applied Mechanics and Engineering, vol. 194, no. 36–38, p.3902–3933.
  • Deb, K. 2000. “An Efficient Constraint Handling Method for Genetic Algorithms,” Computer Methods in Applied Mechanics and Engineering, vol. 186, p.311–338.
  • He, Q., L. Wang, 2007. “An effective co-evolutionary particle swarm optimization for constrained engineering design problems,” Engineering Applications of Artificial Intelligence, vol. 20, no. 1, p.89–99.
  • Mahdavi, M., M. Fesanghary, E. Damangir, 2007. “An improved harmony search algorithm for solving optimization problems,” Applied Mathematics and Computation, vol. 188, no. 2, p.1567–1579.
  • Mezura-Montes, E., C.A.C. Coello, 2008. “An empirical study about the usefulness of evolution strategies to solve constrained optimization problems,” International Journal of General Systems, vol. 37, no. 4, p.443–473.
Toplam 40 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Aybike Özyüksel Çiftçioğlu 0000-0003-4424-7622

Yayımlanma Tarihi 8 Aralık 2021
Gönderilme Tarihi 27 Temmuz 2021
Kabul Tarihi 30 Ağustos 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 62 Sayı: 705

Kaynak Göster

APA Özyüksel Çiftçioğlu, A. (2021). Kısıtlı Mühendislik Problemlerinin Karşılaştırmalı Ağırlık ve Maliyet Optimizasyonu. Mühendis Ve Makina, 62(705), 784-805. https://doi.org/10.46399/muhendismakina.1034211

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ISSN : 1300-3402

E-ISSN : 2667-7520