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Chaotic Multi-swarm Particle Swarm Optimization for Welded Beam Design Engineering Problem

Year 2022, , 1645 - 1660, 16.12.2022
https://doi.org/10.2339/politeknik.880994

Abstract

Design optimization is an important engineering design activity. In general, design optimization determines the necessary values for the design variables so as to optimize the objective function under certain constraints. Particle swarm optimization algorithm experiences unbalanced between local search and global search. Meeting room approach was introduced as a multi-swarm model to improve the Particle Swarm Optimization algorithm. However, Multiple Swarm Particle Swarm Optimization algorithm may not start with a good position. Therefore, the algorithm may have a slow convergence. This problem can be overcome by using a position created with a chaotic logistics map. Welded Beam Design, which is an engineering problem, mainly aims to minimize the beam cost due to constraints on loading load, shear stress, bending stress and final deflection. The aim of this study is to evaluate the performance of the Chaotic Multiple-swarm Particle Swarm Optimization algorithm in solving this problem. In this context, experimental studies were carried out with different swarm sizes and iteration numbers. According to the results obtained, the Chaotic Multi-swarm Particle Swarm Optimization algorithm offers a good solution compared to other well-known algorithms.

References

  • [1] Gen M., Yun Y., "Soft computing approach for reliability optimization: State-of-the-art survey", Reliab Eng Syst Saf, 91(9): 1008–1026, (2006).
  • [2] Keshtegar B., Hao P., "Enriched self-adjusted performance measure approach for reliability-based design optimization of complex engineering problems", Appl Math Model, 57: 37–51, (2018).
  • [3] Peng F., Ouyang Y., "Optimal clustering of railroad track maintenance jobs", Comput Civ Infrastruct Eng, 29(4): 235–247, (2014).
  • [4] Smith R., Ferrebee E., Ouyang Y., Roesler J., "Optimal Staging Area Locations and Material Recycling Strategies for Sustainable Highway Reconstruction", Comput Civ Infrastruct Eng, 29(8): 559–571, (2014).
  • [5] Luo D., Ibrahim Z., Ismail Z., Xu B., "Optimization of the Geometries of Biconical Tapered Fiber Sensors for Monitoring the Early-Age Curing Temperatures of Concrete Specimens", Comput Civ Infrastruct Eng, 28 (7): 531–541, (2013).
  • [6] Yang X-S., "Firefly algorithm, stochastic test functions and design optimisation", Int J Bio-Inspired Comput, 2(2): 78–84, (2010).
  • [7] Salih S.Q., Alsewari A.A., "Solving large-scale problems using multi-swarm particle swarm approach", Int J Eng Technol, 7(3):1725–1729, (2018).
  • [8] Džugan J., Španiel M., Prantl A., Konopík P., Růžička J., Kuželka J., "Identification of ductile damage parameters for pressure vessel steel", Nucl Eng Des. 328: 372–380, (2018).
  • [9] Towler G., Sinnott R., "Design of Pressure Vessels", Chem. Eng. Des., (Second Edition) Elsevier, 563–629, (2013).
  • [10] Lalbakhsh A., Afzal M.U., Esselle K.P., "Multiobjective particle swarm optimization to design a time-delay equalizer metasurface for an electromagnetic band-gap resonator antenna", IEEE Antennas Wirel Propag Lett, 16: 912–915, (2017).
  • [11] Yang X.S., Karamanoglu M., "Swarm Intelligence and Bio-Inspired Computation: An Overview", Swarm Intelligence and Bio-Inspired Computation Theory and Applications, 3–23, (2013).
  • [12] Mirjalili S., Mirjalili S.M., Lewis A., "Grey Wolf Optimizer", Adv Eng Softw, 69: 46–61, (2014).
  • [13] Yang X.S., Deb S., "Engineering optimisation by cuckoo search", Int J Math Model Numer Optim, 1(4): 330 - 343, (2010).
  • [14] Coello C.A.C., "Treating Constraints As Objectives For Single-Objective Evolutionary Optimization", Eng Optim, 32(3): 275–308, (2000).
  • [15] Ray T., Liew K.M., "Society and civilization: An optimization algorithm based on the simulation of social behavior", IEEE Trans Evol Comput, 7(4): 386–396, (2003).
  • [16] He S., Prempain E., Wu Q.H., "An improved particle swarm optimizer for mechanical design optimization problems", Eng Optim, 36(5): 585–605, (2004).
  • [17] Mezura-Montes E., Coello C.A.C., "An empirical study about the usefulness of evolution strategies to solve constrained optimization problems", Int J Gen Syst, 37(4): 443–473, (2008).
  • [18] Savsani V., "Implementation of modified artificial bee colony (ABC) optimization technique for minimum cost design of welded structures", Int J Simul Multidiscip Des Optim, 5:A11, 1-10, (2014).
  • [19] Mirjalili S., Mirjalili S.M., Hatamlou A., "Multi-Verse Optimizer: a nature-inspired algorithm for global optimization", Neural Comput Appl, 27: 495–513, (2016).
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  • [21] Rini D. P., Shamsuddin S. M., Yuhaniz S. S., "Particle Swarm Optimization: Technique, System and Challenges", International Journal of Computer Applications, 14(1): 19-27, (2011).
  • [22] Van den Bergh F., Engelbrecht A.P., "A new locally convergent particle swarm optimiser", Proc. IEEE Int. Conf. Syst. Man Cybern., Yasmine Hammamet, Tunisia,3, 1-6, (2002).
  • [23] Salih S.Q., Alsewari A.A., Al-Khateeb B., Zolkipli M.F., "Novel multi-swarm approach for balancing exploration and exploitation in particle swarm optimization", Adv. Intell. Syst. Comput., 843: 196–206, (2019).
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  • [26] Gandomi A.H., Yang X.S., "Chaotic bat algorithm", J Comput Sci, 5(2): 224–232, (2014).
  • [27] Murillo-Escobar M.A., Cruz-Hernández C., Cardoza-Avendaño L., Méndez-Ramírez R., "A novel pseudorandom number generator based on pseudorandomly enhanced logistic map", Nonlinear Dyn, 87: 407–425, (2017).
  • [28] Nematollahi A.F., Rahiminejad A., Vahidi B., "A novel physical based meta-heuristic optimization method known as Lightning Attachment Procedure Optimization", Appl Soft Comput J, 59: 596–621, (2017).
  • [29] Ravindran A., Ragsdell K.M., Gintaras V. "Engineering optimization : methods and applications", Wiley, New York, (1983).
  • [30] Smarandache F. Abdel-Basset M. and Chang V. Neutrosophic Operational Research, Eds.: Smarandache, F., Abdel-Basset, M., Chang V., 3, Pons Publishing House, Brussels (2018).

Kaynaklı Kiriş Tasarımı Mühendislik Problemi İçin Kaotik Çoklu-sürü Parçacık Sürü Optimizasyonu

Year 2022, , 1645 - 1660, 16.12.2022
https://doi.org/10.2339/politeknik.880994

Abstract

Tasarım optimizasyonu önemli bir mühendislik tasarım etkinliğidir. Genel olarak, tasarım optimizasyonu belirli kısıtlar altında amaç fonksiyonunu optimize edecek şekilde tasarım değişkenleri için gerekli değerleri belirler. Parçacık sürü optimizasyonu algoritması, yerel arama ve küresel arama arasında dengesizlik yaşar. Toplantı Odası yaklaşımı, Parçacık Sürü Optimizasyon algoritmasını iyileştirmek için çok sürülü bir model olarak öne sürülmüştür. Ancak, Çoklu-sürü Parçacık Sürü Optimizasyonu algoritması iyi bir pozisyondan başlamayabilir. Bu sebeple algoritma yavaş bir yakınsamaya sahip olabilir. Kaotik lojistik haritası ile oluşturulan bir pozisyon kullanılarak bu sorun aşılabilmektedir. Bir mühendislik problemi olan Kaynaklı Kiriş Tasarımı temel olarak, yükleme yükü, kayma gerilmesi, eğilme gerilmesi ve son sapma üzerindeki kısıtlamalara bağlı olarak kiriş maliyetinin en aza indirilmesini amaçlar. Bu çalışmada amaç, bu problemin çözümünde Kaotik çoklu-sürü parçacık sürü optimizasyonu algoritmasının performansını değerlendirmektir. Bu çerçevede, farklı sürü boyutları ve yineleme sayıları ile deneysel çalışmalar gerçekleştirilmiştir. Elde edilen sonuçlara göre, Kaotik Çoklu-sürü Parçacık Sürü Optimizasyonu algoritması diğer iyi bilinen algoritmalara kıyasla iyi bir çözüm sunmuştur.

References

  • [1] Gen M., Yun Y., "Soft computing approach for reliability optimization: State-of-the-art survey", Reliab Eng Syst Saf, 91(9): 1008–1026, (2006).
  • [2] Keshtegar B., Hao P., "Enriched self-adjusted performance measure approach for reliability-based design optimization of complex engineering problems", Appl Math Model, 57: 37–51, (2018).
  • [3] Peng F., Ouyang Y., "Optimal clustering of railroad track maintenance jobs", Comput Civ Infrastruct Eng, 29(4): 235–247, (2014).
  • [4] Smith R., Ferrebee E., Ouyang Y., Roesler J., "Optimal Staging Area Locations and Material Recycling Strategies for Sustainable Highway Reconstruction", Comput Civ Infrastruct Eng, 29(8): 559–571, (2014).
  • [5] Luo D., Ibrahim Z., Ismail Z., Xu B., "Optimization of the Geometries of Biconical Tapered Fiber Sensors for Monitoring the Early-Age Curing Temperatures of Concrete Specimens", Comput Civ Infrastruct Eng, 28 (7): 531–541, (2013).
  • [6] Yang X-S., "Firefly algorithm, stochastic test functions and design optimisation", Int J Bio-Inspired Comput, 2(2): 78–84, (2010).
  • [7] Salih S.Q., Alsewari A.A., "Solving large-scale problems using multi-swarm particle swarm approach", Int J Eng Technol, 7(3):1725–1729, (2018).
  • [8] Džugan J., Španiel M., Prantl A., Konopík P., Růžička J., Kuželka J., "Identification of ductile damage parameters for pressure vessel steel", Nucl Eng Des. 328: 372–380, (2018).
  • [9] Towler G., Sinnott R., "Design of Pressure Vessels", Chem. Eng. Des., (Second Edition) Elsevier, 563–629, (2013).
  • [10] Lalbakhsh A., Afzal M.U., Esselle K.P., "Multiobjective particle swarm optimization to design a time-delay equalizer metasurface for an electromagnetic band-gap resonator antenna", IEEE Antennas Wirel Propag Lett, 16: 912–915, (2017).
  • [11] Yang X.S., Karamanoglu M., "Swarm Intelligence and Bio-Inspired Computation: An Overview", Swarm Intelligence and Bio-Inspired Computation Theory and Applications, 3–23, (2013).
  • [12] Mirjalili S., Mirjalili S.M., Lewis A., "Grey Wolf Optimizer", Adv Eng Softw, 69: 46–61, (2014).
  • [13] Yang X.S., Deb S., "Engineering optimisation by cuckoo search", Int J Math Model Numer Optim, 1(4): 330 - 343, (2010).
  • [14] Coello C.A.C., "Treating Constraints As Objectives For Single-Objective Evolutionary Optimization", Eng Optim, 32(3): 275–308, (2000).
  • [15] Ray T., Liew K.M., "Society and civilization: An optimization algorithm based on the simulation of social behavior", IEEE Trans Evol Comput, 7(4): 386–396, (2003).
  • [16] He S., Prempain E., Wu Q.H., "An improved particle swarm optimizer for mechanical design optimization problems", Eng Optim, 36(5): 585–605, (2004).
  • [17] Mezura-Montes E., Coello C.A.C., "An empirical study about the usefulness of evolution strategies to solve constrained optimization problems", Int J Gen Syst, 37(4): 443–473, (2008).
  • [18] Savsani V., "Implementation of modified artificial bee colony (ABC) optimization technique for minimum cost design of welded structures", Int J Simul Multidiscip Des Optim, 5:A11, 1-10, (2014).
  • [19] Mirjalili S., Mirjalili S.M., Hatamlou A., "Multi-Verse Optimizer: a nature-inspired algorithm for global optimization", Neural Comput Appl, 27: 495–513, (2016).
  • [20] Eberhart R., Kennedy J., "New optimizer using particle swarm theory", Proc. Int. Symp. Micro Mach. Hum. Sci., 39–43, (1995).
  • [21] Rini D. P., Shamsuddin S. M., Yuhaniz S. S., "Particle Swarm Optimization: Technique, System and Challenges", International Journal of Computer Applications, 14(1): 19-27, (2011).
  • [22] Van den Bergh F., Engelbrecht A.P., "A new locally convergent particle swarm optimiser", Proc. IEEE Int. Conf. Syst. Man Cybern., Yasmine Hammamet, Tunisia,3, 1-6, (2002).
  • [23] Salih S.Q., Alsewari A.A., Al-Khateeb B., Zolkipli M.F., "Novel multi-swarm approach for balancing exploration and exploitation in particle swarm optimization", Adv. Intell. Syst. Comput., 843: 196–206, (2019).
  • [24] Poli R., Kennedy J., Blackwell T., "Particle swarm optimization", Swarm Intell, 1:33–57, (2007).
  • [25] Gandomi A.H., Yun G.J., Yang X.S., Talatahari S., "Chaos-enhanced accelerated particle swarm optimization", Commun Nonlinear Sci Numer Simul, 18(2): 327–340, (2013).
  • [26] Gandomi A.H., Yang X.S., "Chaotic bat algorithm", J Comput Sci, 5(2): 224–232, (2014).
  • [27] Murillo-Escobar M.A., Cruz-Hernández C., Cardoza-Avendaño L., Méndez-Ramírez R., "A novel pseudorandom number generator based on pseudorandomly enhanced logistic map", Nonlinear Dyn, 87: 407–425, (2017).
  • [28] Nematollahi A.F., Rahiminejad A., Vahidi B., "A novel physical based meta-heuristic optimization method known as Lightning Attachment Procedure Optimization", Appl Soft Comput J, 59: 596–621, (2017).
  • [29] Ravindran A., Ragsdell K.M., Gintaras V. "Engineering optimization : methods and applications", Wiley, New York, (1983).
  • [30] Smarandache F. Abdel-Basset M. and Chang V. Neutrosophic Operational Research, Eds.: Smarandache, F., Abdel-Basset, M., Chang V., 3, Pons Publishing House, Brussels (2018).
There are 30 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Research Article
Authors

Kemal Akyol 0000-0002-2272-5243

Shahad Odah Feneaker Feneaker 0000-0002-3094-3523

Publication Date December 16, 2022
Submission Date February 15, 2021
Published in Issue Year 2022

Cite

APA Akyol, K., & Feneaker, S. O. F. (2022). Kaynaklı Kiriş Tasarımı Mühendislik Problemi İçin Kaotik Çoklu-sürü Parçacık Sürü Optimizasyonu. Politeknik Dergisi, 25(4), 1645-1660. https://doi.org/10.2339/politeknik.880994
AMA Akyol K, Feneaker SOF. Kaynaklı Kiriş Tasarımı Mühendislik Problemi İçin Kaotik Çoklu-sürü Parçacık Sürü Optimizasyonu. Politeknik Dergisi. December 2022;25(4):1645-1660. doi:10.2339/politeknik.880994
Chicago Akyol, Kemal, and Shahad Odah Feneaker Feneaker. “Kaynaklı Kiriş Tasarımı Mühendislik Problemi İçin Kaotik Çoklu-sürü Parçacık Sürü Optimizasyonu”. Politeknik Dergisi 25, no. 4 (December 2022): 1645-60. https://doi.org/10.2339/politeknik.880994.
EndNote Akyol K, Feneaker SOF (December 1, 2022) Kaynaklı Kiriş Tasarımı Mühendislik Problemi İçin Kaotik Çoklu-sürü Parçacık Sürü Optimizasyonu. Politeknik Dergisi 25 4 1645–1660.
IEEE K. Akyol and S. O. F. Feneaker, “Kaynaklı Kiriş Tasarımı Mühendislik Problemi İçin Kaotik Çoklu-sürü Parçacık Sürü Optimizasyonu”, Politeknik Dergisi, vol. 25, no. 4, pp. 1645–1660, 2022, doi: 10.2339/politeknik.880994.
ISNAD Akyol, Kemal - Feneaker, Shahad Odah Feneaker. “Kaynaklı Kiriş Tasarımı Mühendislik Problemi İçin Kaotik Çoklu-sürü Parçacık Sürü Optimizasyonu”. Politeknik Dergisi 25/4 (December 2022), 1645-1660. https://doi.org/10.2339/politeknik.880994.
JAMA Akyol K, Feneaker SOF. Kaynaklı Kiriş Tasarımı Mühendislik Problemi İçin Kaotik Çoklu-sürü Parçacık Sürü Optimizasyonu. Politeknik Dergisi. 2022;25:1645–1660.
MLA Akyol, Kemal and Shahad Odah Feneaker Feneaker. “Kaynaklı Kiriş Tasarımı Mühendislik Problemi İçin Kaotik Çoklu-sürü Parçacık Sürü Optimizasyonu”. Politeknik Dergisi, vol. 25, no. 4, 2022, pp. 1645-60, doi:10.2339/politeknik.880994.
Vancouver Akyol K, Feneaker SOF. Kaynaklı Kiriş Tasarımı Mühendislik Problemi İçin Kaotik Çoklu-sürü Parçacık Sürü Optimizasyonu. Politeknik Dergisi. 2022;25(4):1645-60.
 
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