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Parameter Analysis of Bacterial Foraging Optimization Algorithm for Least Weight Design of Truss Structures

Year 2019, Volume: 23 Issue: 2, 300 - 314, 25.08.2019
https://doi.org/10.19113/sdufenbed.548654

Abstract

The problem of obtaining the least weight design of truss structures with a fixed topology is an optimization where the cross-sectional areas are determined. In order to solve the optimization problem, bacterial foraging optimization algorithm which is one of the swarm-based methods is chosen. In this work it is studied that which parameters should be chosen for successful application of the algorithm in the solution of the least weight design of truss structures. The parameters of the algorithm are collected in dual groups and their effects are investigated. Additionally, it is recommended to change the step length parameter, which greatly influences the results depending on the reproduction numbers rather than using a constant value from the beginning to end. At the end, the appropriate parameters are determined for least weight truss design problems. Since swarm-based optimization methods start at random points, the results obtained at the end of each run differs. The proximity of the results to be obtained is an indicator of the stability of the algorithm. It is seen that the coefficient of variation of the analysis results using the parameters obtained at the end of the study was below 0.7%. The bacterial foraging optimization algorithm parameters obtained in this study show that these parameters can be used for least weight truss structure design.

References

  • [1] Dorn, W. S., 1964. Automatic design of optimal structures, Journal de mecanique 3 25–52.
  • [2] Rajeev, S., Krishnamoorthy, C. S, 1997. Genetic Algorithms-Based Methodologies for Design Optimization of Trusses, Journal of Structural Engineering, 123 (3)
  • [3] Dorigo, M., Di Caro, G., 1999. Ant colony optimization: a new meta-heuristic, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99, 6-9 July, Washington, 1470–1477.
  • [4] Kaveh, A., Talatahari, S., 2009. A particle swarm ant colony optimization for truss structures with discrete variables, Journal of Constructional Steel Research, 65 (8-9), 1558–1568.
  • [5] Geem, Z.W., Kim, J. H., Loganathan, G., 2001. A New Heuristic Optimization Algorithm: Harmony Search, SIMULATION, 76 (2), 60–68
  • [6] Glover, F., 1990. Tabu Search - Part I, ORSA Journal on Computing, 2 (1)
  • [7] Glover, F., 1990. Tabu Search—Part II, ORSA Journal on Computing, 2 (1)
  • [8] Bennage, W. A., Dhingra, A. K., 1995. Optimization of truss topology using tabu search, International Journal for Numerical Methods in Engineering, 38 (23), 4035–4052
  • [9] Kennedy, J., Eberhart, R., 1995. Particle swarm optimization, International Conference on Neural Networks, 27 Nov.-1 Dec., Perth, 1942–1948
  • [10] Schutte, J. F., Groenwold, A. A., 2003. Sizing design of truss structures using particle swarms, Structural and Multidisciplinary Optimization, 25 (4), 261–269
  • [11] Karaboga, D., 2005. An idea based on Honey Bee Swarm for Numerical Optimization, Technical Report TR06, Erciyes University
  • [12] Sonmez, M., 2011. Artificial Bee Colony algorithm for optimization of truss structures, Applied Soft Computing 11 (2), 2406–2418.
  • [13] Sonmez, M., 2011. Discrete optimum design of truss structures using artificial bee colony algorithm, Structural and Multidisciplinary Optimization 43 (1), 85–97.
  • [14] Passino, K. M., 2002. Biomimicry of bacterial foraging for distributed optimization and control, Control Systems, IEEE, 22 (3), 52–67
  • [15] Devi, S., Geethanjali, M., 2014. Application of Modified Bacterial Foraging Optimization algorithm for optimal placement and sizing of Distributed Generation, Expert Systems with Applications 41 (6), 2772–2781
  • [16] Niu, B., Wang, H., Wang, J., Tan, L., 2013. Multi-objective bacterial foraging optimization, Neurocomputing, 116, 336–345.
  • [17] Sathya, P. D., Kayalvizhi, R., 2011. Modified bacterial foraging algorithm based multilevel thresholding for image segmentation, Engineering Applications of Artificial Intelligence, 24 (4), 595–615
  • [18] Majhi. R., Panda, G., Majhi, B., Sahoo, G., 2009. Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques, Expert Systems with Applications, 36 (6), 10097–10104
  • [19] S. Hezer, Y. Kara, 2014, Eşzamanlı dağıtımlı ve toplamalı araç rotalama problemlerinin çözümü için bakteriyel besin arama optimizasyonu tabanlı bir algoritma, Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 28 (2), 373–382
  • [20] Zhao, W., Wang, L., 2016. An effective bacterial foraging optimizer for global optimization, Information Sciences, 329, 719–735.
  • [21] Biswas, A., Das, S., Abraham, A., Dasgupta, S., 2010. Stability analysis of the reproduction operator in bacterial foraging optimization, Theoretical Computer Science, 411 (21), 2127–2139
  • [22] Chen, H., Niu, B., Ma, L., Su, W., Zhu, Y., 2014. Bacterial colony foraging optimization, Neurocomputing, 137, 268–284.
  • [23] Karaboga, D., Basturk, B., 2007. Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization, 12th International Fuzzy Systems Association World Congress, June 18-21, Mexico, 789–798
  • [24] Kaveh, A., Bakhshpoori, T., 2013. Optimum Design of Space Trusses Using Cuckoo Search Algorithm With Levy Flights, IJST, Transactions of Civil Engineering, 37 (C1), 1–15.
  • [25] Cheng, M. Y., Prayogo, D., 2014. Symbiotic Organisms Search: A new metaheuristic optimization algorithm, Computers and Structures, 139, 98–112
  • [26] Cuevas, E., Cienfuegos, M., 2014. A new algorithm inspired in the behavior of the social-spider for constrained optimization, Expert Systems with Applications, 41 (2), 412–425.
  • [27] Deb, K., 2000. An efficient constraint handling method for genetic algorithms, Computer Methods in Applied Mechanics and Engineering, 186 (2-4), 311–338.
  • [28] Parpinelli, R. S., Teodoro, F. R., Lopes, H. S., 2012. A comparison of swarm intelligence algorithms for structural engineering optimization, International Journal for Numerical Methods in Engineering, 91 (6), 666–684
  • [29] Lee, K. S., Geem, Z. W., 2004. A new structural optimization method based on the harmony search algorithm, Computers & Structures, 82 (9-10), 781–798
  • [30] Li, L. J., Huang, Z. B., Liu, F., Wu, Q. H., 2007. A heuristic particle swarm optimizer for optimization of pin connected structures, Computers and Structures,85 (7-8), 340–349
  • [31] Farshi, B., Alinia-Ziazi, A., 2010. Sizing optimization of truss structures by method of centers and force formulation, International Journal of Solids and Structures, 47 (18-19), 2508–2524
  • [32] Aslani, M., Ghasemi, P., Gandomi, A. H., 2018. Constrained mean-variance mapping optimization for truss optimization problems, Structural Design of Tall and Special Buildings, 27 6), 1–17
  • [33] Camp, C. V., 2007. Design of Space Trusses Using Big Bang–Big Crunch Optimization, Journal of Structural Engineering, 133 (7), 999–1008
  • [34] Lamberti, L.. 2008. An efficient simulated annealing algorithm for design optimization of truss structures, Computers & Structures, 86 (19-20), 1936–1953
  • [35] Dede, T., Bekirolu, S., Ayvaz, Y., 2011. Weight minimization of trusses with genetic algorithm, in: Applied Soft Computing Journal, 11, 2565–2575

En Hafif Kafes Yapı Tasarımı için Bakteri Yiyecek Arama Optimizasyon Algoritmasının Parametre Analizi

Year 2019, Volume: 23 Issue: 2, 300 - 314, 25.08.2019
https://doi.org/10.19113/sdufenbed.548654

Abstract

 Topolojisi belirli kafes yapıların en hafif tasarımının elde edilmesi problemi kesit alanlarının belirlenmesine yönelik bir optimizasyon problemidir. Optimizasyon probleminin çözümünde  sürü tabanlı yöntemlerden olan bakteri yiyecek arama optimizasyon algoritması tercih edilmiştir. Bu algoritmanın en hafif kafes yapı tasarımı problemlerinin çözümünde başarı ile kullanması için seçilmesi gereken parametrelerin neler olması gerektiği üzerine çalışılmıştır. Algoritmanın parametreleri ikili gruplar halinde değiştirilerek sonuca etkileri araştırılmıştır. Ek olarak algoritmadan alınacak sonuca büyük oranda etki eden adım uzunluğu parametresinin seçiminde  baştan sona sabit bir değer kulanılması yerine üreme sayılarına bağlı olarak değiştirilmesi önerilmektedir. Elde edilen bulgular sonunda en hafif kafes tasarımı problemleri için uygun parametreler belirlenmiştir. Sürü tabanlı optimizasyon yöntemleri rastgele noktalardan başladıklarından her çalıştırma sonunda elde edilen sonuçlar da farklılık göstermektedir. Elde edilecek sonuçların birbirine olan yakınlığı algoritmanın kararlılığının bir göstergesidir. Çalışma sonunda ortaya çıkan parametreler kullanılarak üç örnek problem üzerinde yapılan analiz sonuçlarının varyasyon katsayılarının %0.7'nin altında olduğu görülmüştür. Bu çalışmada elde edilen bakteri yiyecek arama optimizasyon algoritması parametrelerinin en hafif kafes yapı tasarımı problemlerinde kullanılabilir olduğunu göstermektedir.

References

  • [1] Dorn, W. S., 1964. Automatic design of optimal structures, Journal de mecanique 3 25–52.
  • [2] Rajeev, S., Krishnamoorthy, C. S, 1997. Genetic Algorithms-Based Methodologies for Design Optimization of Trusses, Journal of Structural Engineering, 123 (3)
  • [3] Dorigo, M., Di Caro, G., 1999. Ant colony optimization: a new meta-heuristic, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99, 6-9 July, Washington, 1470–1477.
  • [4] Kaveh, A., Talatahari, S., 2009. A particle swarm ant colony optimization for truss structures with discrete variables, Journal of Constructional Steel Research, 65 (8-9), 1558–1568.
  • [5] Geem, Z.W., Kim, J. H., Loganathan, G., 2001. A New Heuristic Optimization Algorithm: Harmony Search, SIMULATION, 76 (2), 60–68
  • [6] Glover, F., 1990. Tabu Search - Part I, ORSA Journal on Computing, 2 (1)
  • [7] Glover, F., 1990. Tabu Search—Part II, ORSA Journal on Computing, 2 (1)
  • [8] Bennage, W. A., Dhingra, A. K., 1995. Optimization of truss topology using tabu search, International Journal for Numerical Methods in Engineering, 38 (23), 4035–4052
  • [9] Kennedy, J., Eberhart, R., 1995. Particle swarm optimization, International Conference on Neural Networks, 27 Nov.-1 Dec., Perth, 1942–1948
  • [10] Schutte, J. F., Groenwold, A. A., 2003. Sizing design of truss structures using particle swarms, Structural and Multidisciplinary Optimization, 25 (4), 261–269
  • [11] Karaboga, D., 2005. An idea based on Honey Bee Swarm for Numerical Optimization, Technical Report TR06, Erciyes University
  • [12] Sonmez, M., 2011. Artificial Bee Colony algorithm for optimization of truss structures, Applied Soft Computing 11 (2), 2406–2418.
  • [13] Sonmez, M., 2011. Discrete optimum design of truss structures using artificial bee colony algorithm, Structural and Multidisciplinary Optimization 43 (1), 85–97.
  • [14] Passino, K. M., 2002. Biomimicry of bacterial foraging for distributed optimization and control, Control Systems, IEEE, 22 (3), 52–67
  • [15] Devi, S., Geethanjali, M., 2014. Application of Modified Bacterial Foraging Optimization algorithm for optimal placement and sizing of Distributed Generation, Expert Systems with Applications 41 (6), 2772–2781
  • [16] Niu, B., Wang, H., Wang, J., Tan, L., 2013. Multi-objective bacterial foraging optimization, Neurocomputing, 116, 336–345.
  • [17] Sathya, P. D., Kayalvizhi, R., 2011. Modified bacterial foraging algorithm based multilevel thresholding for image segmentation, Engineering Applications of Artificial Intelligence, 24 (4), 595–615
  • [18] Majhi. R., Panda, G., Majhi, B., Sahoo, G., 2009. Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques, Expert Systems with Applications, 36 (6), 10097–10104
  • [19] S. Hezer, Y. Kara, 2014, Eşzamanlı dağıtımlı ve toplamalı araç rotalama problemlerinin çözümü için bakteriyel besin arama optimizasyonu tabanlı bir algoritma, Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 28 (2), 373–382
  • [20] Zhao, W., Wang, L., 2016. An effective bacterial foraging optimizer for global optimization, Information Sciences, 329, 719–735.
  • [21] Biswas, A., Das, S., Abraham, A., Dasgupta, S., 2010. Stability analysis of the reproduction operator in bacterial foraging optimization, Theoretical Computer Science, 411 (21), 2127–2139
  • [22] Chen, H., Niu, B., Ma, L., Su, W., Zhu, Y., 2014. Bacterial colony foraging optimization, Neurocomputing, 137, 268–284.
  • [23] Karaboga, D., Basturk, B., 2007. Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization, 12th International Fuzzy Systems Association World Congress, June 18-21, Mexico, 789–798
  • [24] Kaveh, A., Bakhshpoori, T., 2013. Optimum Design of Space Trusses Using Cuckoo Search Algorithm With Levy Flights, IJST, Transactions of Civil Engineering, 37 (C1), 1–15.
  • [25] Cheng, M. Y., Prayogo, D., 2014. Symbiotic Organisms Search: A new metaheuristic optimization algorithm, Computers and Structures, 139, 98–112
  • [26] Cuevas, E., Cienfuegos, M., 2014. A new algorithm inspired in the behavior of the social-spider for constrained optimization, Expert Systems with Applications, 41 (2), 412–425.
  • [27] Deb, K., 2000. An efficient constraint handling method for genetic algorithms, Computer Methods in Applied Mechanics and Engineering, 186 (2-4), 311–338.
  • [28] Parpinelli, R. S., Teodoro, F. R., Lopes, H. S., 2012. A comparison of swarm intelligence algorithms for structural engineering optimization, International Journal for Numerical Methods in Engineering, 91 (6), 666–684
  • [29] Lee, K. S., Geem, Z. W., 2004. A new structural optimization method based on the harmony search algorithm, Computers & Structures, 82 (9-10), 781–798
  • [30] Li, L. J., Huang, Z. B., Liu, F., Wu, Q. H., 2007. A heuristic particle swarm optimizer for optimization of pin connected structures, Computers and Structures,85 (7-8), 340–349
  • [31] Farshi, B., Alinia-Ziazi, A., 2010. Sizing optimization of truss structures by method of centers and force formulation, International Journal of Solids and Structures, 47 (18-19), 2508–2524
  • [32] Aslani, M., Ghasemi, P., Gandomi, A. H., 2018. Constrained mean-variance mapping optimization for truss optimization problems, Structural Design of Tall and Special Buildings, 27 6), 1–17
  • [33] Camp, C. V., 2007. Design of Space Trusses Using Big Bang–Big Crunch Optimization, Journal of Structural Engineering, 133 (7), 999–1008
  • [34] Lamberti, L.. 2008. An efficient simulated annealing algorithm for design optimization of truss structures, Computers & Structures, 86 (19-20), 1936–1953
  • [35] Dede, T., Bekirolu, S., Ayvaz, Y., 2011. Weight minimization of trusses with genetic algorithm, in: Applied Soft Computing Journal, 11, 2565–2575
There are 35 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Burak Kaymak 0000-0002-1318-0456

Publication Date August 25, 2019
Published in Issue Year 2019 Volume: 23 Issue: 2

Cite

APA Kaymak, B. (2019). En Hafif Kafes Yapı Tasarımı için Bakteri Yiyecek Arama Optimizasyon Algoritmasının Parametre Analizi. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 23(2), 300-314. https://doi.org/10.19113/sdufenbed.548654
AMA Kaymak B. En Hafif Kafes Yapı Tasarımı için Bakteri Yiyecek Arama Optimizasyon Algoritmasının Parametre Analizi. SDÜ Fen Bil Enst Der. August 2019;23(2):300-314. doi:10.19113/sdufenbed.548654
Chicago Kaymak, Burak. “En Hafif Kafes Yapı Tasarımı için Bakteri Yiyecek Arama Optimizasyon Algoritmasının Parametre Analizi”. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 23, no. 2 (August 2019): 300-314. https://doi.org/10.19113/sdufenbed.548654.
EndNote Kaymak B (August 1, 2019) En Hafif Kafes Yapı Tasarımı için Bakteri Yiyecek Arama Optimizasyon Algoritmasının Parametre Analizi. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 23 2 300–314.
IEEE B. Kaymak, “En Hafif Kafes Yapı Tasarımı için Bakteri Yiyecek Arama Optimizasyon Algoritmasının Parametre Analizi”, SDÜ Fen Bil Enst Der, vol. 23, no. 2, pp. 300–314, 2019, doi: 10.19113/sdufenbed.548654.
ISNAD Kaymak, Burak. “En Hafif Kafes Yapı Tasarımı için Bakteri Yiyecek Arama Optimizasyon Algoritmasının Parametre Analizi”. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi 23/2 (August 2019), 300-314. https://doi.org/10.19113/sdufenbed.548654.
JAMA Kaymak B. En Hafif Kafes Yapı Tasarımı için Bakteri Yiyecek Arama Optimizasyon Algoritmasının Parametre Analizi. SDÜ Fen Bil Enst Der. 2019;23:300–314.
MLA Kaymak, Burak. “En Hafif Kafes Yapı Tasarımı için Bakteri Yiyecek Arama Optimizasyon Algoritmasının Parametre Analizi”. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 23, no. 2, 2019, pp. 300-14, doi:10.19113/sdufenbed.548654.
Vancouver Kaymak B. En Hafif Kafes Yapı Tasarımı için Bakteri Yiyecek Arama Optimizasyon Algoritmasının Parametre Analizi. SDÜ Fen Bil Enst Der. 2019;23(2):300-14.

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