Abstract
Ant colony optimization (ACO) algorithm is one
of the artificial intelligence methods used in structural optimization. Values
of some optimization parameters must be determined before the optimization
process in most of the artificial intelligence based optimization algorithms.
Determination of the values of these optimization parameters is essential
especially for the time required for the optimization process and the quality
of results achieved. Pheromone update coefficient, number of ants in the colony,
number of depositing ants, penalty coefficient are the main optimization
parameters in ACO algorithm. This study is focused on the number of ants in the
ant colony. This research is realized using the optimization of grillage
structure which is one of the well-known optimization problems in the
literature. Minimization of the weight of structure is the objective function
of the optimization problem, and the member size of grillages are considered as
discrete design variables. Displacement and strength limitations are considered
as constraints according to manual of LRFD-AISC. A computer program is coded in
BASIC to accomplish the structural design and optimization procedures.
Numerical examples from literature are optimized using different number of ants
to determine the effect of the number of ants on the optimization process. At
the end of the study, some inferences are presented on the number of ants to be
used in the colony.
References
- Aydın, Z. (2016) Size Optimization of Grillage Structures Using a Simplified Ant Colony Optimization Algorithm, 12th International Congress on Advances in Civil Engineering, ACE2016, September 21-23, Istanbul, Turkey.
- Aydın, Z. and Yılmaz, A.H. (2014) A comparison of two heuristic search methods via a structural optimization problem: ant colony optimization and genetic algorithm, 11th International Congress on Advances in Civil Engineering, ACE2014, October 21-25, Istanbul, Turkey.
- Aydoğdu, İ., Akın, A. and Saka, M.P. (2016) Design optimization of real World steel space frames using artificial bee colony algorithm with Levy flight distribution, Advances in Engineering Software, 92, 1-14. doi:10.1016/j.advengsoft.2015.10.013
- Aydoğdu, İ. and Saka, M.P. (2012) Ant colony optimization of irregular steel frames including elemental warping effect, Advances in Engineering Software, 44, 150-169. doi:10.1016/j.advengsoft.2011.05.029
- Camp, C.V. and Bichon B.J. (2004) Design of space trusses using ant colony optimization, Journal of structural Engineering, 130(5), 741-751. doi:10.1061/(ASCE)0733-9445(2004)130:5(741)
- Çarbaş, S. (2016) Design optimization of steel frames using an enhanced firefly algorithm, Engineering Optimization, 48(12), 2007-2025. doi:10.1080/0305215X.2016.1145217
- Çarbaş, S., Aydoğdu, İ., Saka, M.P. (2013) A Comparative Study of Three Metaheuristics for Optimum Design of Engineering Structures, The 10th World Congress on Structural and Multidisciplinary Optimization, Orlando, Florida, USA, May 19-24.
- Daloğlu, A.T., Artar, M., Özgan, K. and Karakas, A.İ. (2016) Optimum design of steel space frames including soil-structure interaction, Structural Multidisciplinary Optimization, 54, 117-131. doi:10.1007/s00158-016-1401-x
- Dede, T. (2013) Optimum design of grillage structures to LRFD-AISC with teaching-learning based optimization, Structural Multidisciplinary Optimization, 48, 955-964. doi:10.1007/s00158-013-0936-3
- Dorigo, M. (1992), Optimization, Learning and Natural Algorithms, PhD thesis, Dip. Elettronica e Informazione, Politecnico di Milano, Italy.
- Farshchin, M., Camp, C.V. and Maniat, M. (2016) Multi-class teaching–learning-based optimization for truss design with frequency constraints, Engineering Structures, 106, 355-369. doi:10.1016/j.engstruct.2015.10.039
- Hasançebi, O., Çarbaş, S., Saka, M.P. (2011) A reformulation of the ant colony optimization algorithm for large scale structural optimization, Proceedings of the Second International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering, Civil-Comp Press.
- Kaveh, A., Ghafari, M.H. and Gholipour, Y. (2017) Optimum seismic design of steel frames considering the connection types, Journal of Constructional Steel Research, 130, 79-87. doi:10.1016/j.jcsr.2016.12.002
- Kaveh, A. and Talatahari, S. (2010) Charged system search for optimum grillage design using the LRFD-AISC code, Journal of Constructional Steel Research, 66, 767-771. doi:10.1016/j.jcsr.2010.01.007
- Kaveh, A. and Talatahari, S. (2012) A hybrid CSS and PSO algorithm for optimal design of structures, Structural Engineering and Mechanics, 42(6), 783-797. doi:10.12989/sem.2012.42.6.783
- LRFD-AISC Manual of Steel Construction (1999) Load and resistance factor design, Metric conversion of the second edition, vols 1 & 2. AISC, Chicago.
- Mashayekhi, M., Salajegheh, E. and Dehghani, M. (2016) Topology optimization of double and triple layer grid structures using a modified gravitational harmony search algorithm with efficient member grouping strategy, Computers and Structures, 172, 40-58. doi:10.1016/j.compstruc.2016.05.008
- Moezi, S.A., Zakeri, E., Zare, A. and Nedaei, M. (2015) On the application of modified cuckoo optimization algorithm to the crack detection problem of cantilever Euler–Bernoulli beam, Computers and Structures, 157, 42-50. doi:10.1016/j.compstruc.2015.05.008
- Rajeev, S. and Krishnamoorthy, C.S. (1992) Discrete optimization of structures using genetic algorithm, Jornal of Structural Engineering, 118(5), 1233-1250. doi:10.1061/(ASCE)0733-9445(1992)118:5(1233)
- Saka, M.P., Daloglu, A. and Malhas, F. (2000) Optimum spacing design of grillage systems using a genetic algorithm, Advances in Engineering Software, 31, 863-873. doi:10.1016/S0965-9978(00)00048-X
- Saka, M.P. and Erdal, F. (2009) Harmony search based algorithm for the optimum design of grillage systems to LRFD-AISC, Structural Multidisciplinary Optimization, 38, 25-41. doi:10.1007/s00158-008-0263-2
- Tort, C., Şahin, S. and Hasançebi, O. (2017) Optimum design of steel lattice transmission line towers using simulated annealing and PLS-TOWER, Computers and Structures, 179, 75-94. doi:10.1016/j.compstruc.2016.10.017
IZGARA SİSTEMLERİN OPTİMİZASYONU ÜZERİNDEN KARINCA KOLONİ OPTİMİZASYON ALGORİTMASINDA KARINCA SAYISININ BELİRLENMESİ
Abstract
Karınca koloni optimizasyon algoritması, yapısal optimizasyonda
kullanılan yapay zekaya dayalı yöntemlerden biridir. Yapay zekaya dayalı
optimizasyon algoritmalarının çoğunda bazı optimizasyon parametrelerinin
değerleri optimizasyon sürecinin öncesinde belirlenmesi gerekmektedir. Bu
optimizasyon parametrelerinin değerlerinin belirlenmesi özellikle
optimizasyonun işlemi için gerekli süre ve ulaşılan sonuçların niteliği
açısından önemlidir. Feromon güncelleme katsayısı, kolonideki karınca sayısı,
feromon bırakacak karınca sayısı, ceza katsayısı karınca koloni
algoritmasındaki başlıca optimizasyon parametreleridir. Bu çalışma ise
kolonideki karınca sayısına odaklanmaktadır. Bu araştırma, literatürde sıkça
ele alınan optimizasyon problemlerinden biri olan, ızgara sistemlerin
optimizasyonu üzerinden gerçekleştirilmiştir. Yapı ağırlığının minimum
değerinin belirlenmesi optimizasyon probleminin amaç fonksiyonu ve ızgara
sitemin oluşturan elemanların enkesit ebatları ise ayrık tasarım değişkenleri
olarak dikkate alınmıştır. Yerdeğiştirme ve dayanım limitleri “LRFD-AISC”
yönetmeliğine göre sınırlayıcılar olarak alınmıştır. Yapısal tasarım ve
optimizasyon süreci için gerekli işlemleri yapmak üzere “BASIC" dilinde
bir bilgisayar programı kodlanmıştır. Karınca sayısının optimizasyon süreci
üzerindeki etkisini belirlemek için literatürden seçilen sayısal örnekler
farklı karınca sayıları kullanılarak optimize edilmiştir. Çalışmanın sonucunda,
kolonide kullanılması gereken karınca sayısına ilişkin bazı çıkarımlar
sunulmuştur.
References
- Aydın, Z. (2016) Size Optimization of Grillage Structures Using a Simplified Ant Colony Optimization Algorithm, 12th International Congress on Advances in Civil Engineering, ACE2016, September 21-23, Istanbul, Turkey.
- Aydın, Z. and Yılmaz, A.H. (2014) A comparison of two heuristic search methods via a structural optimization problem: ant colony optimization and genetic algorithm, 11th International Congress on Advances in Civil Engineering, ACE2014, October 21-25, Istanbul, Turkey.
- Aydoğdu, İ., Akın, A. and Saka, M.P. (2016) Design optimization of real World steel space frames using artificial bee colony algorithm with Levy flight distribution, Advances in Engineering Software, 92, 1-14. doi:10.1016/j.advengsoft.2015.10.013
- Aydoğdu, İ. and Saka, M.P. (2012) Ant colony optimization of irregular steel frames including elemental warping effect, Advances in Engineering Software, 44, 150-169. doi:10.1016/j.advengsoft.2011.05.029
- Camp, C.V. and Bichon B.J. (2004) Design of space trusses using ant colony optimization, Journal of structural Engineering, 130(5), 741-751. doi:10.1061/(ASCE)0733-9445(2004)130:5(741)
- Çarbaş, S. (2016) Design optimization of steel frames using an enhanced firefly algorithm, Engineering Optimization, 48(12), 2007-2025. doi:10.1080/0305215X.2016.1145217
- Çarbaş, S., Aydoğdu, İ., Saka, M.P. (2013) A Comparative Study of Three Metaheuristics for Optimum Design of Engineering Structures, The 10th World Congress on Structural and Multidisciplinary Optimization, Orlando, Florida, USA, May 19-24.
- Daloğlu, A.T., Artar, M., Özgan, K. and Karakas, A.İ. (2016) Optimum design of steel space frames including soil-structure interaction, Structural Multidisciplinary Optimization, 54, 117-131. doi:10.1007/s00158-016-1401-x
- Dede, T. (2013) Optimum design of grillage structures to LRFD-AISC with teaching-learning based optimization, Structural Multidisciplinary Optimization, 48, 955-964. doi:10.1007/s00158-013-0936-3
- Dorigo, M. (1992), Optimization, Learning and Natural Algorithms, PhD thesis, Dip. Elettronica e Informazione, Politecnico di Milano, Italy.
- Farshchin, M., Camp, C.V. and Maniat, M. (2016) Multi-class teaching–learning-based optimization for truss design with frequency constraints, Engineering Structures, 106, 355-369. doi:10.1016/j.engstruct.2015.10.039
- Hasançebi, O., Çarbaş, S., Saka, M.P. (2011) A reformulation of the ant colony optimization algorithm for large scale structural optimization, Proceedings of the Second International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering, Civil-Comp Press.
- Kaveh, A., Ghafari, M.H. and Gholipour, Y. (2017) Optimum seismic design of steel frames considering the connection types, Journal of Constructional Steel Research, 130, 79-87. doi:10.1016/j.jcsr.2016.12.002
- Kaveh, A. and Talatahari, S. (2010) Charged system search for optimum grillage design using the LRFD-AISC code, Journal of Constructional Steel Research, 66, 767-771. doi:10.1016/j.jcsr.2010.01.007
- Kaveh, A. and Talatahari, S. (2012) A hybrid CSS and PSO algorithm for optimal design of structures, Structural Engineering and Mechanics, 42(6), 783-797. doi:10.12989/sem.2012.42.6.783
- LRFD-AISC Manual of Steel Construction (1999) Load and resistance factor design, Metric conversion of the second edition, vols 1 & 2. AISC, Chicago.
- Mashayekhi, M., Salajegheh, E. and Dehghani, M. (2016) Topology optimization of double and triple layer grid structures using a modified gravitational harmony search algorithm with efficient member grouping strategy, Computers and Structures, 172, 40-58. doi:10.1016/j.compstruc.2016.05.008
- Moezi, S.A., Zakeri, E., Zare, A. and Nedaei, M. (2015) On the application of modified cuckoo optimization algorithm to the crack detection problem of cantilever Euler–Bernoulli beam, Computers and Structures, 157, 42-50. doi:10.1016/j.compstruc.2015.05.008
- Rajeev, S. and Krishnamoorthy, C.S. (1992) Discrete optimization of structures using genetic algorithm, Jornal of Structural Engineering, 118(5), 1233-1250. doi:10.1061/(ASCE)0733-9445(1992)118:5(1233)
- Saka, M.P., Daloglu, A. and Malhas, F. (2000) Optimum spacing design of grillage systems using a genetic algorithm, Advances in Engineering Software, 31, 863-873. doi:10.1016/S0965-9978(00)00048-X
- Saka, M.P. and Erdal, F. (2009) Harmony search based algorithm for the optimum design of grillage systems to LRFD-AISC, Structural Multidisciplinary Optimization, 38, 25-41. doi:10.1007/s00158-008-0263-2
- Tort, C., Şahin, S. and Hasançebi, O. (2017) Optimum design of steel lattice transmission line towers using simulated annealing and PLS-TOWER, Computers and Structures, 179, 75-94. doi:10.1016/j.compstruc.2016.10.017
Details
| Subjects | Engineering |
|---|---|
| Journal Section | Research Articles |
| Authors | |
| Publication Date | December 1, 2017 |
| Submission Date | March 17, 2017 |
| Acceptance Date | December 27, 2017 |
| Published in Issue | Year 2017 Volume: 22 Issue: 3 |
Cite
Cited By
Examination of parameters used in ant colony algorithm over truss optimization
Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi
https://doi.org/10.25092/baunfbed.955408
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