Araştırma Makalesi
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Karınca koloni algoritmasında kullanılan parametrelerin kafes sistem optimizasyonu üzerinden irdelenmesi

Yıl 2022, , 263 - 280, 05.01.2022
https://doi.org/10.25092/baunfbed.955408

Öz

Meta-sezgisel optimizasyon teknikleri son 30 yıldır giderek artan bir hızla mühendislik problemlerinin çözümünde kullanılmaktadır. Bu algoritmaların çoğu doğadaki bir süreci taklit ederek geliştirilmiştir. Bu çalışmada karıncaların doğal yaşamını taklit ederek geliştirilmiş karınca koloni algoritması ele alınmıştır. Diğer meta-sezgisel algoritmalarda olduğu gibi karınca koloni algoritması da bir optimizasyonu gerçekleştirebilmek için bir takım parametrelere ihtiyaç duymaktadır. Bu çalışmanın amacı karınca koloni algoritmasında kullanılan parametrelerin değerlerinin sonuçlara etkisini irdelemektir. Bu amaç doğrultusunda örnek problem olarak, literatürde çok sık ele alınan sınırlayıcılı problemlerden biri olan kafes sistemlerin optimizasyonu üzerinden çalışma gerçekleştirilmiştir. Çalışmada kodlanan bir bilgisayar programı ile karınca sayısı, feromon güncelleme katsayısı ve ceza katsayısı gibi optimum tasarım parametrelerinin uygun değerleri araştırılmıştır. Çalışma sonucunda ilgili parametrelerin sonuca etkisi belirlenmiş ve bu parametrelerin seçiminde dikkat edilecek hususlar belirtilmiştir.

Kaynakça

  • Yılmaz A. H., Düzlem çelik kafes sistemlerin karınca kolonisi yöntemi ile optimum tasarımı, Y. Lisans Tezi, Tekirdağ Namık Kemal Üniversitesi, Fen Bilimleri Enstitüsü, Tekirdağ, (2019).
  • Dorigo M., Optimization, Learning and Natural Algorithms, PhD Thesis, Politecnico di Milano, Italy, (1992).
  • Manuel of Steel Construction – Allowable Steel Design, 9th Ed., American Institute of Steel Construction, Chicago, III, (1989).
  • Bland, J. A., Optimal structural design by ant colony optimization, Engineering Optimization, 33, 4, 425-443, (2001).
  • Camp, C. V. and Bichon, B. J., Design of space trusses using ant colony optimization, Journal of structural engineering, 130, 5, 741-751, (2004).
  • Camp, C. V., Bichon, B. J. and Stovall, S. P., Design of steel frames using ant colony optimization, Journal of Structural Engineering, 131, 3, 369-379, (2005).
  • Serra, M. and Venini, P., On some applications of ant colony optimization metaheuristic to plane truss optimization, Structural and Multidisciplinary Optimization, 32, 6, 499-506, (2006).
  • Kaveh, A., Hassani, B., Shojaee, S. and Tavakkoli, S. M., Structural topology optimization using ant colony methodology, Engineering Structures, 30, 9, 2559-2565, (2008).
  • Aydoğdu İ., Optimum design of 3-D irregular steel frames using ant colony optimization and harmony search algorithms, Ph.D Thesis, Middle East Technical University, Graduate School of Natural and Applied Science, Ankara, (2010).
  • Yoo, K. S. and Han, S. Y., A modified ant colony optimization algorithm for dynamic topology optimization, Computers & Structures, 123, 68-78, (2013).
  • Babaei, M. and Sanaei, E., Multi-objective optimal design of braced frames using hybrid genetic and ant colony optimization, Frontiers of Structural and Civil Engineering, 10, 4, 472-480, (2016).
  • Kalatjari, V. R. and Talebpour, M. H., An improved ant colony algorithm for the optimization of skeletal structures by the proposed sampling search space method, Periodica Polytechnica Civil Engineering, 61, 2, 232-243, (2017).
  • Shafei, E. and Shirzad, A., Ant colony optimization for dynamic stability of laminated composite plates, Steel and Composite Structures, 25, 1, 105-116, (2017).
  • Liu, T., Sun, G., Fang, J., Zhang, J. and Li, Q. Topographical design of stiffener layout for plates against blast loading using a modified ant colony optimization algorithm. Structural and Multidisciplinary Optimization, 59, 335-350, (2019).
  • Greco, A., Pluchino, A. and Cannizzaro, F. An improved ant colony optimization algorithm and its applications to limit analysis of frame structures. Engineering Optimization, 51, 1867-1883, (2019).
  • Li, Y. and He, Y., Multi-objective optimization of construction project based on improved ant colony algorithm, Tehnički vjesnik, 27, 1, 184-190, (2020).
  • Soheili, S., Zoka, H. and Abachizadeh, M. Tuned mass dampers for the drift reduction of structures with soil effects using ant colony optimization. Advances in Structural Engineering, 24, 771-783 (2021).
  • Toğan, V. and Daloğlu, A. T., An improved genetic algorithm with initial population strategy and self-adaptive member grouping, Computers & Structures, 86, 11-12, 1204-1218, (2008).
  • Li, L. J., Huang, Z. B. and Liu, F., A heuristic particle swarm optimization method for truss structures with discrete variables, Computers & Structures, 87, 7-8, 435-443, (2009).
  • Sönmez, M., Discrete optimum design of truss structures using artificial bee colony algorithm, Structural and Multidisciplinary Optimization, 43, 85-97, (2011).
  • Sadollah, A., Bahreininejad, A., Eskandar, H. and Hamdi, M., Mine blast algorithm for optimization of truss structures with discrete variables, Computers & Structures, 102, 49-63, (2012).
  • Dede, T., Application of teaching-learning-based-optimization algorithm for the discrete optimization of truss structures, KSCE Journal of Civil Engineering, 18, 6, 1759-1767, (2014).
  • Artar, M., A comparative study on optimum design of multi-element truss structures, Steel and Composite Structures, 22, 3, 521-535, (2016).
  • Artar, M. and Daloglu, A. T., Optimum design of steel space truss towers under seismic effect using Jaya algorithm, Structural Engineering and Mechanics, 71, 1, 1-12, (2019).
  • Kaveh, A. and Zaerreza, A., Size/layout optimization of truss structures using shuffled shepherd optimization method, Periodica Polytechnica Civil Engineering, 64, 2, 408-421, (2020).
  • Rajeev, S. and Krishnamoorthy, C. S., Discrete optimization of structures using genetic algorithms, Journal of Structural Engineering, 118, 5, 1233-1250, (1992).
  • Aydın, Z., Determination the number of ants used in ACO algorithm via grillage optimization, Uludağ University Journal of The Faculty of Engineering, 22, 3, 251-262, (2017).

Examination of parameters used in ant colony algorithm over truss optimization

Yıl 2022, , 263 - 280, 05.01.2022
https://doi.org/10.25092/baunfbed.955408

Öz

Metaheuristic optimization techniques have been used to solve engineering problems with an increasing speed for the last 30 years. Most of these algorithms have been developed by imitating a process in nature. In this study, the ant colony algorithm inspired by the natural life of ants is discussed. The ant colony algorithm requires some parameters to perform an optimization, as in other meta-heuristic algorithms. The aim of this study is to examine the effect the values of the parameters used in the ant colony algorithm on the results. For this purpose, as an exemplary problem, a study was carried out on the optimization of truss systems, one of the constrained problems frequently discussed in the literature. Appropriate values of optimum design parameters such as number of ants, pheromone update coefficient and penalty coefficient were investigated using the coded computer program. As a result of the study, the effect of the relevant parameters on the result was determined and the points to be considered in the selection of these parameters were specified.

Kaynakça

  • Yılmaz A. H., Düzlem çelik kafes sistemlerin karınca kolonisi yöntemi ile optimum tasarımı, Y. Lisans Tezi, Tekirdağ Namık Kemal Üniversitesi, Fen Bilimleri Enstitüsü, Tekirdağ, (2019).
  • Dorigo M., Optimization, Learning and Natural Algorithms, PhD Thesis, Politecnico di Milano, Italy, (1992).
  • Manuel of Steel Construction – Allowable Steel Design, 9th Ed., American Institute of Steel Construction, Chicago, III, (1989).
  • Bland, J. A., Optimal structural design by ant colony optimization, Engineering Optimization, 33, 4, 425-443, (2001).
  • Camp, C. V. and Bichon, B. J., Design of space trusses using ant colony optimization, Journal of structural engineering, 130, 5, 741-751, (2004).
  • Camp, C. V., Bichon, B. J. and Stovall, S. P., Design of steel frames using ant colony optimization, Journal of Structural Engineering, 131, 3, 369-379, (2005).
  • Serra, M. and Venini, P., On some applications of ant colony optimization metaheuristic to plane truss optimization, Structural and Multidisciplinary Optimization, 32, 6, 499-506, (2006).
  • Kaveh, A., Hassani, B., Shojaee, S. and Tavakkoli, S. M., Structural topology optimization using ant colony methodology, Engineering Structures, 30, 9, 2559-2565, (2008).
  • Aydoğdu İ., Optimum design of 3-D irregular steel frames using ant colony optimization and harmony search algorithms, Ph.D Thesis, Middle East Technical University, Graduate School of Natural and Applied Science, Ankara, (2010).
  • Yoo, K. S. and Han, S. Y., A modified ant colony optimization algorithm for dynamic topology optimization, Computers & Structures, 123, 68-78, (2013).
  • Babaei, M. and Sanaei, E., Multi-objective optimal design of braced frames using hybrid genetic and ant colony optimization, Frontiers of Structural and Civil Engineering, 10, 4, 472-480, (2016).
  • Kalatjari, V. R. and Talebpour, M. H., An improved ant colony algorithm for the optimization of skeletal structures by the proposed sampling search space method, Periodica Polytechnica Civil Engineering, 61, 2, 232-243, (2017).
  • Shafei, E. and Shirzad, A., Ant colony optimization for dynamic stability of laminated composite plates, Steel and Composite Structures, 25, 1, 105-116, (2017).
  • Liu, T., Sun, G., Fang, J., Zhang, J. and Li, Q. Topographical design of stiffener layout for plates against blast loading using a modified ant colony optimization algorithm. Structural and Multidisciplinary Optimization, 59, 335-350, (2019).
  • Greco, A., Pluchino, A. and Cannizzaro, F. An improved ant colony optimization algorithm and its applications to limit analysis of frame structures. Engineering Optimization, 51, 1867-1883, (2019).
  • Li, Y. and He, Y., Multi-objective optimization of construction project based on improved ant colony algorithm, Tehnički vjesnik, 27, 1, 184-190, (2020).
  • Soheili, S., Zoka, H. and Abachizadeh, M. Tuned mass dampers for the drift reduction of structures with soil effects using ant colony optimization. Advances in Structural Engineering, 24, 771-783 (2021).
  • Toğan, V. and Daloğlu, A. T., An improved genetic algorithm with initial population strategy and self-adaptive member grouping, Computers & Structures, 86, 11-12, 1204-1218, (2008).
  • Li, L. J., Huang, Z. B. and Liu, F., A heuristic particle swarm optimization method for truss structures with discrete variables, Computers & Structures, 87, 7-8, 435-443, (2009).
  • Sönmez, M., Discrete optimum design of truss structures using artificial bee colony algorithm, Structural and Multidisciplinary Optimization, 43, 85-97, (2011).
  • Sadollah, A., Bahreininejad, A., Eskandar, H. and Hamdi, M., Mine blast algorithm for optimization of truss structures with discrete variables, Computers & Structures, 102, 49-63, (2012).
  • Dede, T., Application of teaching-learning-based-optimization algorithm for the discrete optimization of truss structures, KSCE Journal of Civil Engineering, 18, 6, 1759-1767, (2014).
  • Artar, M., A comparative study on optimum design of multi-element truss structures, Steel and Composite Structures, 22, 3, 521-535, (2016).
  • Artar, M. and Daloglu, A. T., Optimum design of steel space truss towers under seismic effect using Jaya algorithm, Structural Engineering and Mechanics, 71, 1, 1-12, (2019).
  • Kaveh, A. and Zaerreza, A., Size/layout optimization of truss structures using shuffled shepherd optimization method, Periodica Polytechnica Civil Engineering, 64, 2, 408-421, (2020).
  • Rajeev, S. and Krishnamoorthy, C. S., Discrete optimization of structures using genetic algorithms, Journal of Structural Engineering, 118, 5, 1233-1250, (1992).
  • Aydın, Z., Determination the number of ants used in ACO algorithm via grillage optimization, Uludağ University Journal of The Faculty of Engineering, 22, 3, 251-262, (2017).
Toplam 27 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Araştırma Makalesi
Yazarlar

Abidin Hakan Yılmaz Bu kişi benim 0000-0001-7476-4685

Zekeriya Aydın 0000-0003-1597-998X

Yayımlanma Tarihi 5 Ocak 2022
Gönderilme Tarihi 1 Temmuz 2021
Yayımlandığı Sayı Yıl 2022

Kaynak Göster

APA Yılmaz, A. H., & Aydın, Z. (2022). Examination of parameters used in ant colony algorithm over truss optimization. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 24(1), 263-280. https://doi.org/10.25092/baunfbed.955408
AMA Yılmaz AH, Aydın Z. Examination of parameters used in ant colony algorithm over truss optimization. BAUN Fen. Bil. Enst. Dergisi. Ocak 2022;24(1):263-280. doi:10.25092/baunfbed.955408
Chicago Yılmaz, Abidin Hakan, ve Zekeriya Aydın. “Examination of Parameters Used in Ant Colony Algorithm over Truss Optimization”. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi 24, sy. 1 (Ocak 2022): 263-80. https://doi.org/10.25092/baunfbed.955408.
EndNote Yılmaz AH, Aydın Z (01 Ocak 2022) Examination of parameters used in ant colony algorithm over truss optimization. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi 24 1 263–280.
IEEE A. H. Yılmaz ve Z. Aydın, “Examination of parameters used in ant colony algorithm over truss optimization”, BAUN Fen. Bil. Enst. Dergisi, c. 24, sy. 1, ss. 263–280, 2022, doi: 10.25092/baunfbed.955408.
ISNAD Yılmaz, Abidin Hakan - Aydın, Zekeriya. “Examination of Parameters Used in Ant Colony Algorithm over Truss Optimization”. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi 24/1 (Ocak 2022), 263-280. https://doi.org/10.25092/baunfbed.955408.
JAMA Yılmaz AH, Aydın Z. Examination of parameters used in ant colony algorithm over truss optimization. BAUN Fen. Bil. Enst. Dergisi. 2022;24:263–280.
MLA Yılmaz, Abidin Hakan ve Zekeriya Aydın. “Examination of Parameters Used in Ant Colony Algorithm over Truss Optimization”. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, c. 24, sy. 1, 2022, ss. 263-80, doi:10.25092/baunfbed.955408.
Vancouver Yılmaz AH, Aydın Z. Examination of parameters used in ant colony algorithm over truss optimization. BAUN Fen. Bil. Enst. Dergisi. 2022;24(1):263-80.