THE PERFORMANCE COMPARISON OF THREE METAHEURISTIC ALGORITHMS ON THE SIZE, LAYOUT AND TOPOLOGY OPTIMIZATION OF TRUSS STRUCTURES

Year 2019, Volume: 5 Issue: 2, 28 - 41, 11.12.2019

Ali Mortazavi

https://doi.org/10.22531/muglajsci.593482

Cited By: 12

Abstract

The structural optimization problem mostly deals with the weight
minimization of the structural system. This issue can be assessed from the
size, layout and topology aspects. No matter which of these aspects are
targeted, to solve them an optimization technique is required. In the last
decades the metaheuristic techniques, as the non-gradient optimization
algorithms, are widely applied on solving these classes of problems. In the
structural optimization, the most time consuming part of the process is the
objective function evaluation. Based on this fact, in the current work, these
techniques are divided into three main groups as single phase, double phase and
multi-phase algorithms. Then based on the author knowledge, three representative
methods are picked for each group and their search performance comparatively
inspected on solving size, shape and topology optimization of truss structures.
To meet this aim, Integrated Particle Swarm Optimization (iPSO), Teaching and
Learning Based Optimization (TLBO) and Drosophila Food-Search Optimization
(DSO) algorithms are selected, respectively. Different properties like
accuracy, convergence rate and complexity of the algorithms are investigated.
The outcomes are provided via illustrative diagrams and tables. Based on the
achieved results, DSO shows the most complexity level among the other algorithm
while the iPSO and TLBO can outperform it on both accuracy and convergence
rate. Consequently, iPSO presents a higher accuracy level on finding optimal
solutions and TLBO with the lowest standard deviation value through the process
shows the highest level of stability on finding optimal solutions.

Keywords

Structural Optimization, Metaheuristic Algorithms, Constrained Optimization, Performance Comparison

ÜÇ SEZGİSEL YÖNTEMİN KAFES SİSTEMLERİN TOPOLOJİ, GEOMETRİ VE BOYUT OPTİMİZASYONU ÜZERİNDE PERFORMANS KARŞILAŞTIRMASI

Abstract

Bir yapısal optimizasyonda elemanların topolojisi, geometrisi veya kesitlerin boyutları dikkate alınarak sistemin ağırlığının minimize edilmesi amaçlanmaktadır. Çözüm tekniği olarak bu alanda son yıllarda üzerinde oldukça sık çalışılan sezgisel (metaheuristic) yöntemler geliştirilmiş ve kullanılmıştır. Yapısal optimizasyonda, amaç fonksiyonunun değerlendirmesi her iterasiyonda bir (ya da birden fazla)  yapısal analiz gerektirmektedir ve dolaysıyla çözüm sürecinin en çok zaman alan kısmını oluşturmaktadır. Bu gerçeği dikkate alarak, mevcut çalışmada bu yöntemler, tek fazlı, çift fazlı ve çok fazlı algoritmalar olarak üç ana gruba ayrılmış ve her gruptan bir yöntem seçilmiştir. Daha sonra bu yöntemlerin arama performansları kafes yapıların boyut, geometri ve topoloji optimizasyonu üzerinde karşılaştırılmıştır. Entegre edilmiş Partikül Sürüsü Optimizasyon (EPSO), Öğretme ve Öğrenme esaslı Optimizasyon (ÖÖO) ve Derosofila Yiyecek arama Optimizasyon (DYO) sırasıyla seçilen algoritmalardır. Algoritmaların, yakınsama hızı, dikkati ve karmaşıklığı gibi farklı özellikleri değerlendirilmiştir. Elde edilen sonuçlara göre, DYO diğer algoritmalara kıyasen en yüksek karmaşıklık indeksine sahiptir, ayrıca EPSO ve ÖÖO dikkat ve yakınsama hızı açısından daha iyi performans göstermektedirler. Üstelik, EPSO, optimum çözümler bulma konusunda daha yüksek bir dikkat seviyesine sahiptir. Optimizasyon sürecinde ÖÖO en düşük standart sapma değerine sahiptir ve dolaysıyla optimum çözümler bulma konusunda en yüksek kararlılık seviyesini göstermektedir.

Keywords

Yapısal Optimizasyon, Sezgisel Algoritmalar, Kısıtlı Optimizasyon, Performans Karşılaştırması

References

• [1]Mortazavi, A., Toğan V., Nuhoğlu A., "An integrated particle swarm optimizer for optimization of truss structures with discrete variables", Structural Engineering and Mechanics, Vol. 61, pp. 359-370, 2017.
• [2]Shon, S.-D., Hwang K.-J., Lee S.-J., "Numerical evaluation of buckling behavior in space structure considering geometrical parameters with joint rigidity", Journal of Central South University, Vol. 21 No. 3, pp. 1115-1124, 2014.
• [3]Hasançebi, O., Kazemzadeh Azad S., "Discrete size optimization of steel trusses using a refined big bang–big crunch algorithm", Engineering Optimization, Vol. 46 No. 1, pp. 61-83, 2014.
• [4]Hasançebi, O., Çarbaş S., Doğan E., Erdal F., Saka M.P., "Performance evaluation of metaheuristic search techniques in the optimum design of real size pin jointed structures", Computers & Structures, Vol. 87 No. 5, pp. 284-302, 2009.
• [5]Chen, D., Zhao C., "Particle swarm optimization with adaptive population size and its application", Applied Soft Computing, Vol. 9 No. 1, pp. 39-48, 2009.
• [6]Dede, T., Ayvaz Y., "Combined size and shape optimization of structures with a new meta-heuristic algorithm", Applied Soft Computing, Vol. 28, pp. 250-258, 2015.
• [7]Mortazavi, A., Toğan V., "Sizing and layout design of truss structures under dynamic and static constraints with an integrated particle swarm optimization algorithm", Applied Soft Computing, Vol. 51, pp. 239-252, 2017.
• [8]Gholizadeh, S., "Layout optimization of truss structures by hybridizing cellular automata and particle swarm optimization", Computers & Structures, Vol. 125, pp. 86-99, 2013.
• [9]Hasançebi, O., Erbatur F., "Layout optimization of trusses using improved GA methodologies", Acta Mechanica, Vol. 146 No. 1, pp. 87-107, 2001.
• [10]Mortazavi, A., Toğan V., "Simultaneous size, shape, and topology optimization of truss structures using integrated particle swarm optimizer", Structural and Multidisciplinary Optimization, Vol. 54 No. 4, pp. 715-736, 2016.
• [11]Savsani, V.J., Tejani G.G., Patel V.K., "Truss topology optimization with static and dynamic constraints using modified subpopulation teaching–learning-based optimization", Engineering Optimization, Vol. 48 No. 11, pp. 17, 2016.
• [12]Frans, R., Arfiadi Y., "Sizing, Shape, and Topology Optimizations of Roof Trusses Using Hybrid Genetic Algorithms", Procedia Engineering, Vol. 95, pp. 185-195, 2014.
• [13]Rahami, H., Kaveh A., Gholipour Y., "Sizing, geometry and topology optimization of trusses via force method and genetic algorithm", Engineering Structures, Vol. 30 No. 9, pp. 2360-2369, 2008.
• [14]Mortazavi, A., Toğan V., "Triangular units based method for simultaneous optimizations of planar trusses", Advances in Computational Design, Vol. 2 No. 3, pp. 195-210, 2017.
• [15]Kennedy, J., Eberhart R., Particle swarm optimization, in: Neural Networks, 1995. Proceedings., IEEE International Conference on, 1995, pp. 1942-1948.
• [16]Storn, R., Price K., "Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces", Journal of Global Optimization, Vol. 11 No. 4, pp. 341-359, 1997.
• [17]Dorigo, M., Blum C., "Ant colony optimization theory: A survey", Theoretical Computer Science, Vol. 344 No. 2, pp. 243-278, 2005.
• [18]Oftadeh, R., Mahjoob M.J., Shariatpanahi M., "A novel meta-heuristic optimization algorithm inspired by group hunting of animals: Hunting search", Computers & Mathematics with Applications, Vol. 60 No. 7, pp. 2087-2098, 2010.
• [19]Das, K.N., Singh T.K., "Drosophila Food-Search Optimization", Applied Mathematics and Computation, Vol. 231, pp. 566-580, 2014.
• [20]Gonçalves, M.S., Lopez R.H., Miguel L.F.F., "Search group algorithm: A new metaheuristic method for the optimization of truss structures", Computers & Structures, Vol. 153, pp. 165-184, 2015.
• [21]Merrikh-Bayat, F., "The runner-root algorithm: A metaheuristic for solving unimodal and multimodal optimization problems inspired by runners and roots of plants in nature", Applied Soft Computing, Vol. 33, pp. 292-303, 2015.
• [22]Pavithr, R.S., Gursaran. "Quantum Inspired Social Evolution (QSE) algorithm for 0-1 knapsack problem", Swarm and Evolutionary Computation, Vol. 29, pp. 33-46, 2016.
• [23]Liang, Y.-C., Cuevas Juarez J.R., "A novel metaheuristic for continuous optimization problems: Virus optimization algorithm", Engineering Optimization, Vol. 48 No. 1, pp. 73-93, 2016.
• [24]Rao, R.V., Savsani V.J., Vakharia D.P., "Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems", Computer-Aided Design, Vol. 43 No. 3, pp. 303-315, 2011.
• [25]Mortazavi, A., Toğan V., Nuhoğlu A., "Weight minimization of truss structures with sizing and layout variables using integrated particle swarm optimizer AU - Mortazavi, Ali", Journal of Civil Engineering and Management, Vol. 23 No. 8, pp. 985-1001, 2017.
• [26]Kaveh, A., Zolghadr A., "Democratic PSO for truss layout and size optimization with frequency constraints", Computers & Structures, Vol. 130, pp. 10-21, 2014.