Abstract
Artificial bee colony algorithm (ABC) has been efficiently used to obtain the optimum design of structures under design code limitations. There are several enhancements suggested to improve the performance of ABC even further in the literature. In this chapter, the performance of enhanced ABC variants in the optimum design of steel skeletal frames is studied. The mathematical model of such design problem when based on the provisions of design code requirements results in a discrete nonlinear programming problem. Seven variants of ABC is selected from the literature. These are standard artificial bee colony algorithm (sABC), artificial bee colony algorithm with orthogonal learning (OCABC), quick artificial bee colony algorithm (qABC), noel chaotic artificial bee colony algorithm (STOC-BC), directed artificial bee colony algorithm (dABC), improved artificial bee colony algorithm (NSABC) and artificial bee colony algorithm with Levy flight distribution (ABC_Levy). Seven optimum design algorithms are developed to obtain the solution of the design problem formulated considering design code provisions. Two steel space frames, one ten story and the other fifteen story are optimized using each of the seven optimum design techniques. The performance of each variant is observed and the optimum designs are compared. It is noticed after carrying out the statistical analysis that ABC_Levy and qABC algorithms outperform the other algorithms.
Similar content being viewed by others
References
Akay, B., & Karaboga, D. (2012). A modified artificial bee colony algorithm for real-parameter optimization. Information Sciences, 192, 120–142.
Akın, A., & Aydoğdu, İ. (2015). Optimum design of steel space frames by hybrid teaching-learning based optimization and harmony search algorithms. International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 9(7), 1367–1374.
Arasomwan, M. A., & Adewumi, A. (2013). On the performance of linear decreasing inertia weight particle swarm optimization. The Scientific World Journal, 2, 860289.
Aydoğdu, İ. (2010). Optimum Design of 3-D Irregular Steel Frames Using Ant Colony Optimization and Harmony Search algorithms. PhD Thesis, Middle East Technical University, August, Ankara, Turkey,
Aydoğdu, İ., Akın, A., & Saka, M. (2012b). Optimum design of steel space frames by artificial bee colony algorithm. Paper presented at the ACE 2012, Proceedings of 10th International Conference on Advances in civil Engineering, Ankara, Turkey,
Aydoğdu, İ., Akın, A., & Saka, M. (2012a). Discrete design optimization of space steel frames using the adaptive firefly algorithm. Paper presented at the The Eleventh International Conference on Computational Structures Technology, Dubrovnik, Crotia,
Aydoğdu, İ., Akın, A., & Saka, M. (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.
Aydogdu, I., Efe, P., Yetkin, M., & Akin, A. (2017). Optimum design of steel space structures using social spider optimization algorithm with spider jump technique. Structural Engineering and Mechanics, 62(3), 259–272.
Aydogdu, I., & Saka, M. P. Ant Colony Optimization of Irregular Steel Frames Including Effect of Warping. In B. H. V. Topping, L. F. Costa Neves, & R. C. Barros (Eds.), Twelfth International Conference on Civil, Structural and Environmental Engineering Computing, Madeira, Portugal, 2009: Civil Comp.
Bansal, J. C., Singh, P. K., & Pal, N. R. (2019). Evolutionary and swarm intelligence algorithms. Berlin: Springer.
Blum, C., & Merkle, D. (2008). Swarm Intelligence in Optimization. Berlin: Springer.
Bonabeau, E., Dorigo, M., Marco, D. D. R. D. F., Theraulaz, G., & Théraulaz, G. (1999). Swarm intelligence: from natural to artificial systems (Vol 1). Oxford: Oxford University Press.
Boyle, P. P. (1977). Options: A monte carlo approach. Journal of Financial Economics, 4(3), 323–338.
Çarbaş, S. (2016). Design optimization of steel frames using an enhanced firefly algorithm. Engineering Optimization. https://doi.org/10.1080/0305215X.2016.1145217.
Carbas, S., & Aydogdu, I. (2021). Cuckoo search for optimum design of real-sized high-level steel frames. In Applications of Cuckoo Search Algorithm and its Variants (pp. 123–145, Springer Tracts in Nature-Inspired Computing).
Chen, W.-F., & Kim, S.-E. (1997). LRFD steel design using advanced analysis (Vol 13). Boca Raton: CRC Press.
Coelho, L. D. S., & Alotto, P. (2011). Gaussian artificial bee colony algorithm approach applied to loney's solenoid benchmark problem. Ieee Transactions on Magnetics, 47(5), 1326–1329. https://doi.org/10.1109/tmag.2010.2087317.
Degertekin, S. Ö. (2012). Optimum design of geometrically non-linear steel frames using artificial bee colony algorithm. Steel Composite Structures, 12(6), 505–522.
Eberhart, R. C., Shi, Y., & Kennedy, J. (2001). Swarm intelligence. Amsterdam: Elsevier.
Ellingwood, B. (1986). Structural serviceability: A critical appraisal and research needs. Journal of Structural Engineering, 112(12), 2646–2664.
Ellingwood, B. (1989). Serviceability guidelines for steel structures. Engineering Journal AISC, 26(1), 1–8.
Gao, W.-F., & Liu, S.-S. (2012). A modified artificial bee colony algorithm. Computers & Operations Research, 39(3), 687–697. https://doi.org/10.1016/j.cor.2011.06.007.
Gao, W.-F., Liu, S.-Y., & Huang, L.-L. (2013). A novel artificial bee colony algorithm based on modified search equation and orthogonal learning. IEEE Transactions on Cybernetics, 43(3), 1011–1024.
Gao, W. F., Liu, S. Y., & Huang, L. L. A. S. C. (2013). A novel artificial bee colony algorithm with Powell's method. Applied Soft Computing, 13(9), 3763–3775.
Gao, W., Liu, S., Huang, L. J. J. O. C., & Mathematics, A. (2012). A global best artificial bee colony algorithm for global optimization. Journal of Computational and Applied Mathematics, 236(11), 2741–2753.
Garg, H. (2014). Solving structural engineering design optimization problems using an artificial bee colony algorithm. Journal of Industrial Management Optimization, 10(3), 777–794.
Hadidi , A., Kazemzadeh, A. S., & Kazemzadeh, A. S. Structural optimization using artificial bee colony algorithm. In H. C. Rodrigues, J. L. Herskovits, C. M. M. Soares, J. M. Guedes, A. L. Araújo, J. O. Folgado, et al. (Eds.), International Conference on Engineering Optimization, Lisbon, Portugal, 2010
ISOVER (2020). Energy efficiency in buildings. https://www.isover.com/energy-efficiency-buildings. Accessed 04.07 2020.
Kang, F., Li, J., & Li, H. (2013). Artificial bee colony algorithm and pattern search hybridized for global optimization. Applied Soft Computing, 13(4), 1781–1791.
Kang, F., Li, J., Ma, Z., & Li, H. J. J. O. S. (2011). Artificial bee colony algorithm with local search for numerical optimization. Journal of Software, 6(3), 490–497.
Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes university, engineering faculty, Computer Engineering Department.
Karaboga, D., & Akay, B. (2009). A comparative study of artificial bee colony algorithm. Applied mathematics computation, 214(1), 108–132.
Karaboga, D., & Basturk, B. Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. In International fuzzy systems association world congress, 2007a (pp. 789–798): Springer
Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459–471. https://doi.org/10.1007/s10898-007-9149-x.
Karaboga, D., & Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing, 8(1), 687–697. https://doi.org/10.1016/j.asoc.2007.05.007.
Karaboga, D., & Gorkemli, B. A quick artificial bee colony-qABC-algorithm for optimization problems. In 2012 International symposium on innovations in intelligent systems and applications, 2012 (pp. 1–5): IEEE
Kaveh, A. (2014). Advances in metaheuristic algorithms for optimal design of structures. Cham: Springer.
Kaveh, A. (2017). Applications of metaheuristic optimization algorithms in civil engineering (Vol. PUBDB-2017–153072). Cham: Springer.
Kaveh, A., & Bakhshpoori, T. (2019). Metaheuristics: Outlines, MATLAB codes and examples. Cham: Springer.
Kaveh, A., & Eslamlou, A. D. (2020). Metaheuristic optimization algorithms in civil engineering: New applications. Cham: Springer.
Kaveh, A., & Ghazaan, M. I. (2018). Meta-heuristic algorithms for optimal design of real-size structures. Berlin: Springer.
Kıran, M. S., & Fındık, O. (2015). A directed artificial bee colony algorithm. Applied Soft Computing, 26, 454–462.
Kuang, F., Jin, Z., Xu, W., & Zhang, S. A novel chaotic artificial bee colony algorithm based on tent map. In 2014 IEEE congress on evolutionary computation (CEC), 2014 (pp. 235–241): IEEE
Lamberti, L., & Pappalettere, C. (2011). Metaheuristic design optimization of skeletal structures: A review. Computational Technology Reviews, 4(1), 1–32.
Latif, M., & Saka, M. P. (2019). Optimum design of tied-arch bridges under code requirements using enhanced artificial bee colony algorithm. Advances in Engineering Software, 135, 102685.
Li, G., Niu, P., & Xiao, X. (2012). Development and investigation of efficient artificial bee colony algorithm for numerical function optimization. Applied Soft Computing, 12(1), 320–332.
Liao, X., Zhou, J., Zhang, R., & Zhang, Y. (2012). An adaptive artificial bee colony algorithm for long-term economic dispatch in cascaded hydropower systems. International Journal of Electrical Power & Energy Systems, 43(1), 1340–1345. https://doi.org/10.1016/j.ijepes.2012.04.009.
LRFD A. (2000). Load and resistance factor design specification. Chicago: American Institute of Steel Construction.
Mantegna, R. N. (1994). Fast, accurate algorithm for numerical simulation of Levy stable stochastic processes. Physical Review E, 49(5), 4677.
NASA (2020). Global Climate Change. https://climate.nasa.gov/evidence/. Accessed 04.07 2020.
Saka, M. P., Aydogdu, I., Hasancebi, O., & Geem, Z. W. (2011). Harmony search algorithms in structural engineering. In Computational Optimization and Applications in Engineering and Industry (pp. 145–182): Springer.
Saka, M. P., Çarbaş, S., Aydoğdu, İ., & Akın, A. (2016). Use of swarm intelligence in structural steel design optimization. In Metaheuristics and Optimization in Civil Engineering (pp. 43–73): Springer.
Saka, M., Carbas, S., Aydogdu, I., Akin, A., & Geem, Z. (2015). Comparative Study on Recent Metaheuristic Algorithms in Design Optimization of Cold-Formed Steel Structures. In M. P. Nikos D. Lagaros (Ed.), Engineering and Applied Sciences Optimization (Vol. 38, pp. 145–173). Cham, Switzerland: Springer.
Saka, M. P., & Dogan, E. (2012). Recent developments in metaheuristic algorithms: A review. Computer Technology Review, 5(4), 31–78.
Saka, M. P., & Geem, Z. W. (2013). Mathematical and metaheuristic applications in design optimization of steel frame structures: an extensive review. Mathematical Problems in Engineering, 2013, 1–33.
Saka, M. P., & Hasançebi, O. (2009). Adaptive harmony search algorithm for design code optimization of steel structures. In Harmony search algorithms for structural design optimization (pp. 79–120): Springer.
Saka, M., Hasançebi, O., & Geem, Z. (2016). Metaheuristics in structural optimization and discussions on harmony search algorithm. Swarm and Evolutionary Computation, 28, 88–97.
Shi, Y., Pun, C. M., Hu, H., & Gao, H. (2016). An improved artificial bee colony and its application. Knowledge-Based Systems, 107, 14–31.
Sonmez, M. (2011). Artificial Bee Colony algorithm for optimization of truss structures. Applied Soft Computing, 11(2), 2406–2418.
Sonmez, M. (2011). Discrete optimum design of truss structures using artificial bee colony algorithm. Structural and Multidisciplinary Optimization, 43(1), 85–97. https://doi.org/10.1007/s00158-010-0551-5.
Wang, H., Wu, Z., Rahnamayan, S., Sun, H., Liu, Y., & Pan, J. S. (2014). Multi-strategy ensemble artificial bee colony algorithm. Information Sciences, 279, 587–603.
Wu, B., Qian, C., Ni, W., Fan, S. J. C., Applications, M., & w., (2012). Hybrid harmony search and artificial bee colony algorithm for global optimization problems. Computers & Mathematics with Applications, 64(8), 2621–2634.
Yang, X.-S. (2010a). Engineering optimization: an introduction with metaheuristic applications. Hoboken: Wiley.
Yang, X.-S. (2010). Nature-inspired metaheuristic algorithms. Beckington: Luniver press.
Yang, X.-S. (2015). Recent advances in swarm intelligence and evolutionary computation. Berlin: Springer.
Yang, X.-S., Cui, Z., Xiao, R., Gandomi, A. H., & Karamanoglu, M. (2013). Swarm intelligence and bio-inspired computation: theory and applications: Newnes. Amsterdam: Elsevier.
Zhu, G., & Kwong, S. (2010). Gbest-guided artificial bee colony algorithm for numerical function optimization. Applied Mathematics & Computation, 217(7), 3166–3173.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
On behalf of all authors, the corresponding author declares that there is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Saka, M.P., Aydogdu, I. Performance evaluation of artificial bee colony algorithm and its variants in the optimum design of steel skeletal structures. Asian J Civ Eng 22, 73–91 (2021). https://doi.org/10.1007/s42107-020-00299-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s42107-020-00299-z