Optimal Design of Steel Planar Trusses Using Ant Lion Algorithm

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Authors

  • Senior Professor, Department of Civil Engineering Siddaganga Institute of Technology, Tumakuru, Karnataka ,IN

DOI:

https://doi.org/10.18311/jmmf/2022/32021

Abstract

This paper elaborates on optimized design of steel structures directed towards the sustainability of materials. The case in point is steel trusses that are extensively used structural components. Though copious research is available on use of conventional optimization methods, nature-inspired optimization algorithms have received scarce attention particularly in optimal design of planar trusses. In this paper, the development of Ant Lion algorithm for the optimal design models for steel trusses is elaborated. A comprehensive comparison with the optimized sectional weights obtained by other nature inspired optimization algorithms implemented in earlier research by the author. They include elitism based genetic algorithm (EBGA), ant colony optimization (ACO), artificial honeybee optimization (AHBO), and Particle swarm optimization (PSO) algorithm. Four steel trusses with different articulations have been considered for this purpose. It is found that the optimal weights obtained by Ant Lion algorithm are almost on par with those obtained by PSO. The other three algorithms vary marginally. However, the convergence to overall weight of trusses is different for different algorithms. ALO took 100-200 iterations for the convergence. In fact, the convergence to optimized weights are faster in case of ALO and PSO in relation to other algorithms.

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Published

2022-12-08

How to Cite

Jayaram, M. A. (2022). Optimal Design of Steel Planar Trusses Using Ant Lion Algorithm. Journal of Mines, Metals and Fuels, 70(8A), 432–443. https://doi.org/10.18311/jmmf/2022/32021

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References

Abedinia. O, Amjady, N., Ghasemi. A.(2016), A new metaheuristic algorithm based on shark smell optimization, Complexity, 21 (5), 97-116. DOI: https://doi.org/10.1002/cplx.21634

Adel Saad Assiri , Abdelazim G. Hussien , Mohamed Amin. (2020), Ant Lion Optimization: Variants, Hybrids, and Applications, IEEE Access, Vol 8,746-764,2020. DOI: https://doi.org/10.1109/ACCESS.2020.2990338

Alsattar, H.,Zaidan, H., Zaidan .B. (2019), Novel meta-heuristic bald eagle search optimisation algorithm, Artificial Intelligence Review, 53(6), 1-28. DOI: https://doi.org/10.1007/s10462-019-09732-5

Ambriz-Perez, H., E. Acha C., R Fuerte- Esquivel, A. De La Torre., (1998), Incorporation of a UPFC model in an optimal power flow using Newton’s method, IEEE Proceedings on Generation Transmission Distribution, 145(3), 336-440. DOI: https://doi.org/10.1049/ip-gtd:19981944

Askarzadeh. A.,(2016), A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm, Computers & Structures, 169, 1-12. DOI: https://doi.org/10.1016/j.compstruc.2016.03.001

Ali Kaveh, Ataollah Zaerreza.,(2020), Size/ Layout optimization of truss structures using shuffled shepherd optimization method, Periodica Polytechnica, Civil Engineering, 64(2), 408-421. DOI: https://doi.org/10.3311/PPci.15726

Ali Kaveh, Khosravian, M., (2022)., Size and layout optimization of truss structures using vibrating particles system meta-heuristic algorithm and its improved version, Periodica Polytechnica, Civil Engineering, 66(1), 1–17. DOI: https://doi.org/10.3311/PPci.18670

Bekdas G. , Nigdeli, S M., Yang X S.,(2015), Sizing optimization of truss structures using flower pollination algorithm, Appl. Soft Computing, 37, 322 DOI: https://doi.org/10.1016/j.asoc.2015.08.037

Bureerat S, Pholdee N.,(2016): Optimal truss sizing using an adaptive differential evolution algorithm. J. Comput. Civ. Eng., 30, 4015 DOI: https://doi.org/10.1061/(ASCE)CP.1943-5487.0000487

Cheraghalipour, C., Hajiaghaei-Keshteli, M., Paydar M. M., (2018): Tree growth algorithm (tga): A novel approach for solving optimization problems, Engineering Applications of Artificial Intelligence 72, 393-414. DOI: https://doi.org/10.1016/j.engappai.2018.04.021

Degertekin. S. O., Hayalioglu, M.S., (2013): Sizing truss structures using teaching-learning- based Optimization, Comput. Struct,119, 177. DOI: https://doi.org/10.1016/j.compstruc.2012.12.011

Dhiman, G., Kumar,V.,(2017), Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications, Advances in Engineering Software, 114, 48-70. DOI: https://doi.org/10.1016/j.advengsoft.2017.05.014

Dian Setiya Widodo, Dana Marsetiya Utama, (2020), The hybrid Ant Lion optimization for flow shop scheduling problem for mining completion time, Journal of Physics: Conference Series, 2020. DOI: https://doi.org/10.1088/1742-6596/1569/2/022097

Fard, M., Hajiaghaei-Keshteli. M.,(2016): Red deer algorithm (rda); A new optimization algorithm inspired by red deers’ mating”, in: International Conference on Industrial Engineering, IEEE., 33- 34.

Haug, E.J., Arora, J.H., (1989): Introduction to optimal design, McGrawHill, NewYork.

Herbert Martin Gomes, Truss optimization with dynamic constraints using a particle swarm algorithm, Expert Systems with Applications, 38(1), 957-968, 2011. DOI: https://doi.org/10.1016/j.eswa.2010.07.086

Heidari, A. A., Mirjalili, S., Faris,H., Aljarah, Mafarja., M, Chen H., (2019): Harris hawks optimization: Algorithm and applications, Future Generation Computer Systems, 97 , 849-872. DOI: https://doi.org/10.1016/j.future.2019.02.028

Hayder Kilic, Ugur Yuzgec, (2019): Tournament selection-based antlion optimization algorithm for solving quadratic assignment problem, Engineering Science and Technology, 22, 673-691 DOI: https://doi.org/10.1016/j.jestch.2018.11.013

IS-800, (2007), Indian standard general construction in steel- Code of practice, (2017), Third revision, Bureau of Indian standards New Delhi India.

Jain, M., Maurya, S., Rani, A., Singh,V., (2018): Owl search algorithm: A novel nature-inspired heuristic paradigm for global optimization, Journal of Intelligent & Fuzzy Systems, 34 (3), 1573-1582. DOI: https://doi.org/10.3233/JIFS-169452

Jain, M., Singh, V., Rani , A. (2018): A novel nature-inspired algorithm for optimization: Squirrel search algorithm”, Swarm and Evolutionary Computation, 44, 33-45. DOI: https://doi.org/10.1016/j.swevo.2018.02.013

Jayaram, M.A., (2022): Bio-inspired algorithms for optimal design of trusses, In. IOP conf. Series, Earth and Environ. Sci., 982, 1-15. DOI: https://doi.org/10.1088/1755-1315/982/1/012073

Kazemzadeh Azad, S., Hasançebi O.,(2016): Structural optimization using big bang-big crunch algorithm S.: A review, Int. J. Optim. Civil Eng., 6(3), 433-445.

Koziel, S. Yang X.-S.,(2011) Computational optimization, methods and algorithms, vol. 356. Springer. DOI: https://doi.org/10.1007/978-3-642-20859-1

Kirschen, D. S ., Van Meeteren, H. P. (1988), MW/ voltage control in linear programming based optimal power flow, IEEE Trans Power Syst, 3(4), 481–490. DOI: https://doi.org/10.1109/59.192899

Kalyanmoy Deb., 2013 Optimization for engineering design: Algorithms and examples, II edition, PHI Learning, New Delhi.

Masoud Salar, Babak Dizangian,(2019), Sizing optimization of truss structures using ant lion optimizer Proc. of 2nd Int. conf. on civil engineering, architecture and urban management in Iran, Tehran University ,Iran.

Momohd, J. A, XGuo S., Ogbuobiri, E. C., Adapa, R., (1994),The quadratic interior point method solving power system optimization problems, IEEE Transactions on Power Systems, 9(3), 1327-1336. DOI: https://doi.org/10.1109/59.336133

Mirjalili S.,(2015): The ant lion optimizer, Advances in Engineering Software, 83, 80-98. DOI: https://doi.org/10.1016/j.advengsoft.2015.01.010

Saremi, S. Mirjalili, A., Lewis, Grasshopper optimisation algorithm:heory and application”, Advances in Engineering Software, 105 (2017), 30-47. DOI: https://doi.org/10.1016/j.advengsoft.2017.01.004

SP-6(1), (1998): Hand book of structural steel sections (revised), Bureau of Indian standards, New Delhi India.

Tayfun Dedea,, Serkan Bekiroglu , Yusuf Ayvaz, Weight minimization of trusses with genetic algorithms, Applied Soft Computing, 11, 2011, 2565- 2575. DOI: https://doi.org/10.1016/j.asoc.2010.10.006

Tejani G.G, Pholdee N, Bureerat S, and Prayogo D, (2018): Multiobjective adaptive symbiotic organisms search for truss optimization problems, Knowl. Based Syst, 161, 398-414. DOI: https://doi.org/10.1016/j.knosys.2018.08.005

Wang, G.G., Deb, S., (2015): Earthworm optimization algorithm: a bio-inspired metaheuristic algorithm for global optimization problems, 3rd International Symposium on, IEEE, 1-5. DOI: https://doi.org/10.1504/IJBIC.2015.10004283

Wang,G.G, Deb, S., Coelho, L.D.S., (2015): Elephant herding optimization”, in : Computational and Business Intelligence (ISCBI), 3rd International Symposium on, IEEE, 1-5. DOI: https://doi.org/10.1109/ISCBI.2015.8

Yang,X.S., Karamanoglu, M., (2013), Swarm intelligence and bio-inspired computation: an overview, in: Swarm Intelligence and Bio- Inspired Computation, Elsevier, 3-23. DOI: https://doi.org/10.1016/B978-0-12-405163-8.00001-6

Zhu,J, (2009): Optimization of power system operation, John Wiley & sons, Publication, 112- 115. 2009. DOI: https://doi.org/10.1002/9780470466971