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http://dx.doi.org/10.12989/sss.2017.20.5.607

Design of multi-span steel box girder using lion pride optimization algorithm  

Kaveh, A. (Centre of Excellence for Fundamental Studies in Structural Engineering, Iran University of Science and Technology)
Mahjoubi, S. (Centre of Excellence for Fundamental Studies in Structural Engineering, Iran University of Science and Technology)
Publication Information
Smart Structures and Systems / v.20, no.5, 2017 , pp. 607-618 More about this Journal
Abstract
In this research, a newly developed nature-inspired optimization method, the Lion Pride Optimization algorithm (LPOA), is utilized for optimal design of composite steel box girder bridges. A composite box girder bridge is one of the common types of bridges used for medium spans due to their economic, aesthetic, and structural benefits. The aim of the present optimization procedure is to provide a feasible set of design variables in order to minimize the weight of the steel trapezoidal box girders. The solution space is delimited by different types of design constraints specified by the American Association of State Highway and Transportation Officials. Additionally, the optimal solution obtained by LPOA is compared to the results of other well-established meta-heuristic algorithms, namely Gray Wolf Optimization (GWO), Ant Lion Optimizer (ALO) and the results of former researches. By this comparison the capability of the LPOA in optimal design of composite steel box girder bridges is demonstrated.
Keywords
composite box girder; optimal design; lion pride optimization algorithm; constrained problems; meta-heuristic search;
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1 Bureerat, S. and Pholdee, N. (2015), "Optimal truss sizing using an adaptive differential evolution algorithm", J. Comput. Civil Eng., 30(2), 04015019.
2 Cheng, M.Y. and Prayogo, D. (2014), "Symbiotic organisms search: a new metaheuristic optimization algorithm", Comput. Struct., 139, 98-112.   DOI
3 Gandomi, A.H., Yang, X.S. and Alavi, A.H. (2013), "Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems", Eng. Comput., 29(1), 17-35.   DOI
4 Garcia-Segura, T., Yepes, V. and Frangopol, D.M. (2017), "Multi-objective design of post-tensioned concrete road bridges using artificial neural networks", Struct. Multidiscip. O., 56(1) ,139-150.   DOI
5 Geem, Z.W., Kim, J.H. and Loganathan, G. (2001), "A new heuristic optimization algorithm: harmony search", Simulation, 76(2), 60-68.   DOI
6 Guo, S.M., Tsai, J.S.H., Yang, C.C. and Hsu, P.H. (2015), "A self-optimization approach for L-SHADE incorporated with eigenvector-based crossover and successful-parent-selecting framework on CEC 2015 benchmark set", Proceedings of the Evolutionary Computation (CEC), 2015 IEEE Congress on. IEEE.
7 Hasancebi, O. and Azad, S.K. (2013), "Reformulations of big bang-big crunch algorithm for discrete structural design optimization", World Academy of Science Engineering and Technology, 74.
8 Hasancebi, O., Carbas, S. and Saka, M. (2011), "A reformulation of the ant colony optimization algorithm for large scale structural optimization", Proceedings of the 2nd International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering. Civil-Comp Press Stirlingshire.
9 Highway AAoS and Officials T. (2002), Standard specifications for highway bridges: AASHTO.
10 Islam, N., Rana, S., Ahsan, R. and Ghani, S.N. (2014), "An optimized design of network arch bridge using global optimization algorithm", Adv. Struct. Eng., 17(2), 197-210.   DOI
11 Kaveh, A., Bakhshpouri, T. and Barkhori, M. (2014), "Optimum design of multi-span composite box girder bridges using Cuckoo Search algorithm", Applications of Metaheuristic Optimization Algorithms in Civil Engineering, 31-46.
12 Kaveh, A. (2017a), Advances in Metaheuristic Algorithms for Optimal Design of Structures. Springer, Switzerland.
13 Kaveh, A. and Talatahari, S. (2010), "A novel heuristic optimization method: charged system search", Acta Mechanica, 213(3-4), 267-289.   DOI
14 Kaveh, A. (2017b), Applications of Metaheuristic Optimization Algorithms in Civil Engineering. Springer, Switzerland.
15 Kaveh, A. and Ilchi Ghazaan, M. (2015), "Hybridized optimization algorithms for design of trusses with multiple natural frequency constraints", Adv. Eng. Softw., 79, 137-147.   DOI
16 Kaveh, A. and Mahjoubi, S. (2017), "Lion Pride Optimization Algorithm: a meta-heuristic method for design optimization problems", Scientia Iranica, Submitted for publication.
17 Kaveh, A. and Zolghadr, A. (2012), "Truss optimization with natural frequency constraints using a hybridized CSS-BBBC algorithm with trap recognition capability", Comput. Struct., 102-103, 14-27.   DOI
18 Kennedy, J. and Eberhart, R. (1995), "Particle swarm optimization", Proceedings of the Neural Networks, 1995. IEEE International Conference on. 1942-1948 vol.1944.
19 Martinez F.J., Gonzalez-Vidosa, F., Hospitaler, A. and Yepes, V. (2012), "Multi-objective optimization design of bridge piers with hybrid heuristic algorithms", J. Zhejiang Univ. - Sci. A, 13(6), 420-432.   DOI
20 Martinez, F.J., Gonzalez-Vidosa, F, Hospitaler A. and Alcala, J. (2011), "Design of tall bridge piers by ant colony optimization", Eng. Struct., 33(8), 2320-2329.   DOI
21 MatLab, M. (2012), "The language of technical computing", The MathWorks, Inc. http://www.mathworks.com.
22 Mirjalili, S. (2015), "The ant lion optimizer", Adv. Eng. Softw., 83, 80-98.   DOI
23 Rao, R. (2016), "Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems", Int. J. Ind. Eng. Comput., 7(1), 19-34.
24 Mirjalili, S. (2016), "SCA: a sine cosine algorithm for solving optimization problems", Knowledge-Based Syst., 96, 120-133.   DOI
25 Mirjalili, S., Gandomi, A.H., Mirjalili, S.Z., Saremi, S. Faris, H. and Mirjalilie, S.M. (2017), "Salp swarm algorithm: a bio-inspired optimizer for engineering design problems", Adv. Eng. Softw., in press.
26 Mirjalili, S., Mirjalili, S.M. and Lewis, A. (2014), "Grey wolf optimizer", Adv. Eng. Softw., 69, 46-61.   DOI
27 Redondo, J.L. (2009), Solving competitive location problems via memetic algorithms. High performance computing approaches: Universidad Almeria.
28 Sgambi, L., Gkoumas, K. and Bontempi, F. (2012), "Genetic algorithms for the dependability assurance in the design of a long-span suspension bridge", Comput.-Aided Civil Infrastruct. Eng., 27(9), 655-675.   DOI
29 Srinivas, V. and Ramanjaneyulu, K. (2007), "An integrated approach for optimum design of bridge decks using genetic algorithms and artificial neural networks", Adv. Eng. Softw., 38(7), 475-487.   DOI
30 Stander, P.E. (1992), "Cooperative hunting in lions: the role of the individual", Behav. Ecol. Sociobiol., 29(6), 445-454.   DOI
31 Tanabe, R. and Fukunaga, A.S. (2014), "Improving the search performance of SHADE using linear population size reduction", Proceedings of the Evolutionary Computation (CEC), 2014 IEEE Congress on. IEEE.
32 Wilson, E.L. and Habibullah, A. (2002), "Structural analysis program", SAP2000. Computers and Structures Inc., California.
33 Yi, T.H., Li, H.N. and Zhang, X.D. (2012), "Sensor placement on Canton Tower for health monitoring using asynchronous-climb monkey algorithm", Smart Mater. Struct., 21(12), 125023.   DOI
34 Yang, X.S. (2010), "Firefly algorithm, stochastic test functions and design optimisation", Int. J. Bio-Inspired Comput., 2(2), 78-84.   DOI
35 Yazdani, M. and Jolai, F. (2016), "Lion optimization algorithm (LOA): A nature-inspired metaheuristic algorithm", J. Comput. Des. Eng., 3(1), 24-36.   DOI
36 Yepes, V., Marti, J.V., Garcia-Segura, T. and Gonzalez-Vidosa, F. (2017), "Heuristics in optimal detailed design of precast road bridges", Arch. Civil Mech. Eng., 17(4), 738-749.   DOI
37 Yi, T.H., Li, H.N. and Gu, M. (2011), "Optimal sensor placement for structural health monitoring based on multiple optimization strategies", Struct. Des. Tall Spec. Build., 20(7), 881-900.   DOI
38 Zhang, J. and Sanderson, A.C. (2009), "JADE: adaptive differential evolution with optional external archive", IEEE T. Evolut. Comput., 13(5), 945-958.   DOI