A Minimum Cost Design Approach for Steel Frames Based on a Parallelized Firefly Algorithm and Parameter Control

  1. Sánchez-Olivares, Gregorio 1
  2. Tomás, Antonio 1
  3. García-Ayllón, Salvador 1
  1. 1 Department of Mining and Civil Engineering, Universidad Politécnica de Cartagena (UPCT), Paseo Alfonso XIII, 52, 30203 Cartagena, Murcia, Spain
Zeitschrift:
Applied Sciences

ISSN: 2076-3417

Datum der Publikation: 2023

Ausgabe: 13

Nummer: 21

Seiten: 11801

Art: Artikel

DOI: 10.3390/APP132111801 GOOGLE SCHOLAR lock_openOpen Access editor

Andere Publikationen in: Applied Sciences

Ziele für nachhaltige Entwicklung

Zusammenfassung

In this work, the applicability of a Firefly Algorithm (FA) to the real problem of the minimum cost of a detailed design for steel frames is studied. To reduce the calculation time, which is a common problem of meta-heuristic algorithms when they are used to solve real design cases, and to better suit the characteristics of the algorithm, a parallel migration strategy has been implemented and tested. As it is well known that the performance of any metaheuristic algorithm depends on the chosen value of its parameters, an extensive sensitivity analysis has been carried out. This not only serves to improve performance but also provides information on how it depends on the values of these parameters. With the information obtained from this analysis, and in order to achieve the robust behavior of the algorithm, a parameter control strategy has also been implemented and tested. Finally, a study demonstrating the close dependence between one of the parameters and the number of variables considered in the examples has been carried out. As a result of this final study, a simple expression is proposed that provides the minimum necessary population based on the number of variables in the problem.

Bibliographische Referenzen

  • Yang, X.-S. (2021). Nature-Inspired Optimization Algorithms, Elsevier. [2nd ed.].
  • Wolpert, (1997), IEEE Trans. Evol. Comput., 1, pp. 67, 10.1109/4235.585893
  • Wolpert, (2013), Ubiquity, 2013, pp. 1, 10.1145/2555235.2555237
  • Joshi, (2020), Knowl.-Based Syst., 189, pp. 105094, 10.1016/j.knosys.2019.105094
  • Jourdan, (2016), Appl. Soft Comput., 41, pp. 515, 10.1016/j.asoc.2015.12.044
  • Younis, (2018), Appl. Soft Comput., 72, pp. 498, 10.1016/j.asoc.2018.05.032
  • Watanabe, (2009), Stochastic Algorithms: Foundations and Applications, SAGA 2009, Volume 5792, pp. 169
  • Fister, (2013), Swarm Evol. Comput., 13, pp. 34, 10.1016/j.swevo.2013.06.001
  • Yang, (2014), Cuckoo Search and Firefly Algorithm. Theory and Applications, Volume 516, pp. 1
  • Leite, (1999), Comput. Struct., 73, pp. 545, 10.1016/S0045-7949(98)00255-7
  • Thierauf, (1997), Eng. Struct., 19, pp. 318, 10.1016/S0141-0296(96)00076-4
  • Yang, (2014), Cuckoo Search and Firefly Algorithm. Theory and Applications, Volume 516, pp. 291
  • Saka, (2011), Comput. Struct., 89, pp. 2037, 10.1016/j.compstruc.2011.05.006
  • Truong, (2017), J. Constr. Steel Res., 128, pp. 416, 10.1016/j.jcsr.2016.09.013
  • Huang, (2014), J. Intell. Robot. Syst., 76, pp. 475488, 10.1007/s10846-013-9884-9
  • Melin, (2015), Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization, Volume 601, pp. 391, 10.1007/978-3-319-17747-2_30
  • Pan, (2017), Advances in Intelligent Information Hiding and Multimedia Signal Processing, Smart Innovation, Systems and Technologies, Volume 64, pp. 297
  • Gandomi, (2011), Comput. Struct., 89, pp. 2325, 10.1016/j.compstruc.2011.08.002
  • Talatahari, (2012), Struct. Des. Tall Spec. Build., 23, pp. 350, 10.1002/tal.1043
  • Talbi, E. (2009). Metaheuristics: From Design to Implementation, John Wiley & Sons.
  • (2013), J. Constr. Steel Res., 88, pp. 267, 10.1016/j.jcsr.2013.05.023
  • Eiben, (2011), Swarm Evol. Comput., 1, pp. 19, 10.1016/j.swevo.2011.02.001
  • Sánchez-Olivares, G., and Tomás, A. (2021). Optimization of Reinforced Concrete Sections under Compression and Biaxial Bending by Using a Parallel Firefly Algorithm. Appl. Sci., 11.
  • (2005). Eurocode 3 Design of Steel Structures—Part 1—1: General Rules and Rules for Buildings. Standard No. EN 1993-1-1:2005.
  • (2017), Eng. Struct., 130, pp. 162, 10.1016/j.engstruct.2016.10.010
  • Yang, (2013), Comput. Oper. Res., 40, pp. 1616, 10.1016/j.cor.2011.09.026
  • Gandomi, (2013), Commun. Nonlinear Sci. Numer. Simul., 18, pp. 89, 10.1016/j.cnsns.2012.06.009
  • Lobo, (2007), Parameter Setting in Evolutionary Algorithms. Studies in Computational Intelligence, Volume 54, pp. 1, 10.1007/978-3-540-69432-8_1
  • Gandomi, (2015), Eng. Struct., 103, pp. 72, 10.1016/j.engstruct.2015.08.034
  • Morris, (1991), Technometrics, 33, pp. 161, 10.1080/00401706.1991.10484804
  • Liang, J., Qu, B., Suganthan, P., and Chen, Q. (2015). Problem Definitions and Evaluation Criteria for the Cec 2015 Competition on Learning-Based Real-Parameter Single Objective Optimization, Nanyang Technological University.
  • Cabrero, (2005), Eng. Struct., 27, pp. 1125, 10.1016/j.engstruct.2005.02.017
  • Ali, (2009), Eng. Struct., 31, pp. 2766, 10.1016/j.engstruct.2009.07.004
  • Xu, (1993), J. Struct. Eng., 119, pp. 1740, 10.1061/(ASCE)0733-9445(1993)119:6(1740)
  • Foley, (2003), J. Struct. Eng., 129, pp. 648, 10.1061/(ASCE)0733-9445(2003)129:5(648)
  • Kameshki, (2003), J. Constr. Steel Res., 59, pp. 109, 10.1016/S0143-974X(02)00021-4
  • Benavides, E.M. (2012). Advanced Engineering Design. An Integrated Approach, Woodhead Publishing.
  • Harte, (2014), Trends Ecol. Evol., 29, pp. 384, 10.1016/j.tree.2014.04.009
  • Souza de Cursi, E., and Sampaio, R. (2015). Uncertainty Quantification and Stochastic Modeling with Matlab, Elsevier.