Structural Engineering and Mechanics

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Solving structural optimization problems with discrete variables using interactive fuzzy search algorithm

  • Mortazavi, Ali (Graduate School of Natural and Applied Science, Ege University)
  • Received : 2019.06.18
  • Accepted : 2021.06.09
  • Published : 2021.07.25
⟨ Previous Next ⟩

Abstract

The current investigation deals with assessing the search performance of a recently developed, parameter-free, and self-adaptive search algorithm so-called Interactive Fuzzy Search Algorithm (IFSA) in solving weight minimization of the constrained structural optimization problems with discrete variables. The proposed IFSA combines the navigation pattern of the Interactive Search Algorithm (ISA) with the decision-making competence of fuzzy reasoning. The fuzzy module of the proposed IFSA permanently monitors the search process and adjusts each agent's search behavior by considering the governing condition of the current problem. In structural optimization, due to construction limitations, it is more realistic to select the sizing variables from a discrete domain. Thus, in this study, to empirically evaluate the search capability of the IFSA, it is applied to solve a suite of structural optimization problems with the discrete design variables. The attained outcomes are compared with the ISA and some other related methods addressed in the relevant literature. The acquired accuracy level and demanded number of objective function evaluations indicates that the IFSA, comparatively, using lower computational cost could found lighter structural systems. Also, the comparison of the attained standard deviation values shows that the IFSA demonstrates higher stability during the optimization process. These superior outcomes designate that the fuzzy decision-making mechanism of the IFSA could work properly in dynamically adapting the search behavior of the algorithm with the governing condition of the current problem. Consequently, the promising gained results reveal that IFSA can effectively be applied in solving the structural optimization problems with discrete search domains.

Keywords

References

  1. AISC (1989), American Institute of Steel Construction (AISC). Manual of Steel Construction Allowable Stress Design, 9th Edition, Chicago, IL. http://doi.org/10.1002/9781118631201.
  2. Artar, M. and Daloglu Ayse, T. (2019), "Optimum design of steel space truss towers under seismic effect using Jaya algorithm", Struct. Eng. Mech., 71(1), 1-12. http://doi.org/10.12989/sem.2019.71.1.001.
  3. Assimi, H., Jamali, A. and Nariman-zadeh, N. (2017), "Sizing and topology optimization of truss structures using genetic programming", Swarm Evolution. Comput., 37, 90-103. http://doi.org/10.1016/j.swevo.2017.05.009.
  4. Azad, S.K. and Hasancebi, O. (2014), "An elitist self-adaptive step-size search for structural design optimization", Appl. Soft Comput., 19, 226-235. https://doi.org/10.1016/j.asoc.2014.02.017.
  5. Baykasoglu, A. and Akpinar, S. (2015), "Weighted Superposition Attraction (WSA): A swarm intelligence algorithm for optimization problems-Part 2: Constrained optimization", Appl. Soft Comput., 37, 396-415. https://doi.org/10.1016/j.asoc.2015.08.052.
  6. Camp, C. (2007), "Design of space trusses using big bang-big crunch optimization", J. Struct. Eng., 133(7), 999-1008. https://doi.org/10.1061/(ASCE)0733-9445(2007)133:7(999).
  7. Capriles, P., Goliatt, L., Barbosa, H. and Lemonge, A. (2006), "Rank-based ant colony algorithms for truss weight minimization with discrete variables", Commun. Numer. Meth. Eng., 23, 553-575. https://doi.org/10.1002/cnm.912.
  8. Cheng, M.Y., Prayogo, D., Wu, Y.W. and Lukito, M.M. (2016), "A Hybrid Harmony Search algorithm for discrete sizing optimization of truss structure", Auto. Constr., 69, 21-33. http://doi.org/10.1016/j.autcon.2016.05.023.
  9. Coello Coello, C.A. (2000), "Use of a self-adaptive penalty approach for engineering optimization problems", Comput. Indus., 41(2), 113-127. http://doi.org/10.1016/S0166-3615(99)00046-9.
  10. Das, K.N. and Singh, T.K. (2014), "Drosophila food-search optimization", Appl. Math. Comput., 231, 566-580. http://doi.org/10.1016/j.amc.2014.01.040.
  11. Deng, W., Chen, R., He, B., Liu, Y., Yin, L. and Guo, J. (2012), "A novel two-stage hybrid swarm intelligence optimization algorithm and application", Soft Comput., 16(10), 1707-1722. https://doi.org/10.1007/s00500-012-0855-z.
  12. Dorigo, M. and Blum, C. (2005), "Ant colony optimization theory: A survey", Theor. Comput. Sci., 344(2), 243-278. http://doi.org/10.1016/j.tcs.2005.05.020.
  13. Fan, Q. and Yan, X. (2014), "Self-adaptive particle swarm optimization with multiple velocity strategies and its application for p-Xylene oxidation reaction process optimization", Chemomet. Intel. Lab. Syst., 139, 15-25. http://doi.org/10.1016/j.chemolab.2014.09.002.
  14. Ghambari, S. and Rahati, A. (2018), "An improved artificial bee colony algorithm and its application to reliability optimization problems", Appl. Soft Comput., 62, 736-767. https://doi.org/10.1016/j.asoc.2017.10.040.
  15. Groenwold, A., Stander, N. and Snyman, J. (1999), "A regional genetic algorithm for the discrete optimal design of truss structures", Int. J. Numer. Meth. Eng., 44, 749-766. https://doi.org/10.1002/(SICI)1097- 0207(19990228)44:6<749::AID-NME523>3.0.CO;2-F.
  16. Groenwold, A.A. and Stander, N. (1997), "Optimal discrete sizing of truss structures subject to buckling constraints", Struct. Optim., 14(2), 71-80. https://doi.org/10.1007/BF01812508.
  17. Haftka, R.T. (2016), "Requirements for papers focusing on new or improved global optimization algorithms", Struct. Multidisc. Optim., 54(1), 1-1. https://doi.org/10.1007/s00158-016-1491-5.
  18. Hasancebi, O. (2008), "Adaptive evolution strategies in structural optimization: Enhancing their computational performance with applications to large-scale structures", Comput. Struct., 86(1-2), 119-132. http://doi.org/10.1016/j.compstruc.2007.05.012.
  19. Hasancebi, O., Teke, T. and Pekcan, O. (2013), "A bat-inspired algorithm for structural optimization", Comput. Struct., 128, 77-90. http://doi.org/10.1016/j.compstruc.2013.07.006.
  20. Ho-Huu, V., Nguyen-Thoi, T., Vo-Duy, T. and Nguyen-Trang, T. (2016), "An adaptive elitist differential evolution for optimization of truss structures with discrete design variables", Comput. Struct., 165, 59-75. https://doi.org/10.1016/j.compstruc.2015.11.014.
  21. Hussain, K., Salleh, M.N.M., Cheng, S. and Shi, Y. (2019), "On the exploration and exploitation in popular swarm-based metaheuristic algorithms", Neur. Comput. Appl., 31(11), 7665-7683. https://doi.org/10.1007/s00521-018-3592-0.
  22. Juang, D.S. and Chang, W.T. (2006), "A revised discrete Lagrangian-based search algorithm for the optimal design of skeletal structures using available sections", Struct. Multidisc. Optim., 31(3), 201-210. https://doi.org/10.1007/s00158-005-0571-8.
  23. Kalatjari, V. and Talebpour, M.H. (2009), "Reducing the effect of GA parameters on optimization of topology and cross section for truss structures using multi-search-method", J. Technol. Edu., 4(1), 57-72. https://doi.org/10.3311/PPci.8222.
  24. Kaveh, A. and Ilchi Ghazaan, M. (2015), "A comparative study of CBO and ECBO for optimal design of skeletal structures", Comput. Struct., 153, 137-147. https://doi.org/10.1016/j.compstruc.2015.02.028.
  25. Kaveh, A. and Talatahari, S. (2009), "A particle swarm ant colony optimization for truss structures with discrete variables", J. Constr. Steel Res., 65(8-9), 1558-1568. http://doi.org/10.1016/j.jcsr.2009.04.021.
  26. Kennedy, J. and Eberhart, R. (1995), "Particle swarm optimization", Proceedings of ICNN'95-International Conference on Neural Networks, 4, November.
  27. Kureta, R. and Kanno, Y. (2014), "A mixed integer programming approach to designing periodic frame structures with negative Poisson's ratio", Optim. Eng., 15(3), 773-800. http://doi.org/10.1007/s11081-013-9225-7.
  28. Le, D.T., Bui, D.K., Ngo, T.D., Nguyen, Q.H. and Nguyen-Xuan, H. (2019), "A novel hybrid method combining electromagnetism-like mechanism and firefly algorithms for constrained design optimization of discrete truss structures", Comput. Struct., 212, 20-42. https://doi.org/10.1016/j.compstruc.2018.10.017.
  29. Li, L.J., Huang, Z.B. and Liu, F. (2009), "A heuristic particle swarm optimization method for truss structures with discrete variables", Comput. Struct., 87(7-8), 435-443. http://doi.org/10.1016/j.compstruc.2009.01.004.
  30. Liu, H., Cai, Z. and Wang, Y. (2010), "Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization", Appl. Soft Comput., 10(2), 629-640. https://doi.org/10.1016/j.asoc.2009.08.031.
  31. Mlakar, U., Fister, I. and Fister, I. (2016), "Hybrid self-adaptive cuckoo search for global optimization", Swarm Evol. Comput., 29, 47-72. https://doi.org/10.1016/j.swevo.2016.03.001.
  32. Moloodpoor, M., Mortazavi, A. and Ozbalta, N. (2021), "Thermo-economic optimization of double pipe heat exchanger using a compound swarm intelligence", Heat Transf. Res., https://doi.org/10.1615/HeatTransRes.2021037293.
  33. Mortazavi, A. (2019), "Interactive fuzzy search algorithm: A new self-adaptive hybrid optimization algorithm", Eng. Appl. Artif. Intel., 81, 270-282. https://doi.org/10.1016/j.engappai.2019.03.005.
  34. Mortazavi, A. (2020), "Large-scale structural optimization using a fuzzy reinforced swarm intelligence algorithm", Adv. Eng. Softw., 142, 102790. https://doi.org/10.1016/j.advengsoft.2020.102790.
  35. Mortazavi, A. (2020), "A new fuzzy strategy for size and topology optimization of truss structures", Appl. Soft Comput., 93, 106412. https://doi.org/10.1016/j.asoc.2020.106412.
  36. Mortazavi, A. (2020), "Size and layout optimization of truss structures with dynamic constraints using the interactive fuzzy search algorithm", Eng. Optim., 1-23. https://doi.org/10.1080/0305215X.2020.1726341.
  37. Mortazavi, A. (2021), "Bayesian interactive search algorithm: a new probabilistic swarm intelligence tested on mathematical and structural optimization problems", Adv. Eng. Softw., 155, 102994. https://doi.org/10.1016/j.advengsoft.2021.102994.
  38. Mortazavi, A. and Togan, V. (2021), Metaheuristic Algorithms for Optimal Design of Truss Structures, Springer International Publishing, Cham.
  39. Mortazavi, A. and Togan, V. (2017), "Sizing and layout design of truss structures under dynamic and static constraints with an integrated particle swarm optimization algorithm", Appl. Soft Comput., 51, 239-252. http://doi.org/10.1016/j.asoc.2016.11.032.
  40. Mortazavi, A., Togan, V., Daloglu, A. and Nuhoglu, A. (2018), "Comparison of two metaheuristic algorithms on sizing and topology optimization of trusses and mathematical functions", Gazi Univ. J. Sci., 31(2), 416-435. https://doi.org/10.1016/j.engappai.2018.03.003.
  41. Mortazavi, A., Togan, V. and Moloodpoor, M. (2019), "Solution of structural and mathematical optimization problems using a new hybrid swarm intelligence optimization algorithm", Adv. Eng. Softw., 127, 106-123. https://doi.org/10.1016/j.advengsoft.2018.11.004.
  42. Mortazavi, A., Togan, V. and Nuhoglu, A. (2017), "An integrated particle swarm optimizer for optimization of truss structures with discrete variables", Struct. Eng. Mech., 61, 359-370. https://doi.org/10.12989/sem.2017.61.3.359.
  43. Mortazavi, A., Togan, V. and Nuhoglu, A. (2017), "Weight minimization of truss structures with sizing and layout variables using integrated particle swarm optimize", J. Civil Eng. Manage., 23(8), 985-1001. https://doi.org/10.3846/13923730.2017.1348982.
  44. Mortazavi, A., Togan, V. and Nuhoglu, A. (2018), "Interactive search algorithm: A new hybrid metaheuristic optimization algorithm", Eng. Appl. Artif. Intel., 71, 275-292. https://doi.org/10.1016/j.engappai.2018.03.003.
  45. Oftadeh, R., Mahjoob, M.J. and Shariatpanahi, M. (2010), "A novel meta-heuristic optimization algorithm inspired by group hunting of animals: Hunting search", Comput. Math. Appl., 60(7), 2087-2098. http://doi.org/10.1016/j.camwa.2010.07.049.
  46. Omidvar, M.N., Yang, M., Mei, Y., Li, X. and Yao, X. (2017), "DG2: A faster and more accurate differential grouping for large-scale black-box optimization", IEEE Tran. Evol. Comput., 21(6), 929-942. http://doi.org/10.1109/TEVC.2017.2694221.
  47. Rao, R.V., Savsani, V.J. and Vakharia, D.P. (2011), "Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems", Comput. Aid. Des., 43(3), 303-315. http://doi.org/10.1016/j.cad.2010.12.015.
  48. Sadollah, A., Bahreininejad, A., Eskandar, H. and Hamdi, M. (2012), "Mine blast algorithm for optimization of truss structures with discrete variables", Comput. Struct., 102, 49-63. http://doi.org/10.1016/j.compstruc.2012.03.013.
  49. Sadollah, A., Eskandar, H., Bahreininejad, A. and Kim, J.H. (2015), "Water cycle, mine blast and improved mine blast algorithms for discrete sizing optimization of truss structures", Comput. Struct., 149, 1-16. ttp://doi.org/10.1016/j.compstruc.2014.12.003.
  50. Sergeyev, O. and Mroz, Z. (2000), "Sensitivity analysis and optimal design of 3D frame structures for stress and frequency constraints", Comput. Struct., 75(2), 167-185. http://doi.org/10.1016/S0045-7949(99)00088-7.
  51. Storn, R. and Price, K. (1997), "Differential evolution - A aimple and efficient heuristic for global optimization over continuous spaces", J. Global Optim., 11(4), 341-359. http://doi.org/10.1023/a:1008202821328.
  52. Suganthan, P.N., Hansen, N., Liang, J.J., Deb, K., Chen, Y., Auger, A. and Tiwari, S. (2005), "Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization", Technical Report, Nanyang Technological University, Singapore, May 2005 AND KanGAL Report 2005005, IIT Kanpur, India.
  53. Talebpour, M.H., Kaveh, A. and Kalatjari, V. (2014), "Optimization of skeletal structures using a hybridized ant colony-harmony search-genetic algorithm", Iran. J. Sci. Technol.-Tran. Civil Eng., 38, 1-20. http://doi.org/10.22099/IJSTC.2014.1840.
  54. Tang, K., Li, Z., Luo, L. and Liu, B. (2015), "Multi-strategy adaptive particle swarm optimization for numerical optimization", Eng. Appl. Artif. Intel., 37, 9-19. http://doi.org/10.1016/j.engappai.2014.08.002.
  55. Wu, S.J. and Chow, P.T. (1995), "Steady-state genetic algorithms for discrete optimization of trusses", Comput. Struct., 56(6), 979-991. http://doi.org/10.1016/0045-7949(94)00551-D.
  56. Zhang, S., Luo, Q. and Zhou, Y. (2017), "Hybrid grey wolf optimizer using elite opposition-based learning strategy and simplex method", Int. J. Comput. Intel. Appl., 16(2), 175-187. http://doi.org/10.1142/s1469026817500122.