Abstract
Thanks to the advent of powerful computers, searching for optimal solutions to engineering design problems becomes easier every day. Numerous researchers are still developing modern optimization algorithms, and the competition for “the most efficient optimization algorithm” continues apace. This study evaluates the performances of the Crow Search Algorithm (CSA) and a slightly modified variant (CSAM) in one of the most popular and controversial competitions in the structural optimization field for the first time. Unlike most of the works on structural optimization, this paper does not tell a success story. After days of computation to collect the sensitivity and convergence data, it is shown that both CSA and CSAM mostly fail compared to today’s competitive algorithms. The findings of the study are discussed through tables and plots in detail to share the unfavorable experience on the truss optimization attempts, to review the difficulties of using parameter-controlled algorithms in structural optimization through CSA, and to save time for the researchers in the field.
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References
Abdelaziz AY, Fathy A (2017) A novel approach based on crow search algorithm for optimal selection of conductor size in radial distribution networks. Eng Sci Technol Int J 20:391–402. https://doi.org/10.1016/j.jestch.2017.02.004
Adeli H, Kamal O (1986) Efficient optimization of space trusses. Comput Struct 24:501–511. https://doi.org/10.1016/0045-7949(86)90327-5
Adhi A, Santosa B, Siswanto N (2018) A meta-heuristic method for solving scheduling problem: crow search algorithm. IOP Conf Ser Mater Sci Eng 337:012003. https://doi.org/10.1088/1757-899X/337/1/012003
Adil B, Cengiz B (2019) Optimal design of truss structures using weighted superposition attraction algorithm. Eng Comput 1:3. https://doi.org/10.1007/s00366-019-00744-x
Allaoui M, Ahiod B, El Yafrani M (2018) A hybrid crow search algorithm for solving the DNA fragment assembly problem. Expert Syst Appl 102:44–56. https://doi.org/10.1016/j.eswa.2018.02.018
Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12. https://doi.org/10.1016/j.compstruc.2016.03.001
Azad SK, Hasançebi O (2014) An elitist self-adaptive step-size search for structural design optimization. Appl Soft Comput J 19:226–235. https://doi.org/10.1016/j.asoc.2014.02.017
Azad SK, Hasançebi O (2015) Discrete sizing optimization of steel trusses under multiple displacement constraints and load cases using guided stochastic search technique. Struct Multidiscip Optim 52:383–404. https://doi.org/10.1007/s00158-015-1233-0
Azad S, Hasançebi O, Azad S, Erol O (2013) Upper bound strategy in optimum design of truss structures: a Big Bang-Big Crunch algorithm based application. Adv Struct Eng 16:1035–1046. https://doi.org/10.1260/1369-4332.16.6.1035
Bekdaş G, Nigdeli SM, Yang X-S (2015) Sizing optimization of truss structures using flower pollination algorithm. Appl Soft Comput 37:322–331. https://doi.org/10.1016/j.asoc.2015.08.037
Bekdaş G, Niğdeli SM, Yang X-S (2016) Size optimization of truss structures employing flower pollination algorithm without grouping structural members. Int J Theor Appl Mech 1:269–273
Blum C (2005) Ant colony optimization: introduction and recent trends. Phys Life Rev 2:353–373. https://doi.org/10.1016/j.plrev.2005.10.001
Camp CV (2007) Design of space trusses using Big Bang-Big Crunch optimization. J Struct Eng 133:999–1008. https://doi.org/10.1061/(ASCE)0733-9445(2007)133:7(999)
Camp CV, Bichon BJ (2004) Design of space trusses using ant colony optimization. J Struct Eng 130:741–751. https://doi.org/10.1061/(ASCE)0733-9445(2004)130:5(741)
Camp CV, Farshchin M (2014) Design of space trusses using modified teaching–learning based optimization. Eng Struct 62–63:87–97. https://doi.org/10.1016/j.engstruct.2014.01.020
Camp C, Pezeshk S, Cao G (1998) Optimized design of two-dimensional structures using a genetic algorithm. J Struct Eng 124:551–559. https://doi.org/10.1061/(ASCE)0733-9445(1998)124:5(551)
Cao H, Qian X, Chen Z, Zhu H (2017) Layout and size optimization of suspension bridges based on coupled modelling approach and enhanced particle swarm optimization. Eng Struct 146:170–183. https://doi.org/10.1016/j.engstruct.2017.05.048
Casavola C, Lamberti L, Pruncu CI (2012) Weight minimization of truss structures with Big Bang-Big Crunch. In: The 4th international conference “advanced composite materials engineering” COMAT, Brasov, pp 343–350
Charalampakis AE (2016) Comparison of metaheuristic algorithms for size optimization of trusses. In: 11th HSTAM international congress on mechanics, pp 27–30
Cheng M-Y, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112. https://doi.org/10.1016/j.compstruc.2014.03.007
Coello CA, Christiansen AD (2000) Multiobjective optimization of trusses using genetic algorithms. Comput Struct 75:647–660. https://doi.org/10.1016/S0045-7949(99)00110-8
Dede T, Bekiroğlu S, Ayvaz Y (2011) Weight minimization of trusses with genetic algorithm. Appl Soft Comput 11:2565–2575. https://doi.org/10.1016/j.asoc.2010.10.006
Degertekin SO (2012) Improved harmony search algorithms for sizing optimization of truss structures. Comput Struct 92–93:229–241. https://doi.org/10.1016/j.compstruc.2011.10.022
Degertekin SO, Hayalioglu MS (2013) Sizing truss structures using teaching–learning-based optimization. Comput Struct 119:177–188. https://doi.org/10.1016/j.compstruc.2012.12.011
Díaz P, Pérez-Cisneros M, Cuevas E et al (2018) An improved crow search algorithm applied to energy problems. Energies 11:571. https://doi.org/10.3390/en11030571
Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theor Comput Sci 344:243–278. https://doi.org/10.1016/j.tcs.2005.05.020
Dorigo M, Di Caro G (1999) Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406), IEEE, pp 1470–1477
Erol OK, Eksin I (2006) A new optimization method: Big Bang-Big Crunch. Adv Eng Softw 37:106–111. https://doi.org/10.1016/j.advengsoft.2005.04.005
Frans R, Arfiadi Y (2014) Sizing, shape, and topology optimizations of roof trusses using hybrid genetic algorithms. Procedia Eng 95:185–195. https://doi.org/10.1016/j.proeng.2014.12.178
Geem Zong Woo, Kim Joong Hoon, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76:60–68. https://doi.org/10.1177/003754970107600201
Glover F, Kochenberger GA (2003) Handbook of metaheuristics. Kluwer Academic Publishers, Boston
Habachi R, Touil A, Charkaoui A, Echchatbi A (2018) Eagle strategy based crow search algorithm for solving unit commitment problem in smart grid system. Indones J Electr Eng Comput Sci 12:17–29. https://doi.org/10.11591/ijeecs.v12.i1
Hasançebi O, Azad SK (2013) Reformulations of Big Bang-Big Crunch algorithm for discrete structural design optimization. Int J Civ Environ Struct Constr Archit Eng 7:139–150
Hasançebi O, Kazemzadeh Azad S (2014) Discrete size optimization of steel trusses using a refined Big Bang-Big Crunch algorithm. Eng Optim 46:61–83. https://doi.org/10.1080/0305215X.2012.748047
Hasançebi O, Çarbaş S, Doğan E et al (2009) Performance evaluation of metaheuristic search techniques in the optimum design of real size pin jointed structures. Comput Struct 87:284–302. https://doi.org/10.1016/j.compstruc.2009.01.002
Heppner FH, Grenander U (1990) A stochastic nonlinear model for coordinated bird flocks. In: Krasner S (ed) The ubiquity of chaos. AAAS Publications, pp 233–238
Ho-Huu V, Nguyen-Thoi T, Vo-Duy T, 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
Jafari M, Salajegheh E, Salajegheh J (2019) An efficient hybrid of elephant herding optimization and cultural algorithm for optimal design of trusses. Eng Comput 35:781–801. https://doi.org/10.1007/s00366-018-0631-5
Kaveh A (2014) Advances in metaheuristic algorithms for optimal design of structures. Springer, Cham
Kaveh A (2017) Applications of metaheuristic optimization algorithms in civil engineering, 2nd edn. Springer, Cham
Kaveh A, Mahdavi VR (2013) Optimal design of structures with multiple natural frequency constraints using a hybridized BB-BC/quasi-Newton algorithm. Period Polytech Civ Eng 57:27. https://doi.org/10.3311/PPci.2139
Kaveh A, Mahdavi VR (2014) Colliding bodies optimization method for optimum design of truss structures with continuous variables. Adv Eng Softw 70:1–12. https://doi.org/10.1016/j.advengsoft.2014.01.002
Kaveh A, Talatahari S (2009a) Size optimization of space trusses using Big Bang-Big Crunch algorithm. Comput Struct 87:1129–1140. https://doi.org/10.1016/j.compstruc.2009.04.011
Kaveh A, Talatahari S (2009b) Particle swarm optimizer, ant colony strategy and harmony search scheme hybridized for optimization of truss structures. Comput Struct 87:267–283. https://doi.org/10.1016/j.compstruc.2009.01.003
Kaveh A, Talatahari S (2010a) Optimal design of Schwedler and ribbed domes via hybrid Big Bang-Big Crunch algorithm. J Constr Steel Res 66:412–419. https://doi.org/10.1016/j.jcsr.2009.10.013
Kaveh A, Talatahari S (2010b) A discrete Big Bang-Big Crunch algorithm for optimal design of skeletal structures. Asian J Civ Eng 11:103–122
Kaveh A, Zakian P (2018) Improved GWO algorithm for optimal design of truss structures. Eng Comput 34:685–707. https://doi.org/10.1007/s00366-017-0567-1
Kaveh A, 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. https://doi.org/10.1016/j.compstruc.2012.03.016
Kaveh A, Sheikholeslami R, Talatahari S, Keshvari-Ilkhichi M (2014) Chaotic swarming of particles: a new method for size optimization of truss structures. Adv Eng Softw 67:136–147. https://doi.org/10.1016/j.advengsoft.2013.09.006
Kazemzadeh Azad S (2017) Enhanced hybrid metaheuristic algorithms for optimal sizing of steel truss structures with numerous discrete variables. Struct Multidiscip Optim 55:2159–2180. https://doi.org/10.1007/s00158-016-1634-8
Kazemzadeh Azad S, Hasançebi O, Saka MP (2014) Guided stochastic search technique for discrete sizing optimization of steel trusses: a design-driven heuristic approach. Comput Struct 134:62–74. https://doi.org/10.1016/j.compstruc.2014.01.005
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95—international conference on neural networks, IEEE, pp 1942–1948
Khatibinia M, Yazdani H (2017) Accelerated multi-gravitational search algorithm for size optimization of truss structures. Swarm Evol Comput. https://doi.org/10.1016/j.swevo.2017.07.001
Kumar S, Tejani GG, Mirjalili S (2018) Modified symbiotic organisms search for structural optimization. Eng Comput 1:3. https://doi.org/10.1007/s00366-018-0662-y
Lamberti L, Pappalettere C (2000) Comparison of the numerical efficiency of different sequential linear programming based algorithms for structural optimisation problems. Comput Struct 76:713–728. https://doi.org/10.1016/S0045-7949(99)00185-6
Lee KS, Geem ZW (2004) A new structural optimization method based on the harmony search algorithm. Comput Struct 82:781–798. https://doi.org/10.1016/j.compstruc.2004.01.002
Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng 194:3902–3933. https://doi.org/10.1016/j.cma.2004.09.007
Lee KS, Geem ZW, Lee S, Bae K (2005) The harmony search heuristic algorithm for discrete structural optimization. Eng Optim 37:663–684. https://doi.org/10.1080/03052150500211895
Li LJ, Huang ZB, Liu F, Wu QH (2007) A heuristic particle swarm optimizer for optimization of pin connected structures. Comput Struct 85:340–349. https://doi.org/10.1016/j.compstruc.2006.11.020
Li LJ, Huang ZB, Liu F (2009) A heuristic particle swarm optimization method for truss structures with discrete variables. Comput Struct 87:435–443. https://doi.org/10.1016/j.compstruc.2009.01.004
Lotfi H, Ghoddosian A (2015) Size and shape optimization of two-dimensional trusses using hybrid Big Bang-Big Crunch algorithm. Int J Mechatron Electr Comput Technol 5:1987–1998
Luh G-C, Lin C-Y (2011) Optimal design of truss-structures using particle swarm optimization. Comput Struct 89:2221–2232. https://doi.org/10.1016/j.compstruc.2011.08.013
Milajić A, Beljaković D (2016) Multi-objective truss optimization using different types of the BB-BC algorithm. MATEC Web Conf 73:04012. https://doi.org/10.1051/matecconf/20167304012
Milajić A, Beljaković D, Barović D (2014) Optimum truss design using Big Bang-Big Crunch algorithm. In: International conference of contemporary achievements in civil engineering, Subotica, pp 447–453
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007
Nobahari H, Bighashdel A (2017) MOCSA: a multi-objective crow search algorithm for multi-objective optimization. In: 2017 2nd conference on swarm intelligence and evolutionary computation (CSIEC), IEEE, pp 60–65
Ozbasaran H (2017) solveTruss v1.0: static, global buckling and frequency analysis of 2D and 3D trusses with Mathematica. SoftwareX 6:135–140. https://doi.org/10.1016/j.softx.2017.05.004
Ozbasaran H (2018) Optimal design of I-section beam-columns with stress, non-linear deflection and stability constraints. Eng Struct 171:385–394. https://doi.org/10.1016/j.engstruct.2018.05.110
Özbaşaran H (2018) A study on size optimization of trusses with BB-BC algorithm: review and numerical experiments. Afyon Kocatepe Univ J Sci Eng 18:256–264. https://doi.org/10.5578/fmbd.66584
Rajeev S, Krishnamoorthy CS (1992) Discrete optimization of structures using genetic algorithms. J Struct Eng 118:1233–1250. https://doi.org/10.1061/(ASCE)0733-9445(1992)118:5(1233)
Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Des 43:303–315. https://doi.org/10.1016/j.cad.2010.12.015
Reeves WT (1983) Particle systems—technique for modeling a class of fuzzy objects. ACM SIGGRAPH Comput Graph 17:359–375. https://doi.org/10.1145/964967.801167
Reynolds CW (1987) Flocks, herds and schools: a distributed behavioral model. ACM SIGGRAPH Comput Graph 21:25–34. https://doi.org/10.1145/37402.37406
Richardson JN, Adriaenssens S, Bouillard P, Filomeno Coelho R (2012) Multiobjective topology optimization of truss structures with kinematic stability repair. Struct Multidiscip Optim 46:513–532. https://doi.org/10.1007/s00158-012-0777-5
Saha A, Bhattacharya A, Das P, Chakraborty AK (2017) Crow search algorithm for solving optimal power flow problem. In: 2017 second international conference on electrical, computer and communication technologies (ICECCT), IEEE, pp 1–8
Sahu S, Behera DK (2018) Comparison analysis for the machine scheduling using crow search algorithm (CSA) and PSO. Int J Mech Eng Technol 9:170–177
Sayed GI, Darwish A, Hassanien AE (2017) Chaotic crow search algorithm for engineering and constrained problems. In: 2017 12th international conference on computer engineering and systems (ICCES), IEEE, pp 676–681
Sayed GI, Hassanien AE, Azar AT (2019) Feature selection via a novel chaotic crow search algorithm. Neural Comput Appl 31:171–188. https://doi.org/10.1007/s00521-017-2988-6
Sonmez M (2011) Artificial bee colony algorithm for optimization of truss structures. Appl Soft Comput 11:2406–2418. https://doi.org/10.1016/j.asoc.2010.09.003
Stander N, Snyman JA, Coster JE (1995) On the robustness and efficiency of the SAM algorithm for structural optimization. Int J Numer Methods Eng 38:119–135. https://doi.org/10.1002/nme.1620380108
Tahir DS, Ali RS (2017) A chaotic crow search algorithm for high-dimensional optimization problems. Basrah J Eng Sci 17:16–25
Tapao A, Cheerarot R (2017) Optimal parameters and performance of artificial bee colony algorithm for minimum cost design of reinforced concrete frames. Eng Struct 151:802–820. https://doi.org/10.1016/j.engstruct.2017.08.059
Toğan V, Daloğlu AT (2008) An improved genetic algorithm with initial population strategy and self-adaptive member grouping. Comput Struct 86:1204–1218. https://doi.org/10.1016/j.compstruc.2007.11.006
Venkata Rao R (2016) Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput. https://doi.org/10.5267/j.ijiec.2015.8.004
Wang H, Ohmori H (2013) Elasto-plastic analysis based truss optimization using genetic algorithm. Eng Struct 50:1–12. https://doi.org/10.1016/j.engstruct.2013.01.010
Wolfram Research Inc. (2016) Mathematica
Wu S-J, Chow P-T (1995) Steady-state genetic algorithms for discrete optimization of trusses. Comput Struct 56:979–991. https://doi.org/10.1016/0045-7949(94)00551-D
Yang X-S (2010a) Nature-inspired metaheuristic algorithms, 2nd edn. Luniver Press, Beckington
Yang X-S (2010b) A new metaheuristic bat-inspired algorithm. In: González JR, Pelta DA, Cruz C, Terrazas G, Krasnogor N (eds) Studies in computational intelligence. Springer, Berlin, pp 65–74
Yang XS (2010c) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio Inspired Comput 2:78. https://doi.org/10.1504/IJBIC.2010.032124
Yang X-S, Suash Deb (2009) Cuckoo search via Lévy flights. In: 2009 world congress on nature & biologically inspired computing (NaBIC), IEEE, pp 210–214
Zaki DA, Hasanien HM, El-Amary NH, Abdelaziz AY (2017) Crow search algorithm for improving the performance of an inverter-based distributed generation system. In: 2017 nineteenth international middle east power systems conference (MEPCON), IEEE, pp 656–663
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This study was supported by the Scientific Research Projects Fund of Eskisehir Osmangazi University by the Project Number: 2019-3007.
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Ozbasaran, H., Eryilmaz Yildirim, M. Truss-sizing optimization attempts with CSA: a detailed evaluation. Soft Comput 24, 16775–16801 (2020). https://doi.org/10.1007/s00500-020-04972-y
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DOI: https://doi.org/10.1007/s00500-020-04972-y