Enhanced imperialist competitive algorithm for optimal structural design

Document Type : Article

Authors

Department of Civil Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran

Abstract

Solution of complex engineering problems by meta-heuristics, requires powerful operators to maintain sufficient diversification as well as proper intensification during the search. Standard
Imperialist Competitive Algorithm, ICA, delays the search intensification by propagating it via a number of artificial empires that compete each other until one concurs the others. An Enhanced Imperialist Competitive Algorithm is developed here by adding an evolutionary operator to the standard ICA followed by greedy replacement; in order to improve its effectiveness. The new operator introduces a walk step directed from the less fit to the fitter individual in each pair of the search agents together with a random scaling and pick up scheme. EICA performance is then compared with ICA as well as GA, PSO, DE, CBO, TLBO, SOS; first in a set of fifteen test functions. Second, a variety of continuous and discrete engineering benchmarks and structural sizing problems are solved to evaluate EICA in constrained optimization. In this regard, a diversity index is traced as well as the other convergence metrics. The results exhibit considerable improvement of the algorithm by the proposed features of EICA and its competitive performance with respect to the other treated methods.

Keywords

References

References
 1.        Wolpert, D.H., Macready, W.G., No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1, 67–82 (1997). doi:10.1109/4235.585893
2.        Binitha, S., Sathya, S.S., A Survey of Bio inspired Optimization Algorithms. Int. J. Soft Comput. Eng. 2, 137–151 (2012)
3.        Kaveh, A., Ilchi Ghazaan, M., Meta-heuristic Algorithms for Optimal Design of Real-Size Structures. Springer International Publishing (2018)
4.        Atashpaz-Gargari, E., Lucas, C., Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. 2007 IEEE Congr. Evol. Comput. CEC 2007. 4661–4667 (2007). doi:10.1109/CEC.2007.4425083
5.        Hosseini, S., Al Khaled, A., A survey on the Imperialist Competitive Algorithm metaheuristic: Implementation in engineering domain and directions for future research. Appl. Soft Comput. J. 24, 1078–1094 (2014). doi:10.1016/j.asoc.2014.08.024
6.        Lin, J.-L., Tsai, Y.-H., Yu, C.-Y., Li, M.-S., Interaction Enhanced Imperialist Competitive Algorithms. Algorithms. 5, 433–448 (2012)
7.        Talatahari, S., Kaveh, A., Sheikholeslami, R., Chaotic imperialist competitive algorithm for optimum design of truss structures. Struct. Multidiscip. Optim. 46, 355–367 (2012). doi:10.1007/s00158-011-0754-4
8.        Bahrami, H., Faez, K., Abdechiri, M., Imperialist competitive algorithm using chaos theory for optimization: (CICA). UKSim2010 - UKSim 12th Int. Conf. Comput. Model. Simul. 98–103 (2010). doi:10.1109/UKSIM.2010.26
9.        Atashpaz-Gargari, E., Imperialist competitive algorithm (ICA); http://www.mathworks.com/matlabcentral/fileexchange/22046-imperialist-competitive-algorithm-ica (accessed on 10 January 2012).
10.      Duan, H.B., Xu, C.F., Liu, S.Q., Shao, S., Template matching using chaotic imperialist competitive algorithm. Pattern Recognit. Lett. 31, 1868–1875 (2010)
11.      Jain, T., Nigam, M.J., Synergy of evolutionary algorithm and socio-political process for global optimization. Expert Syst. Appl. 37, 3706–3713 (2010). doi:10.1016/j.eswa.2009.11.055
12.      Khorani, V., Forouzideh, N., Nasrabadi, A.M., Artificial neural network weights optimization using ICA, GA, ICA-GA and R-ICA-GA: Comparing performances. IEEE SSCI 2011 - Symp. Ser. Comput. Intell. - HIMA 2011 2011 IEEE Work. Hybrid Intell. Model. Appl. 61–67 (2011). doi:10.1109/HIMA.2011.5953956
13.      Karimi, N., Zandieh, M., Najafi, A.A., Group scheduling in flexible flow shops: A hybridised approach of imperialist competitive algorithm and electromagnetic-like mechanism. Int. J. Prod. Res. 49, 4965–4977 (2011). doi:10.1080/00207543.2010.481644
14.      Blum, C., Puchinger, J., Raidl, G.R., Roli, A., A brief survey on hybrid metaheuristics. Bioinspired Optim. Methods their Appl. - Proc. 4th Int. Conf. Bioinspired Optim. Methods their Appl. BIOMA 2010. 3–16 (2010). doi:10.1007/978-3-540-78295-7_1
15.      Ghaffarzadeh, H., Raeisi, F., Damage Identification in Truss Structures Using Finite Element Model Updating and Imperialist Competitive Algorithm. 10, 266–277 (2016)
16.      Eskandar, H., Salehi, P., Sabour, M.H., Imperialist Competitive Ant Colony Algorithm for Truss Structures. Appl. Sci. 12, 94–105 (2011)
17.      Talatahari, S., Kaveh, A., Sheikholeslami, R., Chaotic imperialist competitive algorithm for optimum design of truss structures. Struct. Multidiscip. Optim. 46, 355–367 (2012). doi:10.1007/s00158-011-0754-4
18.      Mohammadi-ivatloo, B., Rabiee, A., Soroudi, A., Ehsan, M., Imperialist competitive algorithm for solving non-convex dynamic economic power dispatch. Energy. 44, 228–240 (2012). doi:10.1016/j.energy.2012.06.034
19.      Shaban, H., Vahidi, B., Ebrahimpour, M., A robust PID controller based on imperialist competitive algorithm for load-frequency control of power systems. ISA Trans. 52, 88–95 (2012)
20.      Roozbeh Nia, A., Hemmati Far, M., Akhavan Niaki, S.T., A hybrid genetic and imperialist competitive algorithm for green vendor managed inventory of multi-item multi-constraint EOQ model under shortage. Appl. Soft Comput. 30, 353–634 (2015)
21.      Hajihassani, M., Armaghani, D.J., Marto, A., Tonnizam, E.M., Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bull Eng Geol Env. 74, 873–886 (2014). doi:10.1007/s10064-014-0657-x
22.      Kashani, A.R., Gandomi, A.H., Mousavi, M., Imperialistic Competitive Algorithm: A metaheuristic algorithm for locating the critical slip surface in 2-Dimensional soil slopes. Geosci. Front. 7, 83–89 (2016)
23.      S.Ali, E., Speed control of induction motor supplied by wind turbine via Imperialist Competitive Algorithm. Energy. 89, 593–600 (2015)
24.      Lin, J.L., Cho, C.W., Chuan, H.C., Imperialist competitive algorithms with perturbed moves for global optimization. Appl. Mech. Mater. 284–287, 3135–3139 (2013). doi:10.4028/www.scientific.net/AMM.284-287.3135
25.      Jamil, M., Yang, X.-S., A Literature Survey of Benchmark Functions For Global Optimization. Int. J. Math. Model. Numer. Optim. 4, 150–194 (2013). doi:10.1504/IJMMNO.2013.055204
26.      Arora, J.S., Introduction to Optimum Design. Elsevier acadenic press, London, UK (2004)
27.      Kaveh, A., Khayatazad, M., A new meta-heuristic method: Ray Optimization. Comput. Struct. 112–113, 283–294 (2012). doi:10.1016/j.compstruc.2012.09.003
28.      Mezura-Montes, E., Coello, C.A.C., An empirical study about the usefulness of evolution strategies to solve constrained optimization problems. Int. J. Gen. Syst. 37, 443–473 (2008). doi:10.1080/03081070701303470
29.      Kaveh, A., Zolghadr, A., a Novel Meta-Heuristic Algorithm: Tug of War  Optimization. Int J. Optim. Civil Eng. 6, 469–492 (2016)
30.      Sadollah, A., Bahreininejad, A., Eskandar, H., Hamdi, M., Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems. Appl. Soft Comput. 13, 2592–2612 (2013). doi:10.1016/j.asoc.2012.11.026
31.      He, Q., Wang, L., A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization. Appl. Math. Comput. 186, 1407–1422 (2007). doi:10.1016/j.amc.2006.07.134
32.      Mirjalili, S., Lewis, A., The Whale Optimization Algorithm. Adv. Eng. Softw. 95, 51–67 (2016). doi:10.1016/j.advengsoft.2016.01.008
33.      Rashedi, E., Nezamabadi-pour, H., Saryazdi, S., GSA: A Gravitational Search Algorithm. Inf. Sci. (Ny). 179, 2232–2248 (2009). doi:10.1016/j.ins.2009.03.004
34.      Kannan: An augmented Lagrange multiplier based method for mixed integer discrete continuous optimization and its applications to mechanical design. ASME J. Mech. Des. 1, 405–411 (1994)
35.      Li, M.D., Zhao, H., Weng, X.W., Han, T., A novel nature-inspired algorithm for optimization: Virus colony search. Adv. Eng. Softw. 92, 65–88 (2016). doi:10.1016/j.advengsoft.2015.11.004
36.      Mirjalili, S., Mirjalili, S.M., Lewis, A., Grey Wolf Optimizer. Adv. Eng. Softw. 69, 46–61 (2014). doi:10.1016/j.advengsoft.2013.12.007
37.      Gold, S., Krishnamurty, S., Tradeoffs in Robust Engineering Design. In: 1997 ASME Design Engineering Technical Conferences (1997)
38.      Gandomi, A.H., Yang, X.S., Alavi, A.H., Cuckoo search algorithm: A metaheuristic approach to solve structural optimization problems. Eng. Comput. 29, 17–35 (2013). doi:10.1007/s00366-011-0241-y
39.      Wang, G.G., Adaptive Response Surface Method Using Inherited Latin Hypercube Design Points. J. Mech. Des. 125, 210–220 (2003). doi:10.1115/1.1561044
40.      Shahrouzi, M., Kaveh, A., Dynamic fuzzy-membership Optimization: an enhanced meta-heuristic search. Asian J. Civ. Eng. 16, 249–268 (2015)
41.      Rajeeve, S., Krishnamurty, S., Discrete optimization of structures using genetic algorithm. ASCE J Struct Eng. 118, 1123–1250 (1992)
42.      Li, L.J., Huang, Z.B., Liu, F., A heuristic particle swarm optimization method for truss structures with discrete variables. Comput. Struct. 87, 435–443 (2009). doi:10.1016/j.compstruc.2009.01.004
43.      Sadollah, A., Bahreininejad, A., Eskandar, H., Hamdi, M., Mine blast algorithm for optimization of truss structures with discrete variables. Comput. Struct. 102–103, 49–63 (2012). doi:10.1016/j.compstruc.2012.03.013
44.      Hasançebi, O., Çarbaş, S., Doǧan, E., Erdal, F., Saka, M.P., Performance evaluation of metaheuristic search techniques in the optimum design of real size pin jointed structures. Comput. Struct. 87, 284–302 (2009). doi:10.1016/j.compstruc.2009.01.002
45.      Kaveh, A., Talatahari, S., A particle swarm ant colony optimization for truss structures with discrete variables. J. Constr. Steel Res. 65, 1558–1568 (2009). doi:10.1016/j.jcsr.2009.04.021
46.      Kaveh, A., Mahdavi, V.R., Colliding bodies optimization: A novel meta-heuristic method. Comput. Struct. 139, 18–27 (2014). doi:10.1016/j.compstruc.2014.04.005
47.      Shahrouzi, M., Aghabagloua, M., Rafiee, F., Observer-teacher-learner-based optimization: An enhanced meta-heuristic for structural sizing design. Struct. Eng. Mech. 62, 537–550 (2017). doi:10.12989/sem.2017.62.5.537
48.      AISC. American Construction Institutes of Steel: Manual of steel construction. Allowable stress design. , Chicago, Illinois (1989)
Scientia Iranica
Volume 28, Issue 4
Transactions on Civil Engineering (A)
July and August 2021
Pages 1973-1993

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  • Receive Date: 25 June 2019
  • Revise Date: 21 July 2020
  • Accept Date: 16 November 2020

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