Browse > Article
http://dx.doi.org/10.12989/sem.2017.61.3.359

An integrated particle swarm optimizer for optimization of truss structures with discrete variables  

Mortazavi, Ali (Department of Civil Engineering, Ege University)
Togan, Vedat (Department of Civil Engineering, Karadeniz Technical University)
Nuhoglu, Ayhan (Department of Civil Engineering, Ege University)
Publication Information
Structural Engineering and Mechanics / v.61, no.3, 2017 , pp. 359-370 More about this Journal
Abstract
This study presents a particle swarm optimization algorithm integrated with weighted particle concept and improved fly-back technique. The rationale behind this integration is to utilize the affirmative properties of these new terms to improve the search capability of the standard particle swarm optimizer. Improved fly-back technique introduced in this study can be a proper alternative for widely used penalty functions to handle existing constraints. This technique emphasizes the role of the weighted particle on escaping from trapping into local optimum(s) by utilizing a recursive procedure. On the other hand, it guaranties the feasibility of the final solution by rejecting infeasible solutions throughout the optimization process. Additionally, in contrast with penalty method, the improved fly-back technique does not contain any adjustable terms, thus it does not inflict any extra ad hoc parameters to the main optimizer algorithm. The improved fly-back approach, as independent unit, can easily be integrated with other optimizers to handle the constraints. Consequently, to evaluate the performance of the proposed method on solving the truss weight minimization problems with discrete variables, several benchmark examples taken from the technical literature are examined using the presented method. The results obtained are comparatively reported through proper graphs and tables. Based on the results acquired in this study, it can be stated that the proposed method (integrated particle swarm optimizer, iPSO) is competitive with other metaheuristic algorithms in solving this class of truss optimization problems.
Keywords
optimization; truss structures; particle swarm optimization, weighted particle, constraint handling;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Chen, D. and Zhao, C. (2009), "Particle swarm optimization with adaptive population size and its application", App. Soft Comput., 9, 39-48.   DOI
2 Coello Coello, C.A. (2002), "Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art", Comput. Meth. Appl. Mech. Eng., 191, 1245-1287.   DOI
3 Deb, K. and Gulati, S. (2001), "Design of truss-structures for minimum weight using genetic algorithms", Finite Elem. Anal. Des., 37, 447-465.   DOI
4 Dizangian, B. and Ghasemi, M.R. (2016), "An efficient method for reliable optimum design of trusses", Steel Compos. Struct., 21(5), 1069-1084.   DOI
5 Erbatur, F., Hasancebi, O., Tutuncu, I. and Kilic, H. (2000), "Optimal design of planar and space structures with genetic algorithms", Comput. Struct., 75, 209-224.   DOI
6 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", Chemom. Intell. Lab. Syst., 139, 15-25.   DOI
7 Gholizadeh, S., Salajegheh, E. and Torkzadeh, P. (2008), "Structural optimization with frequency constraints by genetic algorithm using wavelet radial basis function neural network", J. Sound Vib., 312, 316-331.   DOI
8 Hajela, P. and Lee, E. (1995), "Genetic algorithms in truss topological optimization", Int. J. Solid. Struct., 32, 3341-3357.   DOI
9 Hasancebi, O. (2008), "Adaptive evolution strategies in structural optimization: Enhancing their computational performance with applications to large-scale structures", Comput. Struct., 86, 119-132.   DOI
10 Hasancebi, O. and Erbatur, F. (2002), "On efficient use of simulated annealing in complex structural optimization problems", Acta Mech., 157, 27-50.   DOI
11 Alaimo, A., Milazzo, A. and Orlando, C. (2016), "Nonlinear model based particle swarm optimization of PID shimmy damping control", Adv. Aircr. Spacecrt. Sci., 3(2), 211-214.   DOI
12 Camp, C.V. and Bichon, B.J. (2004), "Design of space trusses using ant colony optimization", J. Struct. Eng., 130, 741-751.   DOI
13 Hasancebi, O., Teke, T. and Pekcan, O. (2013), "A bat-inspired algorithm for structural optimization", Comput. Struct., 128, 77-90.   DOI
14 Kaveh, A. and Talatahari, S. (2009b), "Size optimization of space trusses using Big Bang-Big crunch algorithm", Comput. Struct., 87, 1129-1140.   DOI
15 He, R.S. and Hwang, S.F. (2007), "Damage detection by a hybrid real-parameter genetic algorithm under the assistance of grey relation analysis", Eng. Appl. Artif. Intell., 20, 980-992.   DOI
16 He, S., Prempain, E. and Wu, Q.H. (2004), "An improved particle swarm optimizer for mechanical design optimization problems", Eng. Optim., 36, 585-605.   DOI
17 Kaveh, A. and Talatahari, S. (2009a), "A particle swarm ant colony optimization for truss structures with discrete variables", J. Constr. Steel Res., 65, 1558-1568.   DOI
18 Kaveh, A., Kalatjari, V. and Talebpour, M. (2016), "Optimal design of steel towers using a multi-metaheuristic based search method", Period. Polytech. Civil Eng., doi:10.3311/PPci.8222.   DOI
19 Kennedy, J. and Eberhart, R. (1995), "Particle swarm optimization", Proceedings of IEEE International Conference on Neural Networks, Perth, WA.
20 Lee, K.S. and Geem, Z.W. (2004), "A new structural optimization method based on the harmony search algorithm", Comput. Struct., 82, 781-798.   DOI
21 Lee, K.S., Geem, Z.W., Lee, S.H. and Bae, K.W. (2005), "The harmony search heuristic algorithm for discrete structural optimization", Eng. Optim., 37, 663-684.   DOI
22 Li, J.P. (2015), "Truss topology optimization using an improved species-conserving genetic algorithm", Eng. Optim., 47, 107-128.   DOI
23 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, 435-443.   DOI
24 Rajeev, S. and Krishnamoorthy, C. (1992), "Discrete optimization of structures using genetic algorithms", J. Struct. Eng., 118, 1233-1250.   DOI
25 Nickabadi, A., Ebadzadeh, M.M. and Safabakhsh, R. (2011), "A novel particle swarm optimization algorithm with adaptive inertia weight", Appl. Soft Comput., 11, 3658-3670.   DOI
26 Perez, R.E. and Behdinan, K. (2007), "Particle swarm approach for structural design optimization", Comput. Struct., 85, 1579-1588.   DOI
27 Rahami, H., Kaveh, A. and Gholipour, Y. (2008), "Sizing, geometry and topology optimization of trusses via force method and genetic algorithm", Eng. Struct., 30, 2360-2369.   DOI
28 Sadollah, A., Bahreininejad, A., Eskandar, H. and Hamdi, M. (2012), "Mine blast algorithm for optimization of truss structures with discrete variables", Comput. Struct., 102-103, 49-63.
29 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.   DOI
30 Sonmez, M. (2011), "Artificial bee colony algorithm for optimization of truss structures", Appl. Soft Comput., 11, 2406-2418.   DOI
31 Togan, V. and Daloglu, A.T. (2006), "Optimization of 3d trusses with adaptive approach in genetic algorithms", Eng. Struct., 28, 1019-1027.   DOI
32 Togan, V. and Daloglu, A.T. (2008), "An improved genetic algorithm with initial population strategy and self-adaptive member grouping", Comput. Struct., 86, 1204-1218.   DOI
33 Li, N.J., Wang, W.J., James Hsu, C.C., Chang, W., Chou, H.G. and Chang, J.W. (2014), "Enhanced particle swarm optimizer incorporating a weighted particle", Neurocomput., 124, 218-227.   DOI
34 Zheng, Y.J. (2015), "Water wave optimization: a new natureinspired metaheuristic", Comput. Oper. Res., 55, 1-11.   DOI
35 Wu, S.J. and Chow, P.T. (1995), "Steady-state genetic algorithms for discrete optimization of trusses", Comput. Struct., 56, 979-991.   DOI