Abstract
Optimal design of stiffened laminated composite cylinder of symmetric and balanced layup with isogrid form stiffeners is investigated and presented. The isogrid stiffened cylinder is subjected to uniform compressive load. In the optimization for the maximum buckling load, the panel skin laminate stacking sequence and stiffener configuration are chosen as design variables. A smeared model is employed in the buckling analysis of the stiffened composite cylinder. A new variant of particle swarm intelligence algorithm, using multiple swarms, built with quantum and dynamically reconfigurable features is developed and employed in the present investigations. The optimization is carried out using the proposed multi swarm based quantum particle swarm optimisation (PSO) algorithm, taking into consideration of the ply contiguous constraint. An optimal layup of the skin and stiffener configuration has also been obtained by using the proposed dynamic quantum PSO algorithm. Comparisons have been made with quantum PSO, cooperative quantum PSO, multi swarm based hybrid PSO, the newly developed multi swarm versions of quantum PSO and cooperative quantum PSO algorithms. Studies clearly indicate that multi swarm version of quantum PSO algorithms are more consistent and also reliable in providing optimal solutions. The methods presented in this paper will be applicable in general to the design of laminate composite structures.
Similar content being viewed by others
References
Chang N, Wang W, Yang W, Wang J (2010) Sacking sequence optimization of composite laminate by permutation discrete particle swarm optimization.Struct Multidiscip Optim 41(2):179–187
Chung HP, Woo L II, Woo SH, Alain V (2008) Improved genetic algorithm for multidisciplinary optimization of composite laminates. Comput Struct 86(19–20):1894–1903
Clerc M, Kennedy J (2002) The Particle swarm-explosion, stability and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73
Droste S, Hansen T, Wegener I (1999) Perhaps not a Free Lunch but at least a Free Appetizer. Proc Genet Evol Comput Conf 833–839
Eberhart RC, Kennedy J (1995) A new optimizer using particles swarm theory. In: Sixth international symposium on micro machine and human science, Nagoya, pp 39–43
Frans VDB, Andries PE (2004) A cooperative approach to particle swarm optimization. IEEE Trans Evol Comput 8(3):225–239
Gurdal Z, Haftka RT (1991) Optimisation of composite laminates. NATO advanced study institute on optimisation of large structural systems. Berchtesgaden
Hassan R, Cohanim BK, Weck OD, Venter G (2005) A comparison of particle swarm optimization and the genetic algorithm. In: Proceedings of the 46th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference, Austin, pp 1–13
Jacoby SLS, Kowalik JS, Pizzo JT (1972) Iterative methods for nonlinear optimisation problems. Prentice Hall, Englewood Cliffs
Jones RM (1975) Mechanics of composite materials. McGraw-Hill, New York
Karakaya S, Soykasap O (2009) Buckling optimization of laminated composite plates using genetic algorithm and generalized pattern search algorithm. Struct Multidiscip Optim 39(5):477–486
Karakaya S, Soykasap O (2011) Natural frequency and buckling optimization of laminated hybrid composite plates using genetic algorithm and simulated annealing. Struct Multidiscip Optim 43(1):61–72
Kennedy J (1999) Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: Proceedings of IEEE congress on evolutionary computation, Piscataway, pp 1931–1938
Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference neural networks, Piscataway, pp 1942–1948
Kogiso N, Watson LT, Gürdal Z, Haftka RT, Nagendra S (1994a) Design of composite laminates by a genetic algorithm with memory. Mech Compos Mater Struct 1(1):95–117
Kogiso N, Watson LT, Gürdal Z, Haftka RT (1994b) Genetic algorithms with local improvement for composite laminate design. Struct Optimis 7:207–218
Le Riche R, Haftka RT (1993) Optimization of laminate stacking sequence for buckling load maximization by genetic algorithm. AIAA J 31:951–956
Le Riche R, Haftka RT (1995) Improved genetic algorithm for minimum thickness composite laminate design. Compos Eng 5(2):143–161
Liang J, Qin A, Suganthan P, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10:281–295
Liu B, Haftka RT, Trompette P (2004) Maximization of buckling loads of composite panels using flexural lamination parameters. Struct Multidiscip Optim 26(1–2):28–36
Mustafa A, Fazil OS (2008) Optimum design of composite laminates for minimum thickness. Comput Struct 86(21–22):1974–1982
Nagendra S, Jestin D, Gurdal Z, Haftka RT, Watson LT (1996) Improved genetic algorithms for the design of stiffened composite panels. Comput Struct 58(3):543–55
Niranjan P, Autar K, Michael W (2003) Optimization of laminate stacking sequence for failure load maximization using Tabu search. Compos Part B Eng 34(4):405–413
Rama Mohan Rao A, Arvind N (2005) Scatter search algorithm for stacking sequence optimisation of laminate composites. Compos Struct 70(4):383–402
Rama Mohan Rao A, Arvind N (2007) Optimal stacking sequence design of laminate composite structures using tabu embedded simulated annealing. Int J Struct Eng Mech 25(2):239–268
Rama Mohan Rao A, Ganesh A (2007) Optimal placement of sensors for structural system identification and health monitoring using an hybrid swarm intelligence technique. Smart Mater Struct 16:2658–2672
Rama Mohan Rao A, Lakshmi K (2009) Multi-objective optimal design of hybrid laminate composite structures using scatter search. Int J Compos Mater 43(20):2157–2182
Rama Mohan Rao A, Shyju PP (2008) Development of a hybrid meta-heuristic algorithm for combinatorial optimisation and its application for optimal design of laminated composite cylindrical skirt. Comput Struct 86:796–815
Rama Mohan Rao A (2009) Lay-up sequence design of laminate composite plates and cylindrical skirt using ant colony optimization. Proc IMechE Part G J Aerosp Eng 223:1–18
Soremekun G, Gürdal Z, Haftka RT, Watson LT (2001) Composite laminate design optimization by genetic algorithm with generalized elitist selection. Comput Struct 79(2):131–143
Sun J, Feng B, Xu W (2004a) A global search strategy of quantum behaved particle swarm optimization. IEEE conference on cybernetics and intelligent system, Singapore, pp 111–116
Sun J, Feng B, Xu W (2004b) Particle swarm optimization with particles having quantum behavior. IEEE congress on evolutionary computation, Piscataway, pp 325–331
Kim TU, Shin JW (2007) In Hee Hwang, stacking sequence design of a composite wing under a random gust using a genetic algorithm. Comput Struct 85:579–585
Todoroki A, Sasai M (1999) Improvement of design reliability for buckling load maximization of composite cylinder using genetic algorithm with recessive-gene-like repair. JSME Int J Ser A 42(4):530–536
Venkataraman S, Haftka RT (1999) Optimization of Composite Panels - a review. In: Proceedings of the American society of composites-14th annual technical conference. Fairborn, Ohio, pp 479–488
Venkataraman S, Haftka RT (2004) Structural optimization: what has Moore’s law done for us. Struct Multidiscip Optim 28(6):375–387
Wodesenbet E, Kidane S, Pang S (2003) Optimization for buckling loads of grid stiffened composite panels. Compos Struct 60(2):159–169
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67–82
Acknowledgments
This paper is being published with the permission of Director, CSIR-Structural Engineering Research Centre, Taramani, Chennai. The authors gratefully acknowledge the financial support of Aeronautical Research and Development Board (AR&DB), New Delhi for this research work under grant DARO/08/1051334/M/I
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lakshmi, K., Mohan Rao, A.R. Optimal design of laminate composite isogrid with dynamically reconfigurable quantum PSO. Struct Multidisc Optim 48, 1001–1021 (2013). https://doi.org/10.1007/s00158-013-0943-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00158-013-0943-4