Shuffled frog-leaping algorithm (SFLA), a memetic meta-heuristic, is proved to be a successful combinatorial optimization algorithm and applied to several engineering problems. However, its potential is not explored in the context of laminate stacking sequence optimization of composite structures. In this article, we propose a hybrid version of SFLA for solving the combinatorial optimization problem associated with lay-up sequence optimization of laminate composite structures. In order to improve the computational performance as well as reliability of the optimal solutions, a customized neighborhood search algorithm and an adaptive search factor are incorporated in to the SFL algorithm to accelerate the convergence characteristics. Apart from this, a crossover operator is suitably incorporated in the proposed hybrid SFLA. Numerical experiments have been carried out by first considering the problem of buckling and failure load optimization of composite plate and later, optimal design of stiffened composite cylindrical shells. Superior convergence characteristics and robustness of the proposed hybrid SFLA is demonstrated by comparing with other popular meta-heuristic algorithms including genetic algorithms.
Gurdal Z and Haftka RT. Optimisation of composite laminates. In: NATO Advanced Study Institute on Optimisation of Large Structural Systems, Berchtesgaden, Germany, 1991.
SoremekunGGürdalZHaftkaRTWatsonLT. Composite laminate design optimization by genetic algorithm with generalized elitist selection. Comput Struct2001; 79(2): 131–143.
4.
Venkataraman S and Haftka RT. Optimization of composite panels - a review. In: Proceedings of the American Society of Composites-14th Annual Technical Conference, Fairborn, Ohio, 1999, pp. 479–488.
5.
KimT-UShinJWHwangIH. Stacking sequence design of a composite wing under a random gust using a genetic algorithm. Comput Struct2007; 85: 579–585.
6.
AkbulutMSonmezFO. Optimum design of composite laminates for minimum thickness. Comput Struct2008; 86(21-22): 1974–1982.
7.
ParkCHLeeWIHanWSVautrinA. Improved genetic algorithm for multidisciplinary optimization of composite laminates. Comput Struct2008; 86(19-20): 1894–1903.
8.
Rama Mohan RaoAArvindN. A scatter search algorithm for stacking sequence optimisation of laminate composites. Compos Struct2005; 70(4): 383–402.
9.
Rama Mohan RaoALakshmiK. Multi-objective optimal design of hybrid laminate composite structures using scatter search. Int J Compos Mater2009; 43(20): 2157–2182.
10.
Rama Mohan RaoAArvindN. Optimal stacking sequence design of laminate composite structures using tabu embedded simulated annealing. Int J Struct Eng Mech2007; 25(2): 239–268.
11.
PaiNKawAWengM. Optimization of laminate stacking sequence for failure load maximization using tabu search. Composites Part B: Eng2003; 34(4): 405–413.
12.
Rama Mohan RaoA. Lay-up sequence design of laminate composite plates and cylindrical skirt using ant colony optimization. Proc IMechE, Part G: J Aerospace Engineering2009; 223: 1–18.
13.
Rama Mohan RaoAShyjuPP. Development of a hybrid meta-heuristic algorithm for combinatorial optimisation and its application for optimal design of laminated composite cylindrical skirt. Comput Struct2008; 86: 796–815.
14.
VenkataramanSHaftkaRT. Structural optimization: what has Moore’s law done for us. Struct Multidiscipl Optim2004; 28(6): 375–387.
15.
WolpertDHMacreadyWG. No free lunch theorems for optimization. IEEE Trans Evolution Comput1997; 1: 67–82.
16.
Droste, S., Hansen, T. and Wegener, I. Perhaps not a free lunch but at least a free appetizer. In: Proceedings of the Genetic and Evolutionary Computations Conference. Orlando, Florida, USA, 1999, pp. 833-839.
17.
EusuffMMLanseyKE. Optimization of water distribution network design using the shuffled frog-leaping algorithm. J Water Resour Plan Manage2003; 129(3): 210–225.
18.
AmiriBFathianMAnd MaroosiA. Application of shuffled frog-leaping algorithm on clustering. Int J Adv Manuf Technol2009; 45(1-2): 199–209.
19.
Rahimi-VahedAMirzaeiAH. A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem. Comput Ind Eng2007; 53(4): 642–666.
20.
ElbeltagiEHegazyTGriersonD. Comparison among five of the population can be studied evolutionary-based optimization algorithms. Adv Eng Inform2005; 19(1): 43–53.
21.
EusuffMMLanseyKPashaF. Shuffled frog-leaping algorithm: a memetic meta-heuristic for discrete optimization. Eng Optim2006; 38(2): 129–154.
22.
HollandJH. Adaptation in natural and artificial systems, Ann Arbor, MI: University of Michigan Press, 1975.
23.
GoldbergDE. Genetic algorithms in search, optimisation and machine learning, New Jersey: Addison-Wesley, 1989.
24.
van LaarhovenPAartsEHL. Simulated annealing: theory and applications, Dordrecht: Kluwer, 1987.
25.
DorigoMCaroGDGambardellaLM. An algorithm for discrete optimization. Artif Life1999; 5: 137–172.
26.
Eberhart, R.C. and Kennedy, J. A new optimizer using particles swarm theory. In: Proceedings of the 6th International Symposium on Micro Machine and Human Science, Nagoya, Japan, 1995, pp. 39–43.
Kennedy, J. and Eberhart, R. Particle swarm optimization. In: Proceedings IEEE International Conference on Neural Networks, IEEE Service Center, Piscataway, NJ, 1995, pp. 1942–1948.
29.
Kennedy, J. The particle swarm: social adaptation of knowledge. In: Proceedings of the IEEE International Conference on Evolutionary Computation, Indianapolis, IN, USA, 1997, pp. 303–308.
30.
DuanQSorooshianSGuptaV. Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resour Res1992; 28(4): 1015–1031.
31.
PriceWL. Global optimization algorithms for a CAD workstation. J Optim Theory Appl1987; 55: 133–146.
32.
NelderJAMeadR. A simplex method for function minimization. Comput J1965; 7: 308–313.
33.
DawkinsR. The selfish gene, Oxford: Oxford University Press, 1976.
34.
ElbeltagiEHegazyTSoudkiK. Comparison of two evolutionary algorithms for optimization of bridge deck repairs. Comput-Aid Civil Infrastruct Eng2006; 21: 561–572.
35.
JohnsonDSMcGeochLAThe traveling salesman problem: a case study. In: AartsEHLenstraJK (eds). Local search in combinatorial optimization, Chichester: John Wiley & Sons, 1997, pp. 215–310.
36.
LeRicheRHaftkaRT. Optimization of laminate stacking sequence for buckling load maximization by genetic algorithm. AIAA Journal1993; 31: 951–956.
37.
JonesRM. Mechanics of composite materials, New York: McGraw-Hill, 1975, 1975.
38.
SunGMaoR. Optimization of stiffened laminated-composite circular-cylindrical shells for buckling. Compos Struct1993; 23: 53–60.
39.
WodesenbetEKidaneSPangS. Optimization for buckling loads of grid stiffened composite panels. Compos Struct2003; 60(2): 159–169.
40.
KogisoNWatsonLTGürdalZHaftkaRTNagendraS. Design of composite laminates by a genetic algorithm with memory. Mech Compos Mater Struct1994; 1(1): 95–117.
41.
TodorokiASasaiM. Improvement of design reliability for buckling load maximization of composite cylinder using genetic algorithm with recessive-gene-like repair. JSME Int J Ser A1999; 42(4): 530–536.
