Advanced SearchSearch Tips
Optimum parameterization in grillage design under a worst point load
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Optimum parameterization in grillage design under a worst point load
Kim Yun-Young; Ko Jae-Yang;
  PDF(new window)
The optimum grillage design belongs to nonlinear constrained optimization problem. The determination of beam scantlings for the grillage structure is a very crucial matter out of whole structural design process. The performance of optimization methods, based on penalty functions, is highly problem-dependent and many methods require additional tuning of some variables. This additional tuning is the influences of penalty coefficient, which depend strongly on the degree of constraint violation. Moreover, Binary-coded Genetic Algorithm (BGA) meets certain difficulties when dealing with continuous and/or discrete search spaces with large dimensions. With the above reasons, Real-coded Micro-Genetic Algorithm () is proposed to find the optimum beam scantlings of the grillage structure without handling any of penalty functions. can help in avoiding the premature convergence and search for global solution-spaces, because of its wide spread applicability, global perspective and inherent parallelism. Direct stiffness method is used as a numerical tool for the grillage analysis. In optimization study to find minimum weight, sensitivity study is carried out with varying beam configurations. From the simulation results, it has been concluded that the proposed is an effective optimization tool for solving continuous and/or discrete nonlinear real-world optimization problems.
Real-coded micro-genetic algorithm;Grillage;Optimum beam scantling;Direct stiffness method;
 Cited by
Caruana, R. A. and Schaffer, J. D. (1988), 'Representation and hidden bias: gray versus binary coding for genetic algorithms,' Proc. of the Fifth Int. Conf. on Machine Learning, pp. 153-162

Davis, L. (1989), 'Adapting operator probabilities in genetic algorithms,' Proc. of the Third Int. Conf. on Genetic Algorithms, J. David Schaffer (Ed.), Morgan Kaufmann Publishers, San Mateo, pp. 61-69

Goldberg, D. E. (1989), 'Genetic algorithms and walsh functions: part II, deception and its analysis,' Complex Systems, Vol. 3, pp. 153-171

Herrera, F., Lozano, M., and Verdegay, J. L. (1998), 'Tackling real-coded genetic algorithms: operators and tools for behavioural analysis,' Artificial Intelligence Review, Vol. 12, No. 4, pp. 265-319 crossref(new window)

Kim, K. S., Kim, K. S., and Park, H. J. (2004), 'A Study on the Analysis and Design of Grillages under a Worst Point Load,' Key Engineering Materials, Vol. 261-263, pp. 783-788

Kim, Y., Cho, Park, M. C., Gotch, J. W., K., and Toyosada, M. (2003), 'Optimum Design of Sandwich Panel using Hybrid Metaheuristics Approach,' Int. J of Ocean Engineering and Technology, Vol. 17, No. 6, pp. 38-46

Kim Y., Kim, B. I., and Shin, S. C. (2005), 'Real-coded Micro-Genetic Algorithm for Nonlinear Constrained Engineering Designs', J. of Ship & Ocean Technology (SOTECH), Vol. 9, No. 4, pp. 35-46

Kim, Y., Kim, K. S., and Park, J. W. (2006), 'Midship Section Optimization of Hatchcoverless Container Ship based on Real-Coded Micro-Genetic Algorithm,' Key Engineering Materials, Vol. 306-308, pp. 529-534

Koziel, S., and Michalewicz, Z. (1999), 'Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization,' Evolutionary Computation, Vol. 7, No. 1, pp. 19-44 crossref(new window)

Lloyd's Register of shipping (2003), 'Rules and Regulations for the classification of ship'

Michalewicz, Z. (1994) 'Genetic Algorithms + Data Structures = Evolution Programs,' extended edition, Springer-Verlag, New York

Radcliffe, N. J. (1992), 'Non-Linear Genetic Representation,' Parallel Problem Solving from Nature 2, R. Manner and B. Manderick (Ed.), Elsevier Science Publishers, Amsterdam, pp. 259-268