The Effect of Member Grouping on the Optimum Design of Grillages with Particle Swarm Method

Year 2011, Volume: 3 Issue: 3, 47 - 59, 01.09.2011

F. Erdal E. Doğan M. P. Saka

Abstract

Member grouping of a steel grillage system has an important effect in the minimum weight design of these systems. In the present study, this effect is investigated using an optimum design algorithm which is based on a recently developed particle swarm optimization method (PSO). Particle swarm optimizer is a simulator of social behavior that is used to realize the movement of a birds’ flock, which is a population based numerical optimization technique. The optimum design problem of a grillage system is formulated by implementing LRFDAISC (Load and Resistance Factor Design-American Institute of Steel Construction) limitations. It is decided that W-Sections are to be adapted for the longitudinal and transverse beams of the grillage system. 272 WSection beams given in LRFD code are collected in a pool and the optimum design algorithm is expected to select the appropriate sections from this pool so that the weight of the grillage is the minimum correspondingly the design limitations implemented from the design code are satisfied. The solution for this discrete programming problem is determined by using the PSO algorithm. In order to demonstrate the effect of member grouping in the optimum design of grillage systems, a design example is presented

Keywords

Grillage optimization, discrete optimum design, member grouping, stochastic search technique, particle swarm algorithm

References

• [1] Bonabeau, E., Dorigo, M. and Theraulaz, G., Swarm Intelligence: From Natural to Artificial Systems, Oxford University Press, U.K., 1999
• [2] Kennedy, J., Eberhart, R.C. and Shi, Y. Swarm Intelligence, Morgan Kaufmann Publishers, 2001
• [3] Kennedy, J. and Eberhart, R.C., Particle Swarm Optimization, In: Proceedings of IEEE International Conference on Neural Networks NJ: Piscataway; 1942-48, 1995.
• [4] Fourie, P. and Groenwold, A. the Particle Swarm Optimization Algorithm in Size and Shape Optimization, Structural and Multidisciplinary Optimization, 23 (4), 259-267, 2002.
• [5] Li, L., Huang, Z. and Liu, F., An Improved Particle Swarm Optimizer for Truss Structure Optimization, Lecture notes in computer science, Berlin/Heidelberg: Springer, 4456, 1-10, 2007.
• [6] He, S., Prempain, E. and Wu, Q.H., An Improved Particle Swarm Optimizer for Mechanical Design Optimization Problems, Engineering Optimization, 36, 5, 585-605, 2004.
• [7] Kaveh, A. and Talatahari, S., A particle swarm ant colony optimization for truss structures with discrete variables, Journal of Constructional Steel Research, 65, 1558- 1568, 2009
• [8] LRFD-AISC, Manual of Steel Construction, Load and Resistance Factor Design, Metric Conversion of the Second Edition, AISC, I & II, 1999
• [9] Venter, G. and Sobieszczanski-Sobieski, J. Multidisciplinary Optimization of a Transport Aircraft Wing Using Particle Swarm Optimization, Structural and Multidisciplinary Optimization, 26, 21-131, 2004.
• [10] Fourie, P. and Groenwold, A., The Particle Swarm Optimization Algorithm in Size and Shape Optimization, Structural and Multidisciplinary Optimization, 23 (4), 259- 267, 2002.
• [11] Perez, R. E. and Behdinan, K., Particle Swarm Approach for Structural Design Optimization, Computers and Structures, 85 (19-20),1579-1588, 2007.
• [12] Arumugam, M.S., Rao, M.V.C. and Chandramohan, A., A new and Improved Version of Particle Swarm Optimization Algorithm with Global–Local Best Parameters, Knowl Inf. Syst. DOI 10.1007/s10115-007-0109-z.
• [13] Erdal, F., Saka, M.P. and Doğan, E., Optimum Design of cellular beams using harmony search and particle swarm optimizers, Journal of Constructional Steel Research, 67(2), 237-247, 2011.