Abstract
This chapter introduces a novel optimization algorithm, group search optimizer (GSO) algorithm. The implementation method of this algorithm is presented in detail. The GSO was used to investigate the truss structures with continuous variables and was tested by five planar and space truss optimization problems. The efficiency of GSO for frame structure with discrete variables was valued by three frame structures. The optimization results were compared with that of the particle swarm optimizer (PSO), the particle swarm optimizer with passive congregation (PSOPC) and the heuristic particle swarm optimizer (HPSO), ant colony optimization algorithm (ACO) and genetic algorithms (GA). Results from the tested cases illustrate the competitive ability of the GSO to find the optimal results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kaveh, A., Shojaee, S.: Optimal design of scissor-link foldable structures using ant colony optimization algorithm. Computer-Aided Civil and Infrastructure Engineering 22(1), 56–64 (2007)
Dorigo, M., Di Caro, G., Gambardella, L.: Ant algorithms for discrete optimization. Artificial Life 5(3), 137–172 (1999)
Kenndy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, Piscataway, NJ, USA, pp. 1942–1948 (1995)
Li, L.J., Ren, F.M., Liu, F., Wu, Q.H.: An improved particle swarm optimization method and its application in civil engineering. In: The 8th International Conference on Com-putation and Structures Technology, Las Palmas de Gran Canaria, Spain (2006) paper 42
He, S., Prempain, Wu, Q.H.: An improved particle swarm optimizer for mechanical design optimization problems. Engineering Optimization 36(5), 585–605 (2004)
Perez, R.E., Behdinan, K.: Particle swarm approach for structural design optimization. Computers and Structures 85(19-20), 1579–1588 (2007)
Li, L.J., Ren, F.M., Liu, F.: Structure optimization with discrete variables based on heuristic particle swarm optimal algorithm. In: Proceedings of the 3rd International Conference on Steel and Composite Structures, pp. 1017–1021. Taylor & Francis, Manchester (2007)
Li, L.J., Huang, Z.B., Liu, F., Wu, Q.H.: A heuristic particle swarm optimizer for optimization of pin connected structures. Computers and Structures 85(7-8), 340–349 (2007)
Barnard, C.J., Sibly, R.M.: Producers and scroungers: a general model and its application to captive flocks of house sparrows. Animal Behavior 29(2), 543–550 (1981)
Li, L.J., Liu, F., Xu, X.T., Liu, F.: The group search optimizer and its application to truss structure design. Advances in Structural Engineering 13(1), 43–51 (2010)
Liu, F., Xu, X.T., Li, L.J.: The group search optimizer and its application on truss structure design. In: The 4th International Conference on Natural Computation, Jinan, China, pp. 688–692 (2008)
He, S., Wu, Q.H., Saunder, J.R.: A novel group search optimizer inspired by animal behaviour ecology. In: 2006 IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, pp. 4415–4421 (2006)
Carpenter, R.H.S.: Eye movements. Macmilan, London (1991)
Liversedge, S.P., Findley, J.M.: Saccadic eye movements and cognition. Trends in Cognitive Sciences 4(1), 6–14 (2000)
Couzin, I.D., Krause, J., Franks, N.R., Levin, S.A.: Effective leadership and decision-making in animal groups on the move. Nature 433(7025), 513–516 (2005)
O’Brien, W.J., Evans, B.I., Howick, G.L.: A new view of the predation cycle of a planktivorous fish, white crappie (pomoxis annularis). Canadian Journal of Fisheries and Aquatic Sciences 43, 1894–1899 (1986)
Dixon, A.F.G.: An experimental study of the searching behavior of the predatory coccinellid beetle adalia decempunctata. Journal of Animal Ecology 28, 259–281 (1959)
Saka, M.P., Kameshki, E.S.: Optimum design of unbraced rigid frames. Computer & Structures 69, 433–442 (1998)
Kameshki, E.S., Saka, M.P.: Optimum design of nonlinear steel frames with semirigid connections using a genetic algorithm. Computer and Structures 79, 1593–1604 (2001)
Charles, V.C., Barron, J.B., Scott, P.S.: Design of steel frames using ant colony optimization. Structure Engineering ASCE 131(3), 369–379 (2005)
Chuang, W.S.: A PSO-SA hybrid searching algorithm for optimization of structures. Master’s Thesis of National Taiwan University, 116–126 (2007)
Zhu, C.Y., Guo, P.F., et al.: Improving of genetic algorithm and its application to discrete topology optimization for frames. Journal of Mechanical Strength 27(1), 61–65 (2005)
Camp, C., Pezeshk, S., Cao, G.: Optimized design of two-dimensional structures using a genetic algorithm. Journal of Structural Engineering ASCE 124(5), 551–559 (1998)
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Li, L., Liu, F. (2011). Optimum Design of Structures with Group Search Optimizer Algorithm. In: Group Search Optimization for Applications in Structural Design. Adaptation, Learning, and Optimization, vol 9. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20536-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-20536-1_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20535-4
Online ISBN: 978-3-642-20536-1
eBook Packages: EngineeringEngineering (R0)