Design optimization of 2D continuum structures using an efficient simulated annealing algorithm

Authors

Abstract

This paper presents an optimization algorithm based on Simulated Annealing in design optimization of 2D continuum structures. The algorithm – denoted as CMLPSA (Corrected Multi-Level & Multi-Point Simulated Annealing) – implements an advanced search mechanism where each candidate design is selected from a population of trial points randomly generated. The multi-point strategy is adopted for both feasible and infeasible intermediate designs. CMLPSA includes a multi-level annealing strategy where trial points are generated by perturbing all design variables simultaneously (global level) or one by one (local level). In this paper, the effects of rejection ratio and evolutionary rate are investigated in design optimization of 2D continuum structures. The results of this study are also compared with other researches reported in the literature. It is concluded that for different values of initial discarding and rate of discarding parameters the final volume or von Mises stress of the optimum structure do not change considrably, but the final shape for different rate of discarding is changed. However, final volume and von Mises stress of the optimum structure are improved in comparison with other methods.

Keywords

References

[1] Abolbashari MH, Keshavarzmanesh S (2006) On various aspects of application of the evolutionary structural optimization method for 2D and 3D continuum structures. Finite Elem Anal Des 42(6): 478-491.
[2] Chu DN, Xie YM, Hira A, Steven GP (1997) On various aspectsof evolutionary structural optimization for problems with stiffnessconstraints. Finite Elem Anal Des 24: 197-212.
[3] Bendsoe MP, Kikuchi N (1988) Generating optimal topologies in structural design using homogenizationmethod. Comput Method Appl Mech Eng71: 197-224.
[4] Bendsoe MP (1998) Optimal shape design as a material distributionoptimization. Struct Multidiscip Optim 1: 193-202.
[5] Suzuki K, Kikuchi N (1991) A homogenization method for shape and topologyoptimization. Comput Methods Appl Mech Eng 93: 291-318.
[6] Diaz AR, Bendsoe MP (1992) Shape optimization of structures for multipleloading conditions using a homogenization method. Struct MultidiscipOptim 4: 22-77.
[7] Xie YM, Steven GP (1993) A simple evolutionary procedure for structuraloptimization. Comput Struct 49: 885-896.
[8] Lamberti L (2008) An efficient simulated annealing algorithm for design optimization of truss structures. Comput Struct 86(19-20): 1936-1953
[9] Genovese K, Lamberti L, Pappalettere C (2005) Improved global–local simulatedannealing formulation for solving non-smooth engineering optimizationproblems. Int J Solids Struct 42:203-237.
[10] Lamberti L, Pappalettere C (2007) Weight optimization of skeletal structures withmulti-point simulated annealing. Comput Model Eng Sci 18:183-221
[11] Chen TY, Su JJ (2002) Efficiency improvement of simulated annealing in optimal structural designs. Adv Eng Software 33: 675-80.
[12] Huang MW, Arora JS (1997) Optimal design with discrete variables: some numerical experiments. Int J Numer Methods Eng 40: 165-88.
[13] Lamberti L, Pappalettere C (2003) Move limits definition in structural optimization with sequential linear programming. Part I: Optimization algorithm. Comput Struct 81:197-213.
[14] Lamberti L, Pappalettere C (2004) Improved sequential linear programming formulation for structural weight minimization. Comput Methods Appl Mech 193(33): 3493-3521.
[15] Xie YM, Steven GP (1997) Evolutionarystructural optimization. Springer,London.
[16] Hemp WS (1973) Optimum structures. Clarendon Press, Oxford.
Journal of Solid and Fluid Mechanics
Volume 6, Issue 4
January and February 2016
Pages 41-48

Files

Share

How to cite

Statistics