Functionally graded materials optimization using particle swarm-based algorithms

This article presents an application of particle swarm-based algorithms for volume fraction optimization of functionally graded materials. A simple and efficient method for constraint handling is included too. To optimize the material composition, two numerical examples are done. In the first example, the peak residual stresses are minimized when the FGM is cooled from a reference temperature. In the second example, the factor of safety against yielding are maximized for a temperature dependent FGM sphere subjected to thermal gradients. The algorithms PSO, PSOPC, PSACO and PSPCACO are used too and the optimum volume fraction are obtained. According comparison of the results, particle swarm-based algorithms can be used as a powerful tool for optimizing the FG materials.

Ant colony algorithm; Functionally graded materials; Local composition control; Optimal design; Particle swarm-based optimization; Stochastic optimization