Application of GA to optimize the process conditions of Al Matrix nano-composites

In this study, an effective approach based on genetic algorithm (GA), swarm intelligence optimization and finite element method (FEM) was implemented in order to model and optimize the process conditions of Al Matrix nano-composites. The nano-ceramic particles were added into the aluminum alloy to experimentally investigate the microstructure and mechanical behavior of metal matrix nano-composites (MMNCs). Inspired by the idea of breeding swarms, this paper proposes a GA/PSO hybrid algorithm, which combines the standard velocity and position update rules of PSO with the ideas of selection, crossover and mutation from GA. The experimental results of this project were compared with the modeled ones indicating the efficiency of the proposed model to estimate the optimal process conditions in fabrication of the nano-composite via casting.

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