The use of optimization algorithms to adjust the numerical models with experimental values has been applied in other fields, but the efforts done in metal casting sector are much more limited. The advances in this area may contribute to get metal casting adjusted models in less time improving the confidence in their predictions and contributing to reduce tests at laboratory scale. This work compares the performance of four algorithms (compass search, NEWUOA, genetic algorithm (GA) and particle swarm optimization (PSO)) in the adjustment of the metal casting simulation models. The case study used in the comparison is the multiscale simulation of the hypereutectic ductile iron (SGI) casting solidification. The model fitting criteria is the value of the tensile strength. Four different situations have been studied: model fitting based in 2, 3, 6 and 10 variables. Compass search and PSO have succeeded in reaching the error target in the four cases studied, while NEWUOA and GA have failed in some cases. In the case of the deterministic algorithms, compass search and NEWUOA, the use of a multiple random initial guess has been clearly beneficious.

中文翻译：

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球墨铸铁多尺度数值模拟模型拟合中优化算法的性能

在其他领域中已经使用了优化算法来调整具有实验值的数值模型，但是在金属铸造领域所做的努力却受到很大限制。该领域的进展可能有助于在更短的时间内获得金属铸件调整后的模型，从而提高对其预测的信心，并有助于减少实验室规模的测试。这项工作在调整金属铸造仿真模型时比较了四种算法（罗盘搜索，NEWUOA，遗传算法（GA）和粒子群优化（PSO））的性能。比较中使用的案例研究是过共晶球墨铸铁（SGI）铸件凝固的多尺度模拟。模型拟合标准是抗拉强度的值。研究了四种不同的情况：基于2、3，6和10个变量。在所研究的四个案例中，指南针搜索和PSO已成功达到错误目标，而在某些情况下，NEWUOA和GA已失败。对于确定性算法，指南针搜索和NEWUOA，使用多次随机初始猜测显然是有益的。