The water cycle algorithm (WCA), which is a metaheuristic method inspired by the movements of rivers and streams towards the sea in nature, has been adapted and applied here for the first time for solving such a challenging problem as the parameter identification of fermentation process (FP) models. Bacteria and yeast are chosen as representatives of FP models that are subjected to parameter identification due to their impact in different industrial fields. In addition, WCA is considered in comparison with the genetic algorithm (GA), which is another population-based technique that has been proved to be a promising alternative of conventional optimisation methods. The obtained results have been thoroughly analysed in order to outline the advantages and disadvantages of each algorithm when solving such a complicated real-world task. A discussion and a comparative analysis of both metaheuristic algorithms reveal the impact of WCA on model identification problems and show that the newly applied WCA outperforms GA with regard to the model accuracy.

中文翻译：

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发酵过程建模的水循环算法

水循环算法（WCA）是一种元启发式方法，受自然界中的河流向海洋运动的启发，已在此处首次进行了修改和应用，以解决诸如发酵过程的参数识别之类的挑战性问题。 （FP）模型。选择细菌和酵母作为FP模型的代表，由于它们在不同工业领域中的影响，因此要进行参数识别。此外，与遗传算法（GA）相比，还考虑了WCA，遗传算法是另一种基于人群的技术，已被证明是传统优化方法的有希望的替代方法。为了概述每种算法在解决这种复杂的实际任务时的优缺点，已对获得的结果进行了彻底的分析。