Fruit fly optimization algorithm (FOA) is a well-known optimization algorithm with a well-designed structure and superiority of fewer parameters, more effortless adjustment, and competitive and fast computation time. Up till now, FOA has been effectively applied to numerous fields, such as financial forecast and medicine, and it achieves favorable results. However, some aspects need to be enhanced when dealing with some function optimization cases. This method is inclined to falling into local optima with a slow convergence. In this paper, the following behavior-driven multi-population FOA is proposed to relieve these drawbacks, which combines the following mechanism of artificial fish swarm algorithm with the unique searching ability of different types of fruit flies. The chaotic global disturbance is introduced to improve the global exploration ability of the original FOA and reduce the probability of advanced FOA falling into the local extreme. The proposed FOA, it is compared with the advanced FOA with several well-established algorithms and the latest improved optimizers in different dimensions horizontally and vertically to substantiate the effectiveness of the. It is also applied advanced FOA to two practical engineering optimization projects. Systematic analysis and experimental data indicate that the advanced FOA variant outperforms the original FOA and the latest improved algorithms. An online repository will support this research at http://aliasgharheidari.com for any communication and guidance for future works.

果蝇优化算法（FOA）是一种众所周知的优化算法，其结构设计合理，并且具有更少的参数，更轻松的调整以及竞争性和快速的计算时间的优越性。到目前为止，FOA已经有效地应用于财务预测和医学等多个领域，并取得了良好的效果。但是，在处理某些功能优化情况时，需要增强某些方面。该方法倾向于以缓慢收敛落入局部最优。本文提出了以下行为驱动的多种群FOA，以消除这些缺陷，将以下人工鱼群算法的机制与不同类型果蝇的独特搜索能力相结合。引入混沌全局扰动以提高原始FOA的全局探测能力，并降低高级FOA降落到局部极端的可能性。拟议的FOA与先进的FOA进行了比较，后者具有几种公认的算法以及水平和垂直不同维度上最新改进的优化程序，以证实其有效性。它还将高级FOA应用于两个实际的工程优化项目。系统分析和实验数据表明，先进的FOA变种优于原始的FOA和最新的改进算法。一个在线资源库将在http://aliasgharheidari.com上为这项研究提供支持，以获取有关未来作品的任何交流和指导。