Ant Colony Algorithm in Selection Suitable Plant for Urban Farming

A. Wahana, I. Taufik, Daniel Roberto Ramiraj, C. Alam, B. Subaeki
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Abstract

Urban Farming is the right solution where this agricultural method is an agricultural method that can take advantage of the narrow open land for farming purposes. Choosing a suitable crop type for a city can give better results. This research by observing the temperature of 5 (five) big cities. The purpose of this study is to select suitable plants for a city according to the temperature of each city. The Ant Colony Optimization (ACO) algorithm is inspired by the observation of an ant colony. Ants are animals that live as a unit in their colony as opposed to being seen as individuals who live independently of the colony. The results of this study provide selection of plants suitable for cultivation in each city with an the level compatibility of 68 percent.
蚁群算法在城市农业植物选择中的应用
城市农业是正确的解决方案,这种农业方法是一种农业方法,可以利用狭窄的开放土地进行耕作。为一个城市选择合适的作物类型可以获得更好的结果。这项研究通过观察5个大城市的温度。本研究的目的是根据每个城市的温度选择适合城市的植物。蚁群优化算法的灵感来自于对蚁群的观察。蚂蚁是一种以群体为单位生活的动物,而不是被视为独立于群体生活的个体。研究结果为各城市提供了适宜栽培的植物选择,亲和性达到68%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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