基于改进FOA的环境投诉事件相似度计算

Qingwu Fan, Guanghuang Chen, Kaiqin Yang
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引用次数: 0

摘要

对于环境投诉信息的处理,必须判断当前举报事件与历史举报案件的相似性。然而,投诉事件是复杂的,因此很难计算它们之间的相似度。一般来说,事件相似度通常是基于组成元素相似度的综合结果。为此,本文提出了一种基于改进果蝇优化算法(IFOA)的事件相似度计算方法来解决环境投诉事件相似度计算问题。首先,对果蝇优化算法(FOA)进行改进,解决了搜索半径固定和种群多样性低的问题;其次,通过计算两个事件之间各成分的相似度,构建相似度集,作为样本数据;然后,应用广义回归神经网络(GRNN)建立相似度计算模型。最后,利用IFOA对模型参数进行优化。实验结果表明,该方法具有较高的精度,能够满足实际需求。
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Similarity Calculation of Environmental Complaint Events Based on Improved FOA
For the treatment of environmental complaint information, it is essential to judge the similarity between current report events and historical report cases. However, complaint events are complicated, so it is hard to calculate the similarity between them. In general, the event similarity is usually a comprehensive result based on the similarity of constituent elements. Therefore, an event similarity calculation method based on an improved fruit fly optimization algorithm (IFOA) is proposed in this paper to solve the problem of environmental complaint events similarity computation. Firstly, the fruit fly optimization algorithm (FOA) is modified to solve the issues of fixed search radius and low population diversity. Secondly, the similarity degree set is constructed by calculating the similarity degree of each component between two events, which is taken as the sample data. Then, the generalized regression neural network (GRNN) is applied to establish the similarity calculation model. Finally, the parameter of the model is optimized by IFOA. Experimental results show that this method has high accuracy and can meet the actual demand.
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