This paper provides a method to numerically measure the quality of working life based on the reduction of human resource risks. It is conducted through the improved metaheuristic grasshopper optimization algorithm in two phases. First, a go-to study is carried out to identify the relationship between quality of working life and human resource risks in the capital market and to obtain the factors from quality of working life which reduce the risks. Then, a method is presented for the numerical measurement of these factors using a fuzzy inference system based on an adaptive neural network and a new hybrid method called the improved grasshopper optimization algorithm. This algorithm consists of the grasshopper optimization algorithm and the bees algorithm. It is found that the newly proposed method performs better and provides more accurate results than the conventional one.

中文翻译：

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使用改进的元启发式蚱蜢优化算法开发一种测量工作生活质量的方法

本文提供了一种基于减少人力资源风险的数字化衡量工作生活质量的方法。它是通过改进的元启发式蚱蜢优化算法分两个阶段进行的。首先，通过深入研究，识别资本市场工作生活质量与人力资源风险之间的关系，从工作生活质量中获取降低风险的因素。然后，提出了使用基于自适应神经网络的模糊推理系统和称为改进蚱蜢优化算法的新混合方法对这些因素进行数值测量的方法。该算法由蚱蜢优化算法和蜜蜂算法组成。