Art School of Jiangsu University, Zhenjiang 212000, China.
Comput Math Methods Med. 2022 Aug 22;2022:1771617. doi: 10.1155/2022/1771617. eCollection 2022.
There are many problems in the practical application of landscape lighting design. In order to solve these problems more specifically, based on the relevant theories of interactive genetic algorithm, radial basis function and hesitation degree are introduced into genetic algorithm. Through the analysis and processing of the data to get the optimized interactive genetic algorithm, the algorithm can analyze and optimize the landscape lighting design. Based on this model, the lighting design can be predicted and analyzed, and the prediction result is relatively good. Relevant studies show that the interactive genetic algorithm can be divided into three typical change stages according to the different results of intensity calculation, of which the first stage mainly presents the trend of gradual decline. The fluctuation phenomenon is obvious in the second paragraph. The third paragraph shows a gradual increasing trend of change. The corresponding relationship between the two fitness functions is obvious. With the increase of experts in independent variables, the corresponding fitness values show a trend of gradual decline on the whole. Through the calculation and analysis of five different indicators of landscape lighting by using interactive genetic algorithm, it can be seen that electrification has a relatively small impact on landscape lighting. The results of intelligent and environmental protection calculation are relatively high, and the corresponding range of change is relatively large, which shows that these two indicators are very important for improving the lighting design level of landscape. Finally, the model is verified by comparing data and model curves. Interactive genetic algorithm is very important to improve the lighting design of landscape, and the optimization model can be widely used in other fields.
景观照明设计的实际应用存在诸多问题。为了更具体地解决这些问题，基于交互遗传算法的相关理论，引入犹豫度和径向基函数到遗传算法中。通过对数据的分析和处理得到优化的交互遗传算法，该算法可以对景观照明设计进行分析和优化。基于该模型，可以对照明设计进行预测和分析，且预测结果较好。相关研究表明，交互遗传算法可以根据强度计算的不同结果分为三个典型的变化阶段，其中第一阶段主要呈现逐渐下降的趋势。第二段波动现象明显，第三阶段则表现出逐渐增加的变化趋势。两个适应度函数之间存在明显的对应关系。随着自变量中专家数量的增加，整体上对应的适应度值呈逐渐下降的趋势。通过使用交互遗传算法对景观照明的五个不同指标进行计算和分析，可以看出通电对景观照明的影响相对较小。智能和环保计算的结果较高，对应的变化范围较大，这表明这两个指标对于提高景观照明设计水平非常重要。最后，通过对比数据和模型曲线对模型进行验证。交互遗传算法对提高景观照明设计水平非常重要，优化模型可以广泛应用于其他领域。
