Generally, there are different optimal solutions with regard to urban landscape planning depending on the area and the opinions and characteristics of community residents. Furthermore, when considering urban landscape and/or city-planning regulations, it is important to include residents’ opinions based on voluntary activities like participation in town development on a regional scale and its management. However, residents’ opinions are difficult to quantify, as many do not have specialized knowledge. Therefore, when an administrative body plans a city, a system to include residents’ opinions on urban landscape options is required. In this study, an optimization system for urban landscape design was proposed using an interactive genetic algorithm (IGA). In this system, three properties of an urban landscape, that is, wall surface positions, heights, and building textures, were varied and the resulting urban landscape images, developed using OpenGL, were subjectively evaluated by users. Weighted scores were then calculated using the paired comparison method. In this system, a site of 200 m × 70 m was assumed and 20 buildings were located on 20 m × 20 m lots. The building widths were fixed at 20 m, and wall positions from the sidewalk varied from 10 m to 20 m at 2 m intervals. The building heights varied from 20 m to 40 m at 4 m intervals, and eight building textures were considered. Two simulations were performed: Case 1, in which the three parameters were evaluated simultaneously; and Case 2, in which the three parameters were evaluated individually. The same 10 users participated in both cases. Following completion of each case, questionnaires were administered to users in which they were asked to confirm that the results obtained matched their expectations. The results demonstrated that individual users were satisfied with the results generated based on their evaluations. In both cases, the results were obtained from the optimal results of the system as the result of questionnaires. It is necessary to re-examine the evaluation order and evaluation method used as evaluation order may affect optimal results. Furthermore, since users generated different optimal results, it is necessary to develop an optimization system for urban landscapes that allows for collaboration between users.

中文翻译：

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基于交互式遗传算法和3D图像的城市景观设计研究

通常，根据区域以及社区居民的意见和特点，在城市景观规划方面存在不同的最佳解决方案。此外，在考虑城市景观和/或城市规划法规时，重要的是要基于自愿活动纳入居民的意见，例如参与区域规模的城镇发展及其管理。但是，居民的意见难以量化，因为许多人没有专门知识。因此，当一个行政机构计划一个城市时，就需要一个包含居民对城市景观选择的意见的系统。在这项研究中，提出了使用交互式遗传算法（IGA）的城市景观设计优化系统。在该系统中，城市景观的三个属性，即墙面位置，高度和建筑物纹理各不相同，并且使用OpenGL开发的最终城市景观图像由用户主观评估。然后使用配对比较方法计算加权分数。在该系统中，假设场地为200 m×70 m，并且在20 m×20 m地段上有20座建筑物。建筑物的宽度固定为20 m，人行道上的墙壁位置以2 m的间隔从10 m更改为20 m。建筑高度从20 m到40 m间隔为4 m不等，并且考虑了八种建筑纹理。进行了两个模拟：情况1，同时评估了三个参数；案例2，其中三个参数分别进行了评估。在这两种情况下，相同的10位用户都参与了。完成每个案例后，对用户进行了问卷调查，要求他们确认所获得的结果符合他们的期望。结果表明，个人用户对基于他们的评估所产生的结果感到满意。在这两种情况下，结果都是从系统的最佳结果中获得的，这是问卷调查的结果。有必要重新检查评估顺序，并且用作评估顺序的评估方法可能会影响最佳结果。此外，由于用户产生了不同的最佳结果，因此有必要开发一种允许用户之间进行协作的城市景观优化系统。在这两种情况下，结果都是从系统的最佳结果中获得的，这是问卷调查的结果。有必要重新检查评估顺序，并且用作评估顺序的评估方法可能会影响最佳结果。此外，由于用户产生了不同的最佳结果，因此有必要开发一种允许用户之间进行协作的城市景观优化系统。在这两种情况下，结果都是从系统的最佳结果中获得的，这是问卷调查的结果。有必要重新检查评估顺序，并且用作评估顺序的评估方法可能会影响最佳结果。此外，由于用户产生了不同的最佳结果，因此有必要开发一种允许用户之间进行协作的城市景观优化系统。