多障碍建筑群图像空间布局智能寻优方法优化

为了解决传统方法大多将注意力放在建筑群体局部优化方面,缺少对整个含多障碍建筑群体空间布局优化研究的问题。通过改进粒子群法研究多障碍建筑群图像空间布局智能寻优方法优化问题。建立寻优问题模型,将最小化最大风速比、最大化采光满足率、最优化容积率作为多障碍建筑群图像空间布局智能寻优目标,依据寻优问题模型建立总目标函数。针对粒子群算法的弊端,对其进行改进;将粒子和种群最优粒子差异程度当成依据对权重进行确定。通过密集距离对粒子和相邻粒子间的密集程度进行描述,依据密集程度,选用逐一去除法对最优解进行更新。针对提出的多障碍建筑群图像空间布局多目标寻优问题,采用改进粒子群算法进行求解。结果表明:采用所提方法优化后,日照满足率虽然略低于优化前;但最大加速比和容积率均更优;所提方法 WBGT指标(湿球黑球温度)高于其他方法。可见所提方法可令各指标均衡最优化,能够保证新陈代谢率低,热适应差的人舒适性。

Optimization of Image Space Layout Intelligent Optimization Method for Multi Obstacle

In order to solve the problem that the traditional methods mostly focus on the local optimization of building groups and lack the research on the spatial layout optimization of the whole multi-obstacle building group, the intelligent optimization method of image spatial layout of multi-obstacle building groups was studied by improving particle swarm optimization method. The model of optimization problem was established. Minimizing the maximum wind speed ratio, maximizing the lighting satisfaction ratio and optimizing the volume ratio were taken as the intelligent optimization objectives of multi-obstacle building group image spatial layout. The total objective function was established based on the optimization problem model. In view of the shortcomings of particle swarm optimization (PSO), we improve it and use the difference degree between the particle and the optimal particle of the population as the basis to determine the weight. The dense degree between particles and adjacent particles was described by dense distance, and the optimal solution was updated by one-by-one removal method according to the dense degree. Aiming at the multi-objective optimization problem of image spatial layout of multi-obstacle buildings, an improved particle swarm optimization algorithm was used to solve the problem. The results show that although the sunshine satisfaction rate is slightly lower than that before optimization, the maximum acceleration ratio and volume ratio are better, and the WBGT index of the proposed method is higher than that of other methods. It can be seen that the proposed method can balance and optimize the indicators, and can ensure the comfort of people with low metabolic rate and poor thermal adaptation.