摘要
为应对照明建筑设计集成新趋势下的质量提升、氛围塑造、节能优化的多元照明设计目标,引入了生成式设计手段,创建了用于照明逆设计的数学模型及启发式算法.数学模型以工作面照度和空间亮度系数(Feu)2项指标调控环境的视觉质量和照明氛围,照明功率密度(LPD)指标调控照明节能;启发式算法对描述光通量、灯具的横向/纵向间距、地面/墙面平均反射率的5维决策变量进行优化,获取最优方案.为确定最佳算法,基于Python算法库scikit-opt将遗传算法(GA)、差分进化算法(DE)、粒子群算法(PSO)、人工鱼群算法(AFSA)用于数学模型的优化,并以1个逆设计函数、4个数学测试函数为测试基准,比较算法的准确性、稳定性、计算效率3类效能.根据比较结果,创建了用于逆设计的多策略算法(inversedesign multi-strategy algorithm,IDMSA),其特点包括:(1)根据运行阶段和寻优状态决定个体执行DE还是AFSA的寻优操作;(2)基于冯·诺依曼邻域执行AFSA的聚群和追尾行为;(3)采用自适应步长.用提升寻优难度的测试基准验证算法效能.结果表明:IDMSA表现出最好的准确性和稳定性,计算效率优于AFSA和DE;使用逆设计数学模型和IDMSA算法可有效实现工作面照度-Feu-LPD的联合优化,生成方案的工作面照度和Feu与参考值的偏差小于5%,统一眩光值(UGR)和照度均匀度符合规范要求;IDMSA所得方案的LPD比DE低2.6%,比PSO低6.1%,节能效果更优.
The new trend of lighting-building design integration necessitates the multiple lighting design goals of quality improvement,atmosphere forming,and energy saving.This study introduces generative methods,a mathematical model for inverse lighting design,and an improved heuristic algorithm to meet these goals.This model adjusts the spatial luminance coefficient(Feu)and the illuminance on the working surface to improve the visual quality and ambiance,as well as optimize energy saving via controlling lighting power density(LPD).The heuristic algorithm optimizes 5-dimensional decision variables that describe the luminous flux,lateral/longitudinal distance,and the average wall/floor reflectivity to obtain the optimal solution.To determine the best algorithm,the performance of four algorithms,namely,genetic algorithm(GA),differential evolution(DE),particle swarm optimization(PSO),and artificial fish swarm algorithm(AFSA),was compared for inverse design optimization via the Python library scikit-opt.Then,one inverse design and four mathematical test functions were set as benchmarks to compare the accuracy,stability,and computational efficiency of the algorithms.The inverse design multi-strategy algorithm(IDMSA)was created based on these results.Its features include:①The choice between DE or AFSA operations is determined by the stage and the state of optimization;②The clustering and tail-chasing behavior of AFSA occurs in the von Neumann neighborhood;③The step parameter is adaptive.The efficacy of IDMSA was verified based on benchmarks that were modified to be more difficult to optimize.The results demonstrate that IDMSA achieves the best accuracy and stability and is faster than AFSA and DE.The joint optimization of the illuminance-Feu-LPD can be realized via the proposed mathematical model and IDMSA.The generated scheme conforms to the unified glare rating and uniformity limit of the lighting standard.The deviation of illuminance and Feu from the respective reference values were less than 5%.Furthermore,the LPD obt
作者
陈鹏
王立雄
王爱英
吴雨婷
于娟
Chen Peng;Wang Lixiong;Wang Aiying;Wu Yuting;Yu Juan(School of Architecture,Tianjin University,Tianjin 300072,China;Tianjin Key Laboratory of Architectural Physics and Environmental Technology,Tianjin 300072,China)
出处
《天津大学学报（自然科学与工程技术版）》
EI
CAS
CSCD
北大核心
2023年第10期1090-1101,共12页
Journal of Tianjin University：Science and Technology
基金
国家重点研发计划资助项目(2018YFC0705100)
国家自然科学基金资助项目(51678399,52278120)。
关键词
照明建筑设计集成
逆设计
启发式算法
算法效能
算法改进
lighting-building design integration
inverse design
heuristic algorithm
algorithm efficacy
algorithm improvement
