Multi-objective optimization of cellular fenestration by an evolutionary algorithm
Multi-objective optimization of cellular fenestration by an evolutionary algorithm
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DOI：
10.1080/19401493.2012.762808
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发表时间：
2014-01
影响因子：
2.5
通讯作者：
J. Wright;A. Brownlee;M. Mourshed;Mengchao Wang
中科院分区：
文献类型：
--
作者：
J. Wright;A. Brownlee;M. Mourshed;Mengchao Wang
本文描述了基于将建筑物外墙划分为许多规则间隔的小单元的开窗多目标优化设计。通过多目标遗传算法最小化能源使用和资本成本进行了研究： 两种替代问题编码（位串和整数）；应用约束函数来控制窗口的纵横比；以及用可行的设计解决方案进行搜索。结论是，该优化方法能够找到具有创新架构形式的近局部帕累托最优解。通过重复的路径优化以及局部搜索和敏感性分析，我们获得了对解决方案最优性的信心。还得出结论，当窗口纵横比受到约束时，用可行的解决方案进行优化对于获得最佳解决方案非常重要。
This paper describes the multi-objective optimized design of fenestration that is based on the façade of the building being divided into a number of small regularly spaced cells. The minimization of energy use and capital cost by a multi-objective genetic algorithm was investigated for: two alternative problem encodings (bit-string and integer); the application of constraint functions to control the aspect ratio of the windows; and the seeding of the search with feasible design solutions. It is concluded that the optimization approach is able to find near locally Pareto optimal solutions that have innovative architectural forms. Confidence in the optimality of the solutions was gained through repeated trail optimizations and a local search and sensitivity analysis. It was also concluded that seeding the optimization with feasible solutions was important in obtaining the optimum solutions when the window aspect ratio was constrained.