Constrained, mixed-integer and multi-objective optimisation of building designs by NSGA-II with fitness approximation
Constrained, mixed-integer and multi-objective optimisation of building designs by NSGA-II with fitness approximation
复制标题
DOI：
10.1016/j.asoc.2015.04.010
复制
发表时间：
2015-08
期刊：
影响因子：
--
通讯作者：
A. Brownlee;J. Wright
中科院分区：
文献类型：
--
作者：
A. Brownlee;J. Wright
减少建筑能源需求是全球应对气候变化的重要组成部分，而与建筑性能模拟 (BPS) 相结合的进化算法 (EA) 是这项任务中越来越受欢迎的工具。 BPS 的计算密集型阻碍了 EA 在该行业的进一步采用：在时间竞争的环境中，需要数天或更长时间的优化运行是不切实际的。替代适应度模型是该问题的一种可能解决方案，但对于多目标、约束或离散问题（建筑设计中典型的优化问题），很少有方法被证明。本文提出了基于径向基函数网络的代理的修改版本，结合确定性方案，通过允许群体中存在一些不可行的解决方案来处理约束中的近似误差。这些的不同组合与非支配排序遗传算法 II (NSGA-II) 集成，并应用于典型建筑优化问题的三个实例。比较表明，代理和约束处理相结合可提高运行时间和最终解决方案质量。本文最后详细研究了约束处理和适应度景观，以解释性能差异。
Reducing building energy demand is a crucial part of the global response to climate change, and evolutionary algorithms (EAs) coupled to building performance simulation (BPS) are an increasingly popular tool for this task. Further uptake of EAs in this industry is hindered by BPS being computationally intensive: optimisation runs taking days or longer are impractical in a time-competitive environment. Surrogate fitness models are a possible solution to this problem, but few approaches have been demonstrated for multi-objective, constrained or discrete problems, typical of the optimisation problems in building design. This paper presents a modified version of a surrogate based on radial basis function networks, combined with a deterministic scheme to deal with approximation error in the constraints by allowing some infeasible solutions in the population. Different combinations of these are integrated with Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and applied to three instances of a typical building optimisation problem. The comparisons show that the surrogate and constraint handling combined offer improved run-time and final solution quality. The paper concludes with detailed investigations of the constraint handling and fitness landscape to explain differences in performance.