When striving for sustainable development, designers encounter the formidable task of integrating the advantages of daylighting into energy-efficient designs. The process of investigating a multitude of design variables can be arduous and time-intensive. However, computational simulations provide indispensable insights into forecasting performance. A considerable number of these design challenges do not have explicit mathematical solutions; thus, black-box optimization techniques are favored for the purpose of determining the most effective design solutions. Popular meta-heuristic optimization algorithms, including the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA), Sine Cosine Algorithm (SCA), Grey Wolf Optimizer (GWO), Ant Lion Optimizer (ALO), and Multi-Verse Optimizer (MVO), are described and thoroughly analyzed in this article. In addition, the present study run empirical studies to compare these optimization algorithms by applying them to tackle five distinct daylighting and energy optimization problems associated with enhancing building performance. In the test cases, the present study results demonstrate that the PSO and MVO algorithms exhibit better performance compared to the GA and WOA algorithms. The selection of the most suitable algorithm is contingent upon the distinctive attributes of the problem, and the intricacy of the optimization landscape.

中文翻译：

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建筑能耗与采光性能优化方法对比研究


在努力实现可持续发展时，设计师面临着将采光优势融入节能设计的艰巨任务。研究大量设计变量的过程可能是艰巨且耗时的。然而，计算模拟为预测性能提供了不可或缺的见解。其中相当多的设计挑战没有明确的数学解决方案；因此，为了确定最有效的设计解决方案，黑盒优化技术受到青睐。流行的元启发式优化算法，包括遗传算法（GA）、粒子群优化（PSO）、鲸鱼优化算法（WOA）、正余弦算法（SCA）、灰狼优化器（GWO）、蚁狮优化器（ALO）、和 Multi-Verse Optimizer (MVO)，本文进行了描述和彻底分析。此外，本研究还进行了实证研究，通过应用这些优化算法解决与增强建筑性能相关的五个不同的采光和能源优化问题来比较这些优化算法。在测试用例中，本研究结果表明，与 GA 和 WOA 算法相比，PSO 和 MVO 算法表现出更好的性能。最合适算法的选择取决于问题的独特属性以及优化环境的复杂性。