This study presents a new technique for optimal sizing of the gas turbine as the primary mover of a combined cooling heating and power (CCHP) system in a selected commercial building. Because of the vital effect of four substantial parameters including energetic, energetic, economic, and environmental (4E analysis), they used for optimum sizing of the gas engine for the CCHP system. The optimization process has been performed by a newly developed meta‐heuristic, called Opposition‐Based Learning and Lévy flight owl search algorithm (OLOSA) for an industrial building in Iran. During the optimization, eight constraints from the 4E analysis have been considered. Final results of the proposed OLOSA have been compared with genetic algorithm (GA) and the results declare that the optimal size for the gas engine based on OLOSA and GA for the cost value are 0.1923 and 0.5622, respectively and the optimal value of the gas engine is achieved 90.16 kW and is achieved after 9 generations and 39 generations, respectively which shows the excellence of the presented OLOSA toward GA in both terms of accuracy and convergence.

中文翻译：

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改进的猫头鹰搜索算法，用于使用4E分析确定CCHP系统中燃气发动机的最佳容量

这项研究提出了一种用于优化燃气轮机尺寸的新技术，该燃气轮机是选定商业建筑中冷热电联产（CCHP）系统的主要推动者。由于包括能量，能量，经济和环境（4E分析）这四个重要参数的至关重要的作用，它们用于优化CCHP系统燃气发动机的尺寸。优化过程是由新开发的元启发式算法（称为基于对立的学习和列维飞行猫头鹰搜索算法（OLOSA））针对伊朗的一处工业建筑执行的。在优化过程中，已经考虑了4E分析的八个约束。90.16 kW，分别在9代和39代之后实现，这显示了所提出的OLOSA对GA的卓越性（在准确性和收敛性方面）。