Homotypic clustering optimization algorithm for fitness centre geometric parameter configuration optimization study

Abstract

Although many energy-saving technologies and sustainable building designs have successfully reduced building energy consumption and improved user satisfaction, little consideration has been given to the impact of building spatial configuration on the indoor environment. This study focused on a fitness centre and employed an optimization methodology based on computational fluid dynamics (CFD) simulation. The optimization aims of this study were to enhance interior ventilation efficiency and regulate temperature, resulting in the optimal building spatial characteristics. This work presents the homologous clustering optimization algorithm (HCO) as a solution to the limitations of existing optimization approaches. It also compares HCO with the multi-objective evolutionary mating algorithm (MOMA). HCO with a coefficient of eight performed better than MOMA. The Pareto frontier generated by HCO was more succinct, and the computational time was reduced by 52%. Moreover, an analysis of the CFD simulation results revealed that, in the context of this study, the influence of the interface width coefficient was greater than that of the interface height, and the impact of the cavity width was greater than that of the cavity height. The relationship between changes in air age and variations in geometric parameters was similar to that of indoor temperature.