Comparative Evaluation of Genetic and Grey Wolf Algorithms for Building Energy Optimization

Genetic Algorithm (GA) and Grey Wolf Optimization (GWO) can aid building designers in optimizing construction materials, glazing, orientation, and air conditioner sizing. However, their suitability for building optimization via EnergyPlus simulation lacks clear guidance. This paper utilizes GA and GWO to minimize Building Annual Cooling Energy Consumption (ACEC) in Ahmedabad and New Delhi, exploring 196,830 design options through modifications in wall construction, window design and size, roof insulation, building orientation, air conditioner sizing, and cooling setpoints. Both algorithms run for 20 generations with 50 agents each, and their convergence, runtimes, and designs are analysed. In Ahmedabad, GA achieves a 50.72% ACEC, while GWO achieves a higher 51.78% reduction with shorter runtimes. In Ahmedabad, GA suggests Cellular Concrete walls, GWO prefers Aerated Concrete. Both agree on 5% window–wall ratio, Triple Glazing Windows with 0.5-m overhang, similar AC sizes except Bedroom 3. They differ in roof insulation (GA: Glass wool, GWO: Expanded Polystyrene). In New Delhi, GA and GWO closely align, recommending Aerated Concrete walls, Triple Glazing Windows with 5% window–wall ratio, a 0.5-m overhang. Both GA and GWO propose Expanded Polystyrene insulation, AC sizes differ in Bedrooms 1, 2, 3, and 4. GA has shorter runtimes—0.91 h in Ahmedabad and 0.93 h in New Delhi—than GWO’s 1.78 and 2.05 h. The faster convergence of GWO, attributed to its broader search space, underscored its potential as an efficient optimization strategy, promoting sustainable building design.