Decomposition based multi-objective evolutionary algorithm for energy-saving design of homestay buildings - Wu, Xiaohong; Peng, Yingzhi (Center of Biomass & Renewable Energy (CBIORE), 2024)
To improve the prediction accuracy of energy-saving design for homestay buildings, a multi-objective optimization model is studied. A model of multi-objective optimization algorithm for energy efficiency design of home stay buildings based on decomposition multi-objective evolutionary algorithm is proposed. Decomposition based multi-objective evolutionary algorithm is selected. To select the preliminary algorithm for achieving energy-saving design of homestay buildings, it divides the objectives into algorithm determination and model construction and uses multi-objective optimization algorithms to solve the proposed optimization model. The validation results show that the minimum discomfort time calculated using the non-dominated sorting genetic algorithm is 555.30 and the energy consumption is 7.68, while the minimum discomfort time calculated using the non-dominated sorting genetic algorithm method is 896 and the energy consumption is 8.92. With alternative model, the speed of multi-objective Evolutionary algorithm is the fastest, reaching 6105.44 seconds, which is 68.80% lower than the proposed method. With the help of substitutes, the computational speed of the multi-objective particle swarm optimization algorithm has been greatly improved. Its computational speed has reached 1217.231 seconds, while the fastest multi-objective particle swarm optimization algorithm among the four comparison methods is only 3868.591 seconds. Although the individual improvement is not significant, the overall optimization is still considerable and has strategic foresight in the decision-making plan of decision-makers.