Abstract
This study develops a novel multi-agent augmented NSGA-II architecture specifically designed to efficiently handle high-dimensional multi-objective optimization challenges in building energy-efficient design. In this paper, the share Q method is abandoned, and a novel crowding evaluation and comparison mechanism is adopted to ensure comprehensive coverage of the quasi-Pareto frontier while maintaining the diversity of the population. After integrating fast non-dominated sorting, the computational pressure of the algorithm can be effectively reduced. The integration of elite strategies further expands the solution space and prevents the omission of optimal solutions, thereby improving the operating efficiency and stability of the algorithm. After an in-depth analysis of 50 actual building examples, the results show that compared with the conventional NSGA-II method, our method optimizes the quality and diversity of Pareto solutions, with an average improvement of 12% and 15% respectively, while significantly shortening the calculation time, bringing an innovative and efficient optimization path to the energy-saving practice of building design.
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