A multi-objective optimization study of modern architectural heritage based on intelligent algorithms

Abstract

The conservation of modern architectural heritage is different from that of ancient or traditional buildings, and there are special features in the development process from the creation of conservation issues to the implementation of conservation and utilization measures. As the national task of energy conservation and emission reduction increases, the demand for retrofitting the performance of existing buildings increases simultaneously, and China's modern architectural heritage cannot meet the requirements of modern buildings in terms of building energy consumption and indoor comfort. Traditional retrofitting strategies lack quantitative analysis of the retrofitting target and have no clear objectives, making it impossible to achieve the expected results after retrofitting. Therefore, this paper proposes an intelligent algorithm-based multi-objective optimization strategy for retrofitting architectural heritage in order to solve this problem. A neural network is introduced into the genetic algorithm to realize a multi-objective optimization algorithm, which breaks through the limitation of the number of optimization objectives while enhancing the optimization search speed and develops a multi-objective optimization transformation decision process for the reuse of modern architectural heritage. The empirical results prove that the multi-objective optimization transformation decision method proposed in this study can improve the transformation decision efficiency.