<![CDATA[With the national emphasis on building energy efficiency planning, energy efficiency optimization in existing buildings requires renovation measures based on multi-objective factors. In order to get the optimal solution in the multi-objective decision-making of renovation, the study proposes a class of improved grasshopper optimization algorithms. The process employs a systematic methodology to identify an optimal energy renovation method, taking into account the specific characteristics of the building environment. It then classifies and formulates the energy reduction substitution items for building renovation, and finally, it synchronizes the cost of the renovation project as a measure for decision-making. The elite inverse strategy approach enhances the grasshopper optimization algorithm to facilitate the multi-objective decision-making process associated with building renovation measures. The results showed that the improved grasshopper optimization algorithm could achieve a decision accuracy of 98.8% for the test samples, which was 5.5% higher than the accuracy of the particle swarm optimization algorithm. Repeated run tests of the research algorithm for multi-objective decision making yielded a mean decision fitness value of 2.34×104 and a data extreme value of 0.38×104. Compared to other algorithms improved grasshopper optimization algorithm converged in a lower range of fitness values, which indicated that the algorithm worked well for multi-objective optimization and the model repeatability was good. The research algorithm was used to decide the energy efficient renovation planning of the building and the power consumption of the renovated power supply system was reduced by 23.7%-49.6%. This indicates that the renovated building has better energy efficiency and can provide a reliable technical direction for decision-making optimization of building energy efficiency renovation.]]>
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