【摘要】 本研究针对绿色建筑多目标节能优化问题，致力于同时实现节能效果的最大化和节能投入成本的最小化。为达到此目标，研究设计了一个融合了遗传算法的多目标节能优化模型，并通过引入粒子群算法改善求解过程。结果表明，研究设计模型求得最佳组合方案F,其增量成本为84.23元/m~2,增量效益为1 226.41元/m~2,节能设计效率高达14.61。综上，研究模型在绿色建筑节能方案选择中显示出了卓越的性能。特别是方案F和方案H不仅成本效益更佳，长期节能效率也更高，适用于节能投资收益要求不同的项目。此外，对基准收益率的敏感性分析进一步证明了模型在不同经济情景下具有可靠的决策支持能力。本研究为投资者提供了科学的节能决策参考，有助于推动绿色建筑行业实现节能与成本双重优化的目标。更多还原

【Abstract】 This study focuses on the multi-objective energy-saving optimization problem of green buildings, aiming to achieve the maximization of energy-saving effects and the minimization of energy-saving investment costs simultaneously. To achieve this goal, a multi-objective energy-saving optimization model integrating genetic algorithm was studied and designed, and the solving process was improved by introducing particle swarm optimization algorithm. The results show that the optimal combination scheme F obtained from the research and design model has an incremental cost of 84.23 yuan/m~2, an incremental benefit of 1226.41 yuan/m~2, and an energy-saving design efficiency of up to 14.61. In summary, the research model has shown excellent performance in the selection of energy-saving solutions for green buildings. Especially Scheme F and Scheme H not only have better energy-saving efficiency but also higher intellectual efficiency, which are suitable for projects with different requirements for energy-saving investment returns. In addition, the sensitivity analysis of the benchmark return further proves that the model has reliable decision support ability in different economic scenarios. This study provides investors with scientific energy-saving decision-making references, which helps to promote the green building industry to achieve the dual optimization goals of energy conservation and cost.更多还原