十三五时期（2016-2020年）是全面建成小康社会的关键期，要求各地方政府摒弃"唯GDP论"，以"新常态"的经济转型为基准，加大第三产业比重，从整体上提升我国产业竞争力。因此，如何有效且快速地完成我国经济结构的转型已受到人们的广泛关注。在该背景之下，本文以第三产业结构为研究主体，将投资组合理论及熵控原理引入产业结构优化问题中，建立了一种新型的多目标产业结构分析模型，选取北京市为研究对象，利用多目标Pareto遗传算法对模型进行求解，得出若干组投资比例组合。实证结果显示，相对于实际数据，模型所得结果在经济增长、能源消耗、碳排放量、就业人数及产业分布公平性方面均较优。
China's 13th Five-Year period (2016-2020) is the critical period for building a moderately prosperous society, requiring local governments to abandon the only GDP rate theory and cite the" new normal "economic transformation as the kernel in development. In addition, the proportion of the Tertiary Industry should be increased as well in order to integral elevate the industrial competitiveness of China. Therefore, how to effectively and quickly complete the transformation of the economic structure in China has been paid widespread attention by scholars. However, previous studies focusing on this problem are mostly lack of the identifications of input proportion for various industries and the entire stability of the industrial structure distribution. Based on this background, the optimization of the Third Industry structure is taken as the research subject, and the investment portfolio theory and Entropy-Controlled principle are introduced into this problem, in order to provide decision support for government departments to establish scientific industrial structure policies in accordance with the 13th Five-Year Plan. Meanwhile, a new multi-objective industrial structure optimization model solved by the Pareto based GA algorithm is developed, in which the synthesis entropy is used to comprehensively measure the quantity and quality of the industrial structure development, and the industries within the Third Industry are seen as a group of securities. In addition, five aspects, including the economic output, energy consumption, carbon emissions, employment level, and the equity of industrial distribution, are taken into account. Moreover, a case study of Beijing City is also implemented, in which the authors used the proposed model, adopting the data from 2013, to predict and adjust the tertiary industry structure in 2014. The results show that the gained several groups of industrial structural portfolios are relatively better than the actual data with respects to above five considered aspects, accordingly proving the effectiveness of the proposed model and the conclusion that through reasonable adjustment of industrial structure it is possible to improve the economic output, employment level and the equity of industrial distribution without increasing energy consumption and carbon emissions.
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