强制进化随机游走算法优化换热网络时具有程序简单、全局搜索能力强等特点，但在优化过程中不同的进化阶段对进化概率需求不同，算法中设置单一的进化概率难以满足实际进化需求。因此，提出一种进化概率差异化策略，该策略的核心思想是智能识别流股的换热程度，动态调整进化概率，对存在公用工程的流股强制参与进化，使算法在前期具有较强的结构搜索能力；完全换热的流股通过降低进化概率，提升算法全局的精细搜索能力。采用15SP和39SP算例进行验证，所获年综合费用分别为1 494 690和1 894 477 $/a，验证了该策略能够提升算法的优化效率与质量。

The optimization of heat exchanger network by the random walk algorithm with compulsive evolution has the advantages of simple procedure and strong globle searching ability.However,the different evolutionary probabilities are required in different evolutionary stages during the optimization,and a single evolutionary probability is difficult to meet the actual needs of different stages.Therefore,an evolutionary probability differentiation strategy is proposed,and its core ideas are the heat exchange level of streams identified by intelligence,the evolutionary probability adjusted by dynamic condition and the evolution participated compulsively by streams with utility,which makes the algorithm have strong structure searching ability in the early stage.For the streams with full heat exchange,the globle fine searching ability of the algorithm is enhanced by reducing evolutionary probability.The 15SP and 39SP examples are used to verify the results,and the annual comprehensive costs are 1 494 690 $/a and 1 894 477 $/a, respectively.The results show that the strategy can improve the optimization efficiency and quality of the algorithm.

TK124

国家自然科学基金（21978171，51976126）；中国博士后科学基金（2020M671171）