针对微分进化(Differential Evolution,DE)算法应用于换热网络优化存在局部搜索能力不足、收敛速度慢和求解精度低等问题，提出一种混合微分进化(Hybrid Differential Evolution,HDE)算法。当DE算法的变异、交叉和选择操作不再使种群的最优值继续进化时，加入梯度操作使当前种群的最优个体趋向更好的解。为了防止算法早熟收敛，当种群的多样性低于设定的阈值时，引入迁移操作，在最优个体附近区域重新生成新的个体并以此替换旧的个体，增强算法的种群多样性。通过算例验证了该算法可以有效适用于换热网络的优化过程，具有更强的局部搜索能力，更快的收敛速度和更高的优化效率。

Because the differential evolution (DE) algorithm is characterized by earlier mature and slow convergence in the later stage of evolution when applied in the optimization of heat exchanger network (HEN).A hybrid differential evolution (HDE) algorithm is put forward to apply in the synthesis of HEN. While the best individual of the current population cannot be generated any longer by mutation and crossover, the gradient method is applied to push the best individuals tends to be a better solution. In order to avoid the problem of premature convergence, a migrating operation is embedded into the DE algorithm when the population diversity fails to match the desired tolerance. The new individual is regenerated based on the best individual and replace it which will maintain the population diversity. Two cases are used to verify the feasibility of the algorithm. The results show that HDE algorithm is effective to improve local search ability, speed up convergence, heighten optimization efficiency.Key words

TP273

上海市科委部分地方院校能力建设计划（16060502600）; 国家自然科学基金(51176125);沪江基金研究基地专项（D14001）