针对换热网络同步综合方法的不足，本文提出了一种新型 Powell 粒子群算法，具有传统确定性方法的高精度以及启发式方法的高效率。同时针对群体智能算法优化换热网络问题时存在的不足，提出了云记忆体和个体对立策略，有效地避免算法发生早熟现象，扩大搜索范围。为处理整型变量而提出的两条整型变量优化策略与 Powell粒子群算法结合，实现了连续变量与整型变量的同步优化。最后，选取两个经典算例验证算法的性能，均获得了优于文献的结果，表明算法能够找到更优的换热网络结构，是一种处理混合整数非线性问题的有效方法。%Due to the defect ofsimultaneous methods for heat exchanger networks synthesis,a novel Powell particle swarm optimization(PPSO) algorithm was proposed,which has both high precision of the deterministic methods and high efficiency of the stochastic algorithms. For overcoming disadvantages of stochastic algorithms,the cloud memory and the opposite strategy of individualswere proposed,which can avoid premature convergence and expand the search space. In addition,two optimizing strategies of integer variables were combined with PPSO algorithm in order to simultaneously optimize continuous and integer variables. The presented approach was tested on two typical benchmark problems. The obtained solutions are better than that published in the literature. Results showed that the presented algorithm can find better designs,which is conductive to cost saving in industrial production.
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