中文摘要：

换热网络模型具有非凸和非线性的特性,对于大规模超结构优化问题,采用经典的智能算法优化效率低,容易陷入局部最优值。以年综合费用为目标函数,基于自适应竞争群优化算法对无分流分级超结构换热网络模型进行优化。该方法采用对粒子平均位置的递减学习,通过自适应调节速度权重提高换热网络结构的全局优化能力和局部优化能力。通过两个典型算例分析表明,该方法相比量子粒子群算法大幅减少了模型调用次数,缩短了运行时间,并且找到了更好的优化结果。

英文摘要：

For large-scale super-structure optimization on non-convex and non-linear heat exchanger network, conventional intelligent optimization algorithms have poor efficiency and easily fall into local optima. Adaptive competitive swarm optimization algorithm was proposed to optimize no-split stream heat exchanger networks with targeted total annual cost. This method improved abilities of both global and local optimization by attenuated learning of average particle positions and self-adaptive adjusting on weight average of speed. Simulation on two typical cases showed that the proposed algorithm sharply reduced cycles of model being used, shortened optimization time and achieved better optimization results in comparison with quantum particle swarm algorithm.