Random Walk algorithm with Compulsive Evolution can simultaneously optimize the continuous (heat load) and integer (structure) variables in heat exchanger networks, by giving a random step length to heat load and setting the minimum heat load. Meanwhile, the structural mutation is allowed, as imperfect solutions may be accepted, in the low probability event that it may avoid entrapment in the local optima. Consequently, the step length influences the efficiency of both continuous and integer variables optimization significantly. Specifically, this study observes the distribution of step length during the evolution process, while analyzing its effects on the structural mutation and optimization. The observation found that a sufficiently large step length can stimulate structure optimization and enhance the efficiency of escaping local optima. On this basis, the study proposes an enhancing strategy promoted by large step length to establish a rational distribution curve of step length during the evolution while also advance the structure optimization of heat exchanger networks. Finally, the analysis of four different cases verifies the validity of the presented enhancing strategy, showing that the proposed approach promoted by large step length facilitates the structure optimization and achieves more cost-effective results, compared to other existing strategies.

带有强制进化的随机游走算法可通过为热负荷赋予随机步长并设置最小热负荷来同时优化热交换器网络中的连续（热负荷）和整数（结构）变量。同时，由于结构突变可能会被接受，因为可以接受不完善的解决方案，因此可以避免陷入局部最优的低概率事件。因此，步长会显着影响连续变量和整数变量优化的效率。具体而言，本研究观察了进化过程中步长的分布，同时分析了步长对结构突变和优化的影响。观察发现，足够大的步长可以刺激结构优化并提高逃避局部最优的效率。在此基础上，研究提出了一种大步长促进的增强策略，以建立演化过程中步长的合理分布曲线，同时也促进了换热网络的结构优化。最后，对四种不同情况的分析验证了所提出的增强策略的有效性，表明与其他现有策略相比，以大步长推动的拟议方法可促进结构优化并获得更具成本效益的结果。