中文摘要：

强制进化随机游走算法（random walking algorithm with compulsive evolution,RWCE）是一种优化换热网络的新方法,具有程序简单、算法适应性和全局搜索能力较强等优点。本文研究了最大步长对RWCE算法优化性能的影响,提出了抛物线函数的最大步长递减调整策略来平衡RWCE算法的全局搜索与局部搜索能力。将引入策略的RWCE算法与基础算法比较,发现加入最大步长递减调整策略的RWCE算法与基础RWCE算法相比,在进化后期能够跳出局部极小值,具有更强的局部搜索能力。采用10SP2、9SP和15SP换热网络实例检验加入此策略RWCE算法的有效性,其中10SP2和9SP算例的优化结果均好于文献最好结果,相比算例原始文献下降了20.98%和1.11%。对15SP算例优化找到了新的换热网络匹配结构,并好于多数无分流换热网络优化结果,且低于文献结果 4.60%,证明了此方法在换热网络优化中具有较强的优化能力。

英文摘要：

Random walking algorithm with compulsive evolution（RWCE） is a novel heuristic method to optimize heat exchanger networks,which has a powerful global optimizing ability in the process of evolution. In this paper,the effect of maximal step length on the performance of RWCE algorithm was studied. To efficiently control the global and local search ability of the algorithm,a decreasing maximal step length adjustment strategy based on a parabola opening downwards curve was proposed. Compared with the basic algorithm,the strategy is capable of jumping out of local optima in the late evolution stage and strengthening the local search ability. The optimal results of three HEN cases（10SP2,9SP and 15SP） from literatures were used to test the effectiveness of the RWCE algorithm cooperated with proposed strategy. The results of former two（10SP2 and 9SP）are better than the best results published,which is 20.98% and 1.11% lower than the original literature results. A new heat exchanger networks structure was found in case 3（15SP）,which is better than the majority of optimal results of no stream splits and 4.6% lower than the literature results. The results of these three cases demonstrate that the method enjoys a better optimization capability in the global optimization of heat exchanger network.