智能调整接受差解几率的RWCE算法

强制进化随机游走算法（RWCE）应用于换热网络优化，具有算法流程简洁、结构进化能力强等特点。其中接受差解几率对个体跳出局部最优有重要的影响。本文统计优化后期连续变量和结构变量使得年综合费用下降的次数，分析二者对优化进程的影响，讨论接受差解几率在个体优化进程中所起的作用，提出一种通过判别差解结构与原结构的异同来智能调整接受差解几率的换热网络优化方法。该策略进一步强化个体的结构进化与跳出局部极值的能力，通过算例证明了策略的有效性。

An RWCE Algorithm with Intelligently Adjusted Acceptance Probability of Improper Solutions

Random walk algorithm with compulsive evolution(RWCE) is an effective method for the optimization of heat exchanger network with advantage of simple evolutionary strategy and less control parameters. The probability of accepting improper solution has important influence on individual jumping out of local optimal solution. We count times of total annual cost decrease in a certain iteration interval in the later stage of optimization, and analyze influence of probability of accepting improper solution in the optimization process. A heat exchange network optimization method is proposed to adjust probability of accepting improper solution with difference between structure of improper solution and original structure. It strengthens evolutionary ability of the algorithm. Effectiveness of the strategy is shown with several examples.