提出一种基于遗传算法和受控随机搜索的系统优化策略 .在顶层用遗传算法对系统结构进行优化 ;在底层 ,用受控随机搜索对顶层提供的候选结构进行参数优化 ,并将结果返回给顶层作为遗传算法对相应结构的评判依据 .该方法可用于解决较大规模的系统优化问题 ,并能很好地避免局部最优 .通过典型换热网络优化的例子 ,验证了该方法的有效性 .最后 ,给出了结论 A system optimization strategy based on genetic algorithm and controlled random search is proposed. The genetic algorithm is used to optimize the system structure at the top level. At the bottom level, a controlled random search is used to optimize the candidate structure provided by the top level, and the result is returned to The top layer serves as a basis for judging the corresponding structure by using genetic algorithm, which can be used to solve large-scale system optimization problems and avoid local optimization well. The effectiveness of this method is verified by a typical example of heat transfer network optimization Finally, the conclusion is given