Monte Carlo modeling has emerged as a powerful tool to describe system state variations in many engineering systems. If distributed species are involved, the so-called Gillespie-based kinetic Monte Carlo (kMC) simulations are very promising, provided that the search method to identify individual population members is properly chosen. A comparative study is therefore performed on the most promising search methods, using the stochastic simulation algorithm and considering system size variations from 10² to 10⁶ employing 10⁷ randomly selected targets. Attention is paid to already applied search methods as well as novel ones based on recent insights. It is demonstrated that for smaller systems the execution time of the linear search method is the lowest, and for larger systems the quaternary tree-based and tetrasection search methods are most suited. The tests are interpreted based on the search/modification times and an analysis based on iteration numbers is addressed.

蒙特卡罗建模已成为描述许多工程系统中系统状态变化的强大工具。如果涉及分布的物种，那么所谓的基于 Gillespie 的动力学蒙特卡罗 ( k MC) 模拟非常有前景，前提是正确选择了识别个体种群成员的搜索方法。因此，对最有前途的搜索方法进行了比较研究，使用随机模拟算法并考虑 使用 10 ^{7 的}系统大小从 10 ²到 10 ^{6 的}变化随机选择的目标。关注已经应用的搜索方法以及基于最近见解的新方法。结果表明，对于较小的系统，线性搜索方法的执行时间最短，而对于较大的系统，四叉树和四分搜索方法最适合。根据搜索/修改时间解释测试，并解决基于迭代次数的分析。