Generally, the actual explosive is not suitable for the training of security personnel due to its danger. Hence, it is significant to create the simulant as similar as possible to the real explosive, where the difficulties are derived from finding safe compounds from the compound database and their related proportion. In this paper, a cooperative co-evolutionary comprehensive learning particle swarm optimizer is proposed to obtain the formulation design of explosive simulant. To be specific, the proposed algorithm employs particle swarm optimization as the optimizer and creates two cooperative populations focusing on finding compounds and their proportions, respectively. Moreover, a comprehensive cooperative strategy is designed to improve the solution diversity and thus enhance the search performance. To the best of our knowledge, this is the first attempt to employ evolutionary algorithm to design explosive simulant formulation. Comprehensive experiments are conducted on several typical explosives and results demonstrate the superiority of the proposed algorithm in comparison to other algorithms.

通常，由于其危险性，实际爆炸物不适合训练安全人员。因此，重要的是要创建尽可能类似于真实炸药的模拟物，其中困难是从化合物数据库及其相关比例中找到安全化合物而产生的。本文提出了一种协同协同进化的综合学习粒子群优化器，以得到炸药模拟物的配方设计。具体来说，该算法采用粒子群优化算法作为优化程序，并创建了两个合作群体，分别致力于寻找化合物及其比例。此外，设计了一种全面的合作策略来提高解决方案的多样性，从而提高搜索性能。据我们所知，这是采用进化算法设计爆炸性模拟制剂的首次尝试。对几种典型的炸药进行了全面的实验，结果证明了该算法相对于其他算法的优越性。