## 1 Introduction  
Now with the development of the Industrial Internet, the network attacks on industrial control systems are more widespread, and the network attacks faced by industrial control systems are very different from those faced by traditional IT systems. The biggest possible loss loss loss after an attack is information leakage, data tampering, etc., and major dangerous accidents often do not occur directly, because the most important thing in traditional IT information systems is the confidentiality of information. Unlike IT systems, cyber attacks against ICS often lead to serious consequences, such as environmental pollution, equipment damage, and even casualties. Among the well-known cyber attacks on ICS include the Iranian \"Stuxnet\" [1] incident in 2010 and the Ukraine power grid cyber attack [2] in 2015. Therefore, the network security problem of ICS needs to be urgently solved. Due to the dynamic nature of ICS systems, the size of the attack surface and the diversity of attack vectors, addressing the cybersecurity of ICS is a particularly difficult task. New system vulnerabilities are reported daily, while cybersecurity problems are often not bounded by physical boundaries, as the source of malicious action can be anywhere in the world. Given these threats, it is extremely important to have appropriate security strategies in place in advance [3, 4, 5]. Therefore, how to find the best strategy set among many security strategies has a wide range of application scenarios in industrial control prevention and response. Evolutionary algorithms have strong scalability and are not limited by the nature of the problem, and they have great advantages in the field of finding the best security strategy. In the aspect of evolutionary algorithm, to find the best security policy set, predecessors have conducted a lot of related research. [PERSON] proposed Genetic Algorithm (GA) [6] and [PERSON] et al. proposed an optimal security protection strategy selection model based on a Bayesian attack graph and used a particle swarm algorithm to find the optimal strategy set [7]. The evolutionary algorithms used above are relatively easy to fall into the local optimal solution, they are greatly affected by the initial number of sample populations, and there is a problem in that the convergence speed is not fast enough. In addition, inspired by the predation behavior of bats, [PERSON] proposed a novel bionic algorithm--the Bat Algorithm (BA) in 2010 [8]. Due to its simple structure and strong applicability, BA has been widely used. However, similar to many intelligent algorithms, BA itself also has problems such as easily falling into local optimum, slow convergence in later stages, having a single coding scheme, lack of a theoretical basis for parameter determination. [PERSON] et al. proposed a differential evolution algorithm with a two-stage optimization mechanism [9]. In the first stage, the search range of the algorithm can be increased to avoid premature convergence, and the convergence speed can be accelerated in the second stage. Good results are obtained, but the effect of the size of the initial population on the algorithm is not fully accounted for. [PERSON] et al. proposed an algorithm to improve DE by using PSO [10] and achieved good results. Among them, a sigmoid function is used to control the mutation strategy, which is actually very similar to our improvement on CR in this paper, which ensures population diversity to a certain extent. However, they do not have guidelines for setting control parameters, and they use the trial and error method to randomly select values. This can indeed reduce the ability to fall into local optimization, but it will reduce the convergence speed to a certain extent. Moreover, the NP in the experiment is a fixed value of 100, which can not guarantee that the performance of the algorithm can still remain stable in the case of very small samples.