## 1 Introduction
Therefore, in terms of using evolutionary algorithms to select the best strategy, there is not yet a good and general evolutionary algorithm. With the deepening of the research on reinforcement learning [11; 12; 13] and opposition-based learning [14; 15; 16] in recent years, the method of using opposition-based learning combined with evolutionary algorithm has appeared [17; 18; 19], but it has not been widely applied in practice. There are also examples of combining reinforcement learning and evolutionary algorithms to solve problems [20]. [PERSON] et al. used the differential evolution algorithm improved by reinforcement learning to extract the parameters of the photovoltaic model [21]. [PERSON] used reinforcement learning to improve the differential evolution algorithm [22]. Five benchmark examples for minimizing the weight of truss structures are verified, and good results have been achieved. We draw on the ideas in [21; 22] to apply reinforcement learning to the differential evolution algorithm, and we find that the methods proposed in [21; 22] can complement each other's shortcomings. So, we make improvements on this basis and form the prototype of the algorithm in this paper. However, the main function of reinforcement learning is to improve its convergence speed, and it is not a good solution to the problem of premature convergence, so we need an algorithm that can improve both the convergence speed and the convergence accuracy. Therefore, this paper proposes a differential evolution algorithm (RODE) based on reinforcement learning and opposition-based learning. The modified algorithm can accurately and quickly select the optimal security strategy set. Before using it, we must first evaluate the security status of ICS. We constructed the Bayesian attack graph [7] of ICS, calculated the probability of each attribute node being attacked, and obtained the attack benefit. That is the value at risk. In order to weigh the benefits and costs, we quantify the attack benefits and protection costs [23; 24], and we establish an objective function: minimize the attack benefits and protection costs, and the protection cost should maximize the allowable protection cost. Finally, security strategy selection is performed by using a hybrid differential evolution algorithm based on reinforcement learning and opposition-based learning. We compared the effects of similar algorithms and finally verified the feasibility and effectiveness of the method in the real industrial control environment. In summary, the main contributions of this paper are:  
* A differential evolution algorithm based on reinforcement learning and opposition-based learning is proposed and successfully applied to industrial control strategy selection. * Improve the existing enhanced differential evolution algorithm, shorten the convergence speed of the algorithm, and better avoid it falling into the local optimal solution, which is more suitable for strategy selection. * Compared with other similar algorithms, it is proved that the differential evolution algorithm based on reinforcement learning and opposition-based learning proposed in this paper has more advantages in the field of industrial control strategy selection. The rest of the paper is organized as follows. Section 2 describes the method architecture for implementing optimal strategy selection. Section 3 describes how to assess the risk of industrial control systems. Section 4 describes the construction of security measures and objective functions, and in Section 5, the differential evolution algorithm based on mixed strategy proposed in this paper is introduced in detail. Section 6 analyzes the experimental results in detail, and Section 7 is the final summary.