# Adaptive Dynamic Disturbance Strategy for Differential Evolution Algorithm
## 1 Introduction  
The differential evolution (DE) algorithm was proposed by [PERSON] and [PERSON] in 1995 [1; 2]. It was originally used to solve the Chebyshev polynomial problem, and is a bionic optimization algorithm based on swarm evolution. DE's unique population memory capability gives the algorithm a strong global search capability and robust performance. As a highly efficient parallel search algorithm with few controlled parameters, the DE algorithm has been widely used in neuron networks [3; 4], power systems [5; 6], vehicle routing problems [7; 8; 9; 10; 11] and many other fields [12; 13; 14; 15; 16; 17; 18; 19; 20]. Like other intelligent algorithms, however, the DE algorithm also has disadvantages such as precocity, strong parameter dependence and difficulty in obtaining global optimum values for high-dimensional complex objective functions [21; 22; 23; 24; 25; 26; 27]. Therefore, researchers proposed a number of improvement strategies for DE's existing shortcomings. First, to improve the existing parameters--[PERSON] et al. [28] proposed a variable scaling factor differential evolution algorithm, which does not require mutation selection. The type of operation, compared to the random scaling factor, has a large improvement in performance. Secondly, adding new operations was suggested--[PERSON] et al. [29] proposed a generalized inverse differential evolution algorithm, which introduces generalized opposition-based learning based onreverse learning techniques; this prevents the algorithm from falling into a local optimum to a certain extent. Third, multiple groups--[PERSON] et al. [30] divided the population into independent subgroups, with each having different mutation and updating strategies, and introduced a multi-population DE to solve large-scale global optimization problems. Finally, the fourth suggestion was the hybrid algorithm--[PERSON] et al. [31] proposed a hybrid evolutionary model based on genetic algorithms and differential evolution. The binary variables evolved based on the genetic algorithm and the continuous variables evolved based on the DE algorithm, which was used to solve a nonlinear, high-dimensional, high-dimensional and mixed-integeroptimization problem.  
These classical improved algorithms improve the optimization performance of the DE algorithm to a certain extent, but for some high-dimensional and complex problems, there are still disadvantages of falling into the local optimum. Experiments show that the most critical operation in the DE algorithm is differential variation, and the core of differential variation is the composition of differential vectors. Most of the existing improved algorithms adopt adaptive adjustment parameters or randomly choose a mutation strategy. Although the random strategy improves the global search ability of the algorithm and population diversity, the evolutionary direction of the population is more difficult to control, which may cause the population to deviate from the direction of the optimal value search, and an excessive number of iterations will also lead to a decrease in the convergence rate of the algorithm.  
In order to balance the global optimum and the local optimum, an adaptive dynamic disturbance strategy differential evolution algorithm is proposed in this paper. On the basis of the adaptive adjustment of policy level control parameters, it is added a dimensional variation disturbance strategy. Through the theoretical analysis of algorithm factors and performance testing of standard test functions, the results show that when the adaptive dynamic disturbance strategy for differential evolution (ADDSDE) algorithm is used to solve complex optimization problems such as high-dimensions and multi-peaks, the global optimal solution can be obtained with a minimum number of iterations, and the algorithm has strong robustness.