## 1 Introduction
The (Differential Evolution with Global and Local neighborhoods) DEGL algorithm [23; 27] has made some improvements in the individual optimization space, emphasizing the influence of topological structure and spatial neighborhood on the evolutionary algorithm and highlighting the role of the idea of spatial segmentation, which can guide us to think deeply about the search area of the evolutionary algorithm [28; 29]. The new model will be widely used in technological improvement. In Reference [20], we independently proposed the spatial topology concept of the DE algorithm and gave relevant proofs. We independently proposed the spatial topology concept of the DE evolution algorithm and gave relevant proofs. Topology concept is a priority direction in expanding search area. [PERSON] and [PERSON] proposed a new version of adaptive differential evolution algorithm [22; 30]. Through the adaptation of mutation factor \(F\) and cross probability \(CR\), the (Self-Adaptive Differential Evolution) SADE algorithm can adjust the individual search precision and search speed from a micro perspective, which will fundamentally change the search pattern of individuals in the spatial neighborhood and will promote the healthy development of the DE algorithm and provide useful help for the dynamic adjustment strategy of the DE-MPFSC algorithm. In addition, the Modified DE algorithm (MDE) was proposed by [PERSON] et al. [21; 31]. The main idea of MDE is to mix the adaptive mechanism of mean and Gaussian distribution to improve the ability of parameters F and CR to update themselves. The Fitness Adaptation Differential Evolution (FADE) was proposed by [PERSON] et al. [21; 32], the strategy selection mechanism of the main idea is that individual selection behaviors can be randomly generated during the evolution process. When individual populations evolve in spatial neighborhoods, the tentative strategy of selecting individuals is also an effective way to expand the diversity of the population. The spatial structure of individual evolution is not completely random. The evolutionary pattern of individuals will be affected by mutations, resulting in structural deviations of individuals, which is not conducive to large-scale population evolution [33], which was proposed by [PERSON]. The structured properties of evolutionary algorithms is one of the important issues we will explore in the future. Especially in terms of the convergence and stability of evolutionary algorithms, this effect is significant. To this end, we should explore new ideas to reduce structural deviations. We roughly reviewed the variants of the DE algorithm and related research work from recent years. Based on the above viewpoints, we have developed an improved model of the DE algorithm and proposed the DE-MPFSC algorithm, which opens up new idea for further research on the DE algorithm and related applications. B: Technical Route
* According to the idea of region segmentation, propose the region screening criteria for individuals in the evolution process;
* Construct the operation mechanism of the DE-MPFSC algorithm, including the introduction of the dynamic mutation factor \(F\) and the dynamic crossover probability \(CR\);
* According to the structural evaluation system, the idea about entanglement degree of the convergence speed and convergence precision of the algorithm is proposed;
* Compare the four classic DE algorithms JDE, OBDE, DEGL and SADE to further verify the advantages of the DE-MPFSC algorithm. The first advantage of the DE-MPFSC algorithm is that it can classify and extract the original imbalanced data, generate an imbalanced data point searching area and balanced data point searching area, further redistribute the data searching unit. The second advantage is that it can clarify the progressive boundaries of imbalanced points and balanced points of the classified conditions.