## 1 Introduction
[PERSON] [30] proposed a DEEP evolutionary algorithm, which utilized an evolution path to enhance the optimization performance of the original DE. It is worth noting that among these DE variants, mutation schemes and parameter control schemes have received more attention from researchers than crossover schemes. The quasi-affine transformation technique proposed by [PERSON] [31] utilizes an affine function to replace the original mutation method, reducing the time complexity of population-based algorithms such as differential evolution (DE) and providing better performance within a limited number of iterations. Overall, the aforementioned method has three deficits. First, the variables related to the objective function in the well location optimization process of previous studies are not comprehensive enough, resulting in outcomes that are insufficient for identifying feasible optimal locations in the field. Second, fracture-vuggy reservoirs contain a large number of invalid grid areas. Simply improving the uniformity of the sampling distribution from a general perspective leads to low sampling efficiency because most of the population individuals sampled in this way have high fitness values, which are not very useful in the entire optimization process. Finally, the insufficient exploration of local optima may lead to missing the global optimum, necessitating repeated exploration later and wasting convergence time. This paper proposes a new evolutionary algorithm improvement strategy based on the geological characteristics of carbonate reservoirs. First, it incorporates the injection-production regime of the first three years into the optimization variables to ensure that the obtained optimal well locations are robust in the field. Second, it introduces an a priori graph technique to encourage more sampling well locations in favorable areas based on human experience, thus improving sampling efficiency. It also proposes a relocation technique to ensure that each sample corresponds to a well location in an active grid, ensuring the reference value of each sample calculation and further enhancing the sampling efficiency. Finally, it adopts a quasi-affine transformation technique to strengthen the exploration around local optima, avoiding inefficient repetitions. Additionally, this paper uses parallel optimization methods to improve computer utilization and further accelerate the computation process. The application of this algorithm in actual carbonate reservoirs showed that its computational performance is better than SDEA, which is one of the current state-of-the-art (SOTA) algorithms. In the following Section 2, this paper introduces the mathematical representation of this task. The Section 3 describes the basic framework of the algorithm, which is the computational process of the differential evolution (DE) algorithm. The Section 4 presents the specific innovative design ideas of the algorithm. The Section 5 describes the computational process of the entire algorithm. The Section 6 uses a specific carbonate reservoir case to illustrate the advantages of the algorithm. The final section is the conclusion, summarizing the contributions and application conditions, and proposing future research plans.