Using the Improved Rain Optimization Algorithm to Simulate the Movement of Two Dominant Fluids in the Fracture and the Matrix

Rain Optimization Algorithm (ROA) is a population-based algorithm that finds the optimal solution for complex optimization problems by simulating the movement of raindrops. By moving the raindrops towards the minimum points according to the diameter of the raindrops, this algorithm is able to find the minimum or maximum points of a function or optimization problem with acceptable speed and accuracy. In order to improve the search and discovery capabilities of this algorithm, a random search was added before starting to solve the problem by this algorithm, which is inspired by cluster bombs. Thus, before starting to optimize by ROA, random points around this raindrop are first selected and the search starts from a point that has a smaller value. For this reason, the name of the new algorithm was changed to the improved IROA rain optimization algorithm. The effectiveness of the proposed optimizer was tested through the optimization of a simulation problem in mining engineering (simulation of the movement of cement slurry in matrix and fractures) and its performance was compared with several well-known meta-heuristic algorithms. The results show that IROA is able to achieve more accurate solutions in complex optimization problems by providing faster and more efficient convergence speed compared to other successful optimizers.