The river erosion and deposition algorithm with adaptive search dynamics for balancing exploration and exploitation.

Artificial intelligence (AI) is rife with optimization problems, from automating feature engineering and hyperparameter tuning to training intricate neural networks. Finding a balance between exploration and exploitation remains a significant challenge, despite the widespread usage of metaheuristic algorithms to tackle these complex black-box problems. We suggest the River Erosion and Deposition Algorithm (REDA), a unique search technique for numerical and engineering optimization, as a solution to this problem. In order to simulate a dynamic equilibrium state, the algorithm incorporates an adaptive search weight that alternates cyclically between local exploitation and global exploration. While a randomized Boolean operator preserves population diversity, its position-updating process incorporates a stochastic recombination of current population members with an elite memory set to direct the search. We used 19 constrained engineering optimization problems and 29 unconstrained CEC2017 benchmark test functions in a systematic validation process to assess REDA's performance. According to experimental results, REDA performs noticeably better than 13 cutting-edge comparator algorithms. The higher performance of REDA, especially in low-dimensional areas, is confirmed by statistical analyses based on the Friedman test and the Wilcoxon signed-rank test. Furthermore, when utilized to detect parameters in a solar system, REDA showed good accuracy and stability. Collectively, these tests verify that the proposed method effectively balances exploration and exploitation in difficult solution domains.