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

Year: 2026
Type: article
Abstract: 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.
DOI: 10.1038/s41598-026-45131-x
Language: English
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7140682568
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Cites: 46
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