Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月26日

查看全文
  • Jieling Wang,
  • Yanfei Liu,
  • Zhaoyun Luo,
  • Chao Li,
  • Zhong Wang

摘要

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.