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<p>Metaheuristic algorithms are effective for complex optimization, yet their performance is frequently hindered by reliance on manual parameter tuning and rigid exploration-exploitation transitions, often leading to premature convergence. To address these challenges, we propose a hybrid framework named the Reinforcement Learning-Driven Differential-Moss Growth Optimizer (RLDMGO). This method employs a Q-learning controller to adaptively schedule a Differential Evolution (DE) mechanism for global exploration and a Moss Growth Optimization (MGO)-based operator for local exploitation. This dynamic feedback-driven coordination helps reduce parameter dependence and enhances adaptability across diverse problem landscapes. The performance of RLDMGO is evaluated on the IEEE CEC2017 benchmark suite. Statistical evaluations, including the Friedman test, indicate that RLDMGO achieves a competitive ranking among fourteen evaluated state-of-the-art competitors. To demonstrate its practical applicability, a binary variant (bRLDMGO) is applied to feature selection across 12 public datasets, generally yielding lower classification error rates and smaller feature subsets compared to seven binary optimizers. Furthermore, RLDMGO is deployed as an automated Neural Architecture Search (NAS) engine for cellular traction force computation. It optimizes a 3D U-Net, reducing model parameters to 2.47&#xa0;M while maintaining competitive prediction accuracy compared to existing deep learning models. Ultimately, this study suggests that RLDMGO can serve as a viable and adaptive solver for complex optimization problems.</p>

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RLDMGO: a reinforcement learning-driven hybrid framework for complex optimization problems

  • 期刊论文

  • 发表日期：

    2026年8月4日

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  • Zheng Shen,
  • Zhiqing Chen,
  • Yuejin Zhang,
  • Kejian Fu,
  • Wenlei Fan,
  • Sudan Yu,
  • Xiaoxia Jin

摘要

Metaheuristic algorithms are effective for complex optimization, yet their performance is frequently hindered by reliance on manual parameter tuning and rigid exploration-exploitation transitions, often leading to premature convergence. To address these challenges, we propose a hybrid framework named the Reinforcement Learning-Driven Differential-Moss Growth Optimizer (RLDMGO). This method employs a Q-learning controller to adaptively schedule a Differential Evolution (DE) mechanism for global exploration and a Moss Growth Optimization (MGO)-based operator for local exploitation. This dynamic feedback-driven coordination helps reduce parameter dependence and enhances adaptability across diverse problem landscapes. The performance of RLDMGO is evaluated on the IEEE CEC2017 benchmark suite. Statistical evaluations, including the Friedman test, indicate that RLDMGO achieves a competitive ranking among fourteen evaluated state-of-the-art competitors. To demonstrate its practical applicability, a binary variant (bRLDMGO) is applied to feature selection across 12 public datasets, generally yielding lower classification error rates and smaller feature subsets compared to seven binary optimizers. Furthermore, RLDMGO is deployed as an automated Neural Architecture Search (NAS) engine for cellular traction force computation. It optimizes a 3D U-Net, reducing model parameters to 2.47 M while maintaining competitive prediction accuracy compared to existing deep learning models. Ultimately, this study suggests that RLDMGO can serve as a viable and adaptive solver for complex optimization problems.