用强化学习自动设计适合特定问题的差分进化算法
Reinforcement learning Based Automated Design of Differential Evolution Algorithm for Black-box Optimization
- 用强化学习做元优化，自动配置差分进化算法的策略和参数
- 在40种策略组合中搜索，针对不同问题生成定制化算法配置
- 适合需要高效求解黑箱优化的科研与工程场景
差分进化（DE）算法因其无梯度特性，在黑箱优化中表现出卓越性能。尽管已有大量改进方法，但尚无单一变体能在所有问题上通用最优。为此，本文提出一种基于强化学习（RL）的自动化框架，通过元学习实现对黑箱优化问题的自适应DE设计。该框架利用双深度Q网络，同时考虑40种可能的策略组合与参数优化，根据问题特征生成最优的初始化策略、更新规则及超参数配置。实验在多个黑箱优化基准上评估，结果表明该框架具有显著潜力。
原文摘要 · Abstract (English)
Differential evolution (DE) algorithm is recognized as one of the most effective evolutionary algorithms, demonstrating remarkable efficacy in black-box optimization due to its derivative-free nature. Numerous enhancements to the fundamental DE have been proposed, incorporating innovative mutation strategies and sophisticated parameter tuning techniques to improve performance. However, no single variant has proven universally superior across all problems. To address this challenge, we introduce a novel framework that employs reinforcement learning (RL) to automatically design DE for black-box optimization through meta-learning. RL acts as an advanced meta-optimizer, generating a customized DE configuration that includes an optimal initialization strategy, update rule, and hyperparameters tailored to a specific black-box optimization problem. This process is informed by a detailed analysis of the problem characteristics. In this proof-of-concept study, we utilize a double deep Q-network for implementation, considering a subset of 40 possible strategy combinations and parameter optimizations simultaneously. The framework's performance is evaluated against black-box optimization benchmarks and compared with state-of-the-art algorithms. The experimental results highlight the promising potential of our proposed framework.
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