标题： 基于强化学习的差分进化算法黑盒优化自动设计 显示英文标题

标题： Reinforcement learning Based Automated Design of Differential Evolution Algorithm for Black-box Optimization

摘要： 差分进化（DE）算法被认为是效果最显著的进化算法之一，由于其无需导数的特点，在黑盒优化中表现出卓越的有效性。 对基本DE的大量改进已被提出，结合了创新的变异策略和复杂的参数调整技术以提高性能。 然而，没有一种变体在所有问题上都被证明是普遍优越的。 为了解决这一挑战，我们引入了一个新的框架，通过元学习利用强化学习（RL）自动设计用于黑盒优化的DE。 RL作为一种先进的元优化器，生成一个定制的DE配置，包括针对特定黑盒优化问题的最优初始化策略、更新规则和超参数。 这一过程基于对问题特征的详细分析。 在这一概念验证研究中，我们采用双深度Q网络进行实现，同时考虑40种可能策略组合和参数优化的一个子集。 该框架的性能在黑盒优化基准上进行了评估，并与最先进的算法进行了比较。 实验结果突显了我们所提出框架的潜在前景。 显示英文摘要

摘要： 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.