通过个体层面的策略多样性释放微分进化潜力
Unleashing the Potential of Differential Evolution through Individual-Level Strategy Diversity
AI总结：
iStratDE通过在个体层面独立分配突变和交叉策略，提升微分进化算法的搜索性能和并行效率。
摘要：
由于微分进化（DE）对策略选择敏感，现有大多数变体通过自适应机制或复杂设计来追求性能。虽然这些方法关注于随时间调整策略，但静态策略多样性所带来的结构优势仍 largely 未被探索。为弥合这一差距，我们研究了个体层面策略多样性对DE搜索动态和性能的影响，并引入iStratDE（具有个体层面策略的DE），一种最小化变体，它在初始化时将突变和交叉策略独立分配给每个个体，并在整个进化过程中保持不变。通过在个体层面注入多样性而不进行适应或反馈，iStratDE培养出持续的行为异质性，这在大群体中特别有效。此外，其无通信构造具有内在并发性，从而能够实现高效的并行执行和简单的GPU计算扩展。我们进一步在标准可达性假设下提供了iStratDE的收敛分析，这建立了迄今为止最佳适应度的几乎必然收敛。在CEC2022基准套件和机器人控制任务上的大量实验表明，iStratDE与现有自适应DE变体匹配或超越。这些结果突显了个体层面策略分配作为提高DE性能的简单而有效的方法。iStratDE的源代码在https://github.com/EMI-Group/istratde上公开可访问。
英文摘要：
Since Differential Evolution (DE) is sensitive to strategy choice, most existing variants pursue performance through adaptive mechanisms or intricate designs. While these approaches focus on adjusting strategies over time, the structural benefits that static strategy diversity may bring remain largely unexplored. To bridge this gap, we study the impact of individual-level strategy diversity on DE's search dynamics and performance, and introduce iStratDE (DE with individual-level strategies), a minimalist variant that assigns mutation and crossover strategies independently to each individual at initialization and keeps them fixed throughout the evolutionary process. By injecting diversity at the individual level without adaptation or feedback, iStratDE cultivates persistent behavioral heterogeneity that is especially effective with large populations. Moreover, its communication-free construction possesses intrinsic concurrency, thereby enabling efficient parallel execution and straightforward scaling for GPU computing. We further provide a convergence analysis of iStratDE under standard reachability assumptions, which establishes the almost-sure convergence of the best-so-far fitness. Extensive experiments on the CEC2022 benchmark suite and robotic control tasks demonstrate that iStratDE matches or surpasses established adaptive DE variants. These results highlight individual-level strategy assignment as a straightforward yet effective mechanism for enhancing DE's performance. The source code of iStratDE is publicly accessible at: https://github.com/EMI-Group/istratde.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
