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.

翻译：由于差分进化（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提升。尽管这些方法侧重于随时间调整策略，但静态策略多样性可能带来的结构优势在很大程度上仍未得到探索。为填补这一空白，本研究探讨了个体层面策略多样性对DE搜索动态与性能的影响，并提出iStratDE（具有个体层面策略的DE）——一种极简变体，其在初始化阶段为每个个体独立分配变异与交叉策略，并在整个进化过程中保持固定。通过无需自适应或反馈的个体层面多样性注入，iStratDE培育出持久的行为异质性，该特性在大规模种群中尤为有效。此外，其无通信的架构具有内在并发性，从而支持高效的并行执行和面向GPU计算的直接扩展。我们进一步基于标准可达性假设对iStratDE进行了收敛性分析，证明了当前最优适应度的几乎必然收敛性。在CEC2022基准测试集与机器人控制任务上的大量实验表明，iStratDE达到或超越了现有自适应DE变体的性能。这些结果揭示了个体层面策略分配作为一种简单而有效的DE性能增强机制。iStratDE的源代码已公开于：https://github.com/EMI-Group/istratde。