Unleashing the Potential of Differential Evolution through Individual-Level Strategy Diversity
研究了在差分进化中为每个个体独立分配固定突变和交叉策略（个体级策略多样性）对搜索性能的影响，提出iStratDE变体，在CEC2022基准和机器人控制任务上匹配或超越自适应变体，且天然适合GPU并行。
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 MetaDE is publicly accessible at: https://github.com/EMI-Group/istratde.