High-dimensional numerical optimization presents a persistent challenge in computational science. This paper introduces Quasi-Adaptive Search with Asymptotic Reinitialization (QUASAR), an evolutionary algorithm to accelerate convergence in complex, non-differentiable problems afflicted by the curse of dimensionality. QUASAR expands upon the core principles of Differential Evolution (DE), introducing quasi-adaptive mechanisms to dynamically balance exploration and exploitation in its search. Inspired by the behavior of quantum particles, the algorithm utilizes three highly stochastic mechanisms that augment standard DE: 1) probabilistic mutation strategies and scaling factors; 2) rank-based crossover rates; 3) asymptotically decaying covariance reinitializations. Evaluated on the notoriously difficult CEC2017 benchmark suite of 29 test functions, QUASAR achieved the lowest overall rank sum (367) using the Friedman test, outperforming DE (735) and L-SHADE (452). Geometric mean comparisons show average final solution quality improvements of $3.85 \times$ and $2.07 \times$ compared to DE and L-SHADE, respectively ($p \ll 0.001$), with average optimization speed averaging $1.40 \times$ and $5.16 \times$ faster. QUASAR's performance establishes it as an effective, efficient, and user-friendly evolutionary algorithm for complex high-dimensional problems.

翻译：高维数值优化是计算科学中长期存在的挑战。本文提出具有渐近重初始化特性的准自适应搜索算法（QUASAR），这是一种旨在加速受维度诅咒影响的复杂、不可微问题收敛的进化算法。QUASAR 在差分进化（DE）的核心原理基础上进行扩展，引入准自适应机制以动态平衡搜索过程中的探索与利用。受量子粒子行为的启发，该算法采用三种高度随机化的机制来增强标准 DE：1）概率性变异策略与缩放因子；2）基于排序的交叉率；3）渐近衰减的协方差重初始化。在包含 29 个测试函数的著名困难基准测试集 CEC2017 上进行评估，QUASAR 在 Friedman 检验中获得了最低的总秩和（367），优于 DE（735）和 L-SHADE（452）。几何平均比较显示，与 DE 和 L-SHADE 相比，其最终解质量平均分别提升了 $3.85 \times$ 和 $2.07 \times$（$p \ll 0.001$），平均优化速度分别快了 $1.40 \times$ 和 $5.16 \times$。QUASAR 的性能表明，它是一种针对复杂高维问题有效、高效且用户友好的进化算法。