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一个长期存在的挑战。本文提出了一种具有渐进式重初始化机制的准自适应搜索算法（QUASAR），这是一种旨在加速受维度灾难影响的复杂、不可微问题收敛的进化算法。QUASAR 基于差分进化算法的核心原理进行扩展，引入了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，该算法采用了三种高度随机的机制来增强标准差分进化算法：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、公认困难的CEC2017基准测试集上进行评估，QUASAR使用弗里德曼检验获得了最低的总秩和（367），优于差分进化算法（735）和L-SHADE算法（452）。几何平均比较显示，与差分进化算法和L-SHADE算法相比，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的表现确立了其作为一种针对复杂高维问题的有效、高效且用户友好的进化算法的地位。