Learning-assisted algorithm design often has to make reliable search decisions under small evaluation budgets, where committing to a single metaheuristic can be unreliable. We propose WASHH, a Whale-guided Adaptive Selection Hyper-Heuristic for continuous black-box optimization. WASHH uses WOA as the main exploitation backbone, but treats PSO-style memory, GWO-style leader averaging, DE-style variation, local coordinate search, and anchor-guided refinement as selectable search behaviors. An online reward controller allocates evaluations according to observed improvements, while anchor refinement exploits inexpensive reference configurations such as box centers or default model settings without bypassing black-box evaluation. On ten 30-dimensional benchmark functions with 10 independent runs and 12,000 evaluations, WASHH achieves the best average rank, 1.10, and is best or tied best on all ten functions. It strictly improves over WOA on eight functions and ties WOA at the numerical optimum on Rastrigin and Griewank. We further study SVC hyperparameter configuration for breast cancer diagnosis under a 300-evaluation budget. WASHH obtains the lowest mean validation log loss among the compared optimizers, suggesting that anchor-aware selection hyper-heuristics are a practical lightweight direction for LEAD systems.

翻译：学习辅助算法设计通常需要在少量评估预算下做出可靠的搜索决策，此时依赖单一元启发式方法可能不可靠。我们提出WASHH，一种用于连续黑箱优化的鲸群引导自适应选择超启发式算法。WASHH以鲸鱼优化算法作为主要开发主干，但将PSO式记忆、GWO式领导者平均、DE式变异、局部坐标搜索和锚点感知精化作为可选搜索行为。在线奖励控制器根据观测到的改进分配评估资源，而锚点精化则利用低成本的参考配置（如箱子中心或默认模型设置），且不绕过黑箱评估。在十项30维基准函数上，经过10次独立运行和12,000次评估，WASHH取得了最佳平均排名1.10，并在所有十项函数上达到最优或并列最优。它在八项函数上严格优于WOA，并在Rastrigin和Griewank函数上与WOA在数值最优处持平。我们进一步研究了乳腺癌诊断中SVC超参数配置，在300次评估预算下，WASHH在所有对比优化器中获得了最低的平均验证对数损失，表明锚点感知选择超启发式算法是学习辅助算法系统的一个实用轻量级方向。