This paper presents a novel fuzzy adaptive jellyfish search-optimized stacking system (FAJS-SS) that integrates the jellyfish search (JS) optimizer, the fuzzy adaptive (FA) logic controller, and stacking ensemble machine learning. First, FA logic is incorporated into JS optimizer to construct an efficient metaheuristic algorithm for global optimization. The proposed algorithm is benchmarked against various well-known optimizers using mathematical functions. The FAJS optimizer is then used to optimize the hyperparameters of the stacking system (SS). Cases that involve construction productivity, the compressive strength of a masonry structure, the shear capacity of reinforced deep beams, the axial strength of steel tube-confined concrete, and the resilient modulus of subgrade soils were investigated. Results of analyses reveal that the FAJS-SS predicts more accurately than the other machine learning systems in the literature. Accordingly, the proposed fuzzy adaptive metaheuristic-optimized stacking system is effective for providing engineering informatics in the planning and design phase.

本文提出了一种新颖的模糊自适应水母搜索优化堆叠系统 (FAJS-SS)，它集成了水母搜索 (JS) 优化器、模糊自适应 (FA) 逻辑控制器和堆叠集成机​​器学习。首先，将FA逻辑纳入JS优化器，构建高效的全局优化元启发式算法。所提出的算法使用数学函数针对各种著名的优化器进行了基准测试。然后使用 FAJS 优化器来优化堆叠系统 (SS) 的超参数。对涉及施工生产率、砌体结构的抗压强度、加筋深梁的抗剪能力、钢管约束混凝土的轴向强度和路基土的弹性模量的案例进行了研究。分析结果表明，FAJS-SS 的预测比文献中的其他机器学习系统更准确。因此，所提出的模糊自适应元启发式优化堆叠系统对于在规划和设计阶段提供工程信息学是有效的。