The fitness-dependent optimizer (FDO), a newly proposed swarm intelligent algorithm, is focused on the reproductive mechanism of bee swarming and collective decision-making. To optimize the performance, FDO calculates velocity (pace) differently. FDO calculates weight using the fitness function values to update the search agent position during the exploration and exploitation phases. However, the FDO encounters slow convergence and unbalanced exploitation and exploration. Hence, this study proposes a novel hybrid of the sine cosine algorithm and fitness-dependent optimizer (SC-FDO) for updating the velocity (pace) using the sine cosine scheme. This proposed algorithm, SC-FDO, has been tested over 19 classical and 10 IEEE Congress of Evolutionary Computation (CEC-C06 2019) benchmark test functions. The findings revealed that SC-FDO achieved better performances in most cases than the original FDO and well-known optimization algorithms. The proposed SC-FDO improved the original FDO by achieving a better exploit-explore tradeoff with a faster convergence speed. The SC-FDO was applied to the missing data estimation cases and refined the missingness as optimization problems. This is the first time, to our knowledge, that nature-inspired algorithms have been considered for handling time series datasets with low and high missingness problems (10%-90%). The impacts of missing data on the predictive ability of the proposed SC-FDO were evaluated using a large weather dataset from 1985 until 2020. The results revealed that the imputation sensitivity depends on the percentages of missingness and the imputation models. The findings demonstrated that the SC-FDO based multilayer perceptron (MLP) trainer outperformed the other three optimizer trainers with the highest average accuracy of 90% when treating the high-low missingness in the dataset.

中文翻译：

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用于全局优化的混合正弦余弦和适应度相关优化器


适应度相关优化器（FDO）是一种新提出的群体智能算法，专注于蜜蜂集群的繁殖机制和集体决策。为了优化性能，FDO 以不同的方式计算速度（步速）。 FDO 使用适应度函数值计算权重，以在探索和利用阶段更新搜索代理位置。然而，FDO 遇到收敛速度慢以及开发和探索不平衡的问题。因此，本研究提出了一种新颖的正弦余弦算法和适应度相关优化器（SC-FDO）的混合体，用于使用正弦余弦方案更新速度（步速）。这种提出的算法 SC-FDO 已经过 19 个经典和 10 个 IEEE 进化计算大会 (CEC-C06 2019) 基准测试函数的测试。研究结果表明，SC-FDO 在大多数情况下比原始 FDO 和众所周知的优化算法取得了更好的性能。所提出的 SC-FDO 通过以更快的收敛速度实现更好的利用-探索权衡，从而改进了原始 FDO。 SC-FDO应用于缺失数据估计案例，并将缺失细化为优化问题。据我们所知，这是第一次考虑将自然启发的算法用于处理具有低和高缺失问题（10%-90%）的时间序列数据集。使用 1985 年至 2020 年的大型天气数据集评估了缺失数据对拟议 SC-FDO 预测能力的影响。结果表明，插补敏感性取决于缺失百分比和插补模型。 研究结果表明，在处理数据集中的高低缺失时，基于 SC-FDO 的多层感知器 (MLP) 训练器的表现优于其他三个优化器训练器，最高平均准确度为 90%。