Predicting the delay in servicing incoming ships to ports is crucial for maritime transportation. In this study, we use support vector regression (SVR) in order to accurately predict this delay for ships arriving to the terminal No. 1 of Shahid Rajaee's port in Bandar Abbas. To achieve this goal, a combination of Clonal Selection and Grey Wolf Optimization algorithms (named as CLOGWO) is used for two purposes: (i) selecting the most important features among the features that affect prediction of this delay and (ii) optimizing SVR parameters for a more accurate prediction. Performance of the proposed method was compared with Genetic Algorithm (GA), Clonal Selection (CS), Grey Wolf Optimization (GWO), and Particle Swarm Optimization (PSO) algorithms on the following metrics: correlation, rate of feature reduction, root mean square error (RMSE), and normalized RMSE (NRMSE). Evaluations on Shahid Rajaee dataset showed that the mean value of these metrics in 10 independent runs of the proposed method were 0.867, 74.45%, 0.080, and 9.02, respectively. These results and evaluations on standard datasets indicate that the proposed method provides competitive results with other evolutionary algorithms.

中文翻译：

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结合克隆选择和灰狼优化算法，选择预测服务船到达港口延迟的有效特征-案例研究：阿巴斯港的Shahid Rajaee港口

预测进港船舶维修的延迟对海上运输至关重要。在这项研究中，我们使用支持向量回归（SVR）来准确预测到达阿巴斯港Shahid Rajaee港口1号码头的船舶的延误。为了实现此目标，将克隆选择和灰狼优化算法（称为CLOGWO）结合使用有两个目的：（i）在影响此延迟预测的特征中选择最重要的特征，以及（ii）优化SVR参数以获得更准确的预测。在以下指标上，将该方法的性能与遗传算法（GA），克隆选择（CS），灰狼优化（GWO）和粒子群优化（PSO）算法进行了比较：相关性，特征减少率，均方根错误（RMSE），并归一化RMSE（NRMSE）。对Shahid Rajaee数据集的评估表明，在所提出方法的10次独立运行中，这些指标的平均值分别为0.867、74.45％，0.080和9.02。这些结果和对标准数据集的评估表明，该方法与其他进化算法相比具有竞争优势。