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A high‐speed railway system is one of the sustainable alternatives to other modes of transportation and may connect the most congested urban cities with minimum carbon emissions. However, the vibration intensity increases as the train's operating speed increases, resulting in deteriorated ride comfort and stability. Hence, this article investigates a 27‐degree‐of‐freedom (DOF) dynamic model of railway vehicles with an improved active suspension system. The decentralized control structure performs the controlling action with five optimized Proportional Integral Derivative (PID) controllers that suppress the vehicle body's vertical, lateral, pitch, roll, and yaw motions. Further, to optimize the PID parameters, three metaheuristic optimization techniques, Genetic algorithm (GA), Grey Wolf Optimization (GWO), and Flower Pollination Algorithm (FPA), are utilized, and their simulated results are compared with the passive system as well as other conventional tuning technique. Moreover, the performance of the proposed control strategy is evaluated in the frequency domain under random track irregularities, and the results are characterized in terms of power spectral densities (PSDs). The simulated results show that among the proposed metaheuristic algorithms, FPA outperforms with a significant reduction in vehicle vibration compared to other tuning methods. The percentage reduction of the vertical, lateral, pitch, rolls, and yaw accelerations is 71.4%, 35.1%, 52.8%, 48.1%, and 38.2%, respectively, ensuring enhanced vehicle ride comfort. [ABSTRACT FROM AUTHOR]

Feature selection (FS) in data mining is one of the most challenging and most important activities in pattern recognition. In this article, a new hybrid model of whale optimization algorithm (WOA) and flower pollination algorithm (FPA) is presented for the problem of FS based on the concept of opposition‐based learning (OBL) which name is HWOAFPA. The procedure is that the WOA is run first and at the same time during the run, the WOA population is changed by the OBL. And, to increase the accuracy and speed of convergence, it is used as the initial population of FPA. To evaluate the performance of the proposed method, experiments were carried out in two steps. The experiments were performed on 10 datasets from the UCI data repository and Email spam detection datasets. The results obtained from the first step showed that the proposed method was more successful in terms of the average size of selection and classification accuracy than other basic metaheuristic algorithms. In addition, the results from the second step showed that the proposed method which was a run on the Email spam dataset performed much more accurately than other similar algorithms in terms of accuracy of Email spam detection. [ABSTRACT FROM AUTHOR]