This paper proposed an improved optimization algorithm for the engineering optimization problems. This improved optimization algorithm, namely gravitational search strategy assisted rider optimization algorithm (GSSROA), combines the gravitational search strategy (GSS) with the rider optimization algorithm (ROA). In GSSROA, the search step can be adaptively adjusted in each iteration. The performance of the GSSROA has been verified with three benchmark engineering design problem. The results indicate that the proposed GSSROA has the feasibility and good robustness. Then, GSSROA is applied to optimize the weight of front axle of an automobile under the non-linear constraints. The results show that the GSSROA can achieve the optimal structure with a weight that is reduced by 16.82% compared with the initial design.

Large cable shovel is a complex mechatronic system used for primary production in the open pit mine. For such structure-control highly coupled system, the conventional sequential design strategy (structure design followed by the control optimization in sequence) cannot manage this interaction adequately and explicitly. In addition, the large cable shovel consists of large number of sub-systems and/or disciplines, which also poses challenges to the global optimal design for large cable shovel. To enhance large cable shovel’s performance, an integrated design optimization strategy combining the structure-control simultaneous design (co-design) and the multidisciplinary design optimization is established in this study to perform the global optimization for the large cable shovel. In this proposed multidisciplinary co-design, the point-to-point trajectory planning method is extended to achieve the simultaneous optimization of the structure and control system. Besides the structure and control, the dynamics/vibration and energy consumption are taken into account in this multidisciplinary co-design. The objectives are to minimize the energy consumption per volume of ore and to minimize the excavating time. By comparing the multidisciplinary co-design and the conventional sequential design, it is found that the multidisciplinary co-design can not only make large cable shovel’s structure more compact with relatively small vibration, but also generate more flexible control speeds by making the best of the power motors.

The prediction technology of remaining useful life has received a lot attention to ensure the reliability and stability of complex mechanical equipment. Due to the large-scale, non-linear, and high-dimensional characteristics of monitoring data, machine learning does not need an exact physical model and prior expert knowledge. It has robust data processing ability, which shows a broad prospect in the field of life prediction of complex mechanical and electrical equipment. Therefore, a remaining useful life prediction algorithm based on Random Forest and Bi-directional Long Short-Term Memory (RF-BiLSTM) is proposed. In the RF-BiLSTM algorithm, RF is utilized to extract health indicators that reflect the life of the equipment. On this basis, a BiLSTM neural network is used to predict the residual life of the device. The effectiveness and advanced performance of RF-BiLSTM are verified in commercial modular aviation propulsion system datasets. The experimental results show that the RMSE of the RF-BiLSTM is 0.3892, which is 47.96%, 84.81%, 38.89%, and 86.53% lower than that of LSTM, SVR, XGBoost, and AdaBoost, respectively. It is verified that RF-BiLSTM can effectively improve the prediction accuracy of the remaining useful life of complex mechanical and electrical equipment, and it has certain application value.

A novel machine learning method, named CatBoost-DF (CatBoost deep forest), is proposed to solve this existing problem of low accuracy and lack of practicality in thermal sensation prediction. In the CatBoost-DF, a cascading strategy is introduced to strengthen the association between each layer of CatBoost. To verify the accuracy and robustness of CatBoost-DF, experiments collected physiological and environmental data from hundreds of subjects with the help of sensor devices and questionnaires. Compared with existing state-of-the-art machine learning methods, CatBoost-DF shows significant superiority, with a prediction accuracy of 90%, which is 4%-39% higher than other models. Moreover, the study explored the effects of seasonal and gender factors on thermal sensation. Result shown that different seasons have different thermal sensation for males and females. Finally, CatBoost-DF is applied to predict occupants’ thermal sensation, and the “comfort range” of the important parameters HR, WS, and CTR that affect the thermal sensation is calculated experimentally.

Bucket wheel reclaimer (BWR) is a complex engineering machine widely used in the open pit mine; it is characterized by the low efficiency and high maintenance cost. The boom, namely a typical framework structure, is a core component of BWR, which affects the performance of the BWR directly. For addressing this issue, this paper proposes a self-adaptive genetic algorithm (AGA) to improve the performance of the genetic algorithm (GA). The standard genetic algorithm has been improved to enhance the optimization efficiency, because the optimization problem is believed to be highly non-linear. The AGA has been verified by two framework structures, and the results of AGA are compared with the corresponding results of previous literature. Furthermore, the AGA is applied to obtain the optimal size and shape of the BWR boom by taking the BWR boom as a space framework structure, and improve BWR’s performance by meeting the requirements of the intensity and rigidity. The results show that the improved genetic algorithm has a higher efficiency than the standard GA. The structure optimization of the BWR boom is performed using the AGA. From the optimization, BWR boom’s weight decreases by 23.46% from the initial weight.