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To improve the accuracy of common intelligent algorithms when identifying the parameters of geometric error in medical robots, this paper proposes an improved beetle antennae search algorithm (RWSAVSBAS). We first establish a model for the kinematic error in medical robots, and then add the random wandering behavior of the wolf colony algorithm to...
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Thelazia callipaeda is a zoonotic parasite causing ocular disease in domestic dogs, cats, several wild carnivores, hares, and humans. This nematode is widely distributed in Europe, where it is transmitted by the drosophilid fly Phortica variegata. Since the first report of infection in grey wolves (Canis lupus) from southern Italy, other cases of t...
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... Wu et al. [38] used BAS algorithm to optimize the weights between the hidden layer and the output layer of the new neural network classifier (NNC), which improved the computation speed and prediction accuracy. However, BAS algorithm relies heavily on the parameter settings during optimization, and the convergence and solution accuracy of the algorithm are closely related to the parameters used [39,40]. Therefore, Qian et al. [41] proposed an improved BAS algorithm (MHBAS) with adaptive adjustment step size by integrating the preliminary optimization and mutation-crossover mechanisms of multi-objective differential evolution (MDE) algorithm. ...
As the banking industry gradually steps into the digital era of Bank 4.0, business competition is becoming increasingly fierce, and banks are also facing the problem of massive customer churn. To better maintain their customer resources, it is crucial for banks to accurately predict customers with a tendency to churn. Aiming at the typical binary classification problem like customer churn, this paper establishes an early-warning model for credit card customer churn. That is a dual search algorithm named GSAIBAS by incorporating Golden Sine Algorithm (GSA) and an Improved Beetle Antennae Search (IBAS) is proposed to optimize the parameters of the CatBoost algorithm, which forms the GSAIBAS-CatBoost model. Especially, considering that the BAS algorithm has simple parameters and is easy to fall into local optimum, the Sigmoid nonlinear convergence factor and the lane flight equation are introduced to adjust the fixed step size of beetle. Then this improved BAS algorithm with variable step size is fused with the GSA to form a GSAIBAS algorithm which can achieve dual optimization. Moreover, an empirical analysis is made according to the data set of credit card customers from Analyttica official platform. The empirical results show that the values of Area Under Curve (AUC) and recall of the proposed model in this paper reach 96.15% and 95.56%, respectively, which are significantly better than the other 9 common machine learning models. Compared with several existing optimization algorithms, GSAIBAS algorithm has higher precision in the parameter optimization for CatBoost. Combined with two other customer churn data sets on Kaggle data platform, it is further verified that the model proposed in this paper is also valid and feasible.
... More mainstream identification methods have been proposed for linear systems and nonlinear systems, such as the recursive least squares (RLS) methods [18,19], the stochastic gradient descent (SGD) methods [20,21], the artificial neural network (ANN) techniques [22,23], and the Kalman filter. For linear time invariant systems, the RLS method is widely used and easy implemented [24,25] but susceptible to noise [26]. The SGD method involves a small amount of calculation with a low accuracy [27]. ...
While the size of the four-bar linkage is the basis of kinematic performance analysis in a beam pumping unit, there is still a lack of effective and direct measurement of it. Since the motor input power and the polished rod position are commonly used production data, a size identification algorithm of the four-bar linkage based on the motor input power and the polished rod position is proposed in this paper. Firstly, the kinematic model of a beam pumping unit, the speed model of a motor, the initial value, and the state space model are established. Secondly, a Cubature Kalman filter with nonlinear constraints is designed and the size identification algorithm is worked out. Lastly, the model and the size identification algorithm are validated based on the simulated and measured data, and the characteristics of identification with different measurement covariance are studied. The results demonstrate that both the model and the algorithm are feasible. The maximum relative error between the identified and the real size increases from 0.23% to 9.37% when the covariance increases from 10−6 to 10−1. With the measured covariance of the polished rod velocity, the maximum relative error is 7.09%. A comparison of several current identification methods demonstrates that the proposed algorithm is more accurate.
... The employed BAS is an intelligent optimization algorithm inspired by the principle of beetle foraging [45][46][47]. The principle is thus: the beetle receives the smell of food through whiskers. ...
For cemented paste backfill (CPB), uniaxial compressive strength (UCS) is the key to ensuring the safety of stope construction, and its cost is an important part of the mining cost. However, there are a lack of design methods based on UCS and cost optimization. To address such issues, this study proposes a biobjective optimization approach by applying a novel evolved random forest (RF) model. First, the evolved RF model, based on the beetle search algorithm (BAS), was constructed to predict the UCS of CPB. The consistency between the predicted value and the actual value is high, which proves that the hybrid machine learning model has a good effect on the prediction of the UCS of CPB. Then, considering the linear relationship between the costs and the components of CPB, a mathematical model of the cost is constructed. Finally, based on the weighted sum method, the biobjective optimization process of the UCS and cost of CPB is conducted; the Pareto front optimal solutions of UCS and the cost of CPB can be obtained by the sort of solution set. When the UCS or the cost of CPB is constant, the Pareto front optimal solutions can always have a lower cost or a higher UCS compared with the actual dataset, which proves that the biobjective optimization approach has a good effect.
... In our previous work, we proposed a random wandering simulated annealing variable step beetle antennae search algorithm (RWSAVSBAS) algorithm. The results of verification showed that it has a higher accuracy than commonly used affine algorithms [12][13][14]. Because this algorithm was based on the BAS algorithm, which has the advantage of simple coding, we use RWSAVSBAS in this paper to identify the frictional parameters of industrial robots. ...
... This method is superior to the commonly used and improved BAS algorithm and the shuffled frog-leaping algorithm. We thus used RWSAVSBAS to identify the frictional parameters of the industrial robot [12]. ...
... The flow chart is as follows: Then, the flow of RWSAVSBAS algorithm can be obtained from Figure 5 as follows [12]: ...
Commonly used intelligent algorithms that are used to identify the parameters of friction of industrial robots have poor accuracy or involve complex coding, which is not conducive to their use in engineering. This paper uses the random wandering simulated annealing-based variable-step beetle antennae search (RWSAVSBAS) algorithm to identify the parameters of friction of industrial robots. The moment of friction of the third joint of the robot is experimentally obtained and used to establish a Stribeck friction model. Following this, the RWSAVSBAS algorithm is used to identify the frictional parameters of the industrial robot. These parameters can be used to accurately predict the friction-induced torque of the robot.
In recent years, swarm intelligence optimization algorithms have been proven to have significant effects in solving combinatorial optimization problems. Introducing the concept of evolutionary computing, which is currently a hot research topic, into swarm intelligence optimization algorithms to form novel swarm intelligence optimization algorithms has proposed a new research direction for better solving combinatorial optimization problems. The longhorn beetle whisker search algorithm is an emerging heuristic algorithm, which originates from the simulation of longhorn beetle foraging behavior. This algorithm simulates the touch strategy required by longhorn beetles during foraging, and achieves efficient search in complex problem spaces through bioheuristic methods. This article reviews the research progress on the search algorithm for longhorn beetles from 2017 to present. Firstly, the basic principle and model structure of the beetle whisker search algorithm were introduced, and its differences and connections with other heuristic algorithms were analyzed. Secondly, this paper summarizes the research achievements of scholars in recent years on the improvement of longhorn whisker search algorithms. Then, the application of the beetle whisker search algorithm in various fields was explored, including function optimization, engineering design, and path planning. Finally, this paper summarizes the research achievements of scholars in recent years on the improvement of the longhorn whisker search algorithm, and proposes future research directions, including algorithm deep learning fusion, processing of multimodal problems, etc. Through this review, readers will have a comprehensive understanding of the research status and prospects of the longhorn whisker search algorithm, providing useful guidance for its application in practical problems.
To improve performance in terms of overshoot and motor response speed when a permanent-magnet synchronous motor (PMSM) with a proportional–integral (PI) controller is subjected to external disturbances, this paper proposes a speed control strategy based on an enhanced Beetle Antennae Search algorithm, which allows for adjustable parameters of the PI controller within a certain range. Firstly, to enhance the global and local search capabilities of each individual beetle, the step size was improved by linearly decreasing it. Secondly, a simulation model of a PMSM closed-loop control system was built to verify the effectiveness of the improved Beetle Antennae Search (BAS) algorithm. Finally, a linear feedback shift register model that generates four random numbers was developed on a field-programmable gate array (FPGA). The improved BAS algorithm for the PMSM control system was implemented on an FPGA using the Verilog hardware description language, and the feasibility of the system was verified through hardware simulation. Additionally, the hardware resource consumption on different FPGA platforms was analyzed. The simulation results demonstrate that the proposed new speed control strategy can reduce the overshoot and improve the motor response speed.
To improve the prediction accuracy of the International Roughness Index (IRI) of Jointed Plain Concrete Pavements (JPCP) and Continuously Reinforced Concrete Pavements (CRCP), a machine learning approach is developed in this study for the modelling, combining an improved Beetle Antennae Search (MBAS) algorithm and Random Forest (RF) model. The 10-fold cross-validation was applied to verify the reliability and accuracy of the model proposed in this study. The importance scores of all input variables on the IRI of JPCP and CRCP were analysed as well. The results by the comparative analysis showed the prediction accuracy of the IRI of the newly developed MBAS and RF hybrid machine learning model (RF-MBAS) in this study is higher, indicated by the RMSE and R values of 0.2732 and 0.9476 for the JPCP as well as the RMSE and R values of 0.1863 and 0.9182 for the CRCP. The accuracy of this obtained result far exceeds that of the IRI prediction model used in the traditional Mechanistic-Empirical Pavement Design Guide (MEPDG), indicating the great potential of this developed model. The importance analysis showed that the IRI of JPCP and CRCP was proportional to the corresponding input variables in this study, including the total joint faulting cumulated per KM (TFAULT), percent subgrade material passing the 0.075-mm Sieve (P200) and pavement surface area with flexible and rigid patching (all Severities) (PATCH) which scored higher.
