Beetle Antennae Search Algorithm Research Articles

Using Quadratic Interpolated Beetle Antennae Search to Enhance Robot Arm Calibration Accuracy

During the past decades, industrial robot arms have been widely adopted in various fields, like aerospace and medical care. However, an uncalibrated industrial robot arm suffers from its high absolute positioning error, which tremendously restricts its application in high-precision intelligent manufacture. Related robot arm calibration methods present a potential solution to this issue, while recent algorithms frequently encounter some issues, such as long-tail convergence and low calibration accuracy. To address this thorny issue, this study proposes a novel robot arm calibration method incorporating the extended Kalman filter with Quadratic Interpolated Beetle Antennae Search algorithm. It adopts three-fold ideas: a) developing a new Quadratic Interpolated Beetle Antennae Search algorithm that greatly addresses the issue of local optimum and low convergence rate in a Beetle Antennae Search algorithm; b) adopting an extended Kalman filter algorithm to decrease the influence of measurement noises and c) proposing a robot arm calibration method incorporating an extended Kalman filter with Quadratic Interpolated Beetle Antennae Search algorithm to searching the optimal kinematic parameters of a robot arm. Extensive experiments on an ABB IRB120 industrial robot arm demonstrate that the calibration accuracy of the proposed method is 5.17% higher than that of the state-of-the-art method. Therefore, it is appropriate for practical application occasions with high-precision requirements.

Fault Diagnosis of Oil-Immersed Transformer based on TSNE and IBASA- SVM

Background: With the rapid development of power system, oil-immersed transformers are widely used in the substation and distribution system. The faults of oil-immersed transformers are large threat to the power system. Therefore, it is significant that the faults of oil-immersed transformers can be diagnosed accurately. Objective: To accurately diagnose the faults of oil-immersed transformers through machine learning methods and swarm intelligent algorithms. Methods: To accurately diagnose the faults of oil-immersed transformers, a fault diagnosis method based on T-distributed stochastic neighbor embedding and support vector machine is proposed. The improved beetle antennae search algorithm is used to optimize the parameters of support vector machine. Firstly, the non-coding ratio method is used to obtain nine-dimensional characteristic indices. Secondly, the original nine-dimensional data are reduced to three-dimensional by T-distributed stochastic neighbor embedding. Lastly, the data after dimensionality reduction are used as the input of the support vector machine optimized by improved beetle antennae search algorithm and the fault types of transformers can be diagnosed. Results: The accuracy rate is 94.53% and the operation time is about 1.88s. The results indicate that the method proposed by this paper is reasonable. Conclusion: The experimental results show that the method proposed by this paper has a high accuracy rate and low operation time. Mixed faults that are difficult to diagnose also can be diagnosed by this paper's method. In the era of big data, there is a lot of data of transformer, so the method proposed in this paper has certain engineering significance.

Time‐cost efficient memory configuration for serverless workflow applications

SummaryRecently, workflow applications are increasingly migrated to Function‐as‐a‐Service platforms which are easy to manage, highly‐scalable, and pay‐as‐you‐go. Meanwhile, users face challenges in migration of serverless applications because of the lack of efficient algorithm for workflow memory configuration to optimize the performance. To this end, this article proposes a heuristic urgency‐based algorithm UWC and a meta‐heuristic hybrid algorithm BPSO to tackle the time‐cost tradeoff. UWC sorts functions and allocates each function an appropriate memory size by greedy strategy. BPSO hybridizes particle swarm optimization as well as beetle antennae search algorithm to guide particles to search directionally and utilizes nonlinear inertia weight to avoid local premature convergence. Extensive experiments with classical serverless application demonstrate that UWC and BPSO are very competitive in comparison with existing algorithms as they can find the optimal workflow memory configuration.

Combined EIS and BAS-BP neural network analysis of electrochemical corrosion on pipeline steel in silty soil in a Salt–Temperature coupling environment

Soil corrosion negatively affects long-distance pipeline transportation. Studies on this phenomenon have revealed the potential safety risks associated with metal durability. In previous studies on the soil corrosion of pipeline steel, corrosion behaviour, which is influenced by the salt–temperature coupling environment, was seldom considered. In this study, a laboratory-simulated corrosion environment for electrochemical impedance spectroscopy (EIS) measurements was designed to analyse the corrosion behaviour of X80 steel immersed in silty soil mixed with different concentrations of NaCl and Na2SO4 over a temperature range of 20 to −20 °C. Nyquist plots displayed three capacitive reactance arcs in various soil environments in the range of 100 kHz to 0.1 Hz. Furthermore, an equivalent circuit model was utilised to fit the Nyquist and Bode plots. The effects of the Cl− concentration, SO42− concentration, and temperature on the EIS characteristics were analysed. The change in the temperature considerably affected the degree of corrosion. Cl− reacted with the metal and catalysed the corrosion process, whereas SO42− enhanced the chemical inertness of the metal surface. The mechanism of the metal corrosion reaction was revealed by performing scanning electron microscopy and X-ray photoelectron spectroscopy. Based on the experimental results and beetle antennae search (BAS) algorithm, an optimised back propagation (BP) neural network model was established to predict the Nyquist plot of the soil corrosion process. The predicted Nyquist plots exhibited the same electrochemical characteristics as those shown by the experimental curves. A comparison of the evaluation parameters revealed that the equivalent circuit of the corrosion system can be applied to the prediction results obtained from the BAS-BP neural network model. Moreover, it demonstrated the practicality of the neural network prediction method in the field of corrosion science.

An improved beetle antennae search path planning algorithm for vehicles.

With the development of society, the application of mobile robots in industry and life is increasingly extensive, and the local path planning of mobile robots in unknown environments is a problem that needs to be solved. Aiming at the problem that the traditional beetle antennae search (BAS) algorithm can easily fall into local optimum and the optimization accuracy is low, we propose an improved beetle antennae search. It introduces a map safety threshold, the addition of virtual target points, and the smoothing of the path. Map safety threshold means extra space with obstacles at all times, improving path reliability by avoiding collisions. Adding virtual target points reduces situations where the vehicle gets stuck in local optima. The B-spline smoothing path reduces the original path’s straight turns to improve the path’s robustness. The effectiveness and superiority of the algorithm are verified by comparing and testing the existing path planning algorithms through simulation in different environments.

An improved beetle antennae search algorithm with Lévy flight and its application in micro-laser assisted turning

The beetle antennae search (BAS) algorithm is a single-solution metaheuristic optimizer that imitates the foraging behavior of beetles. Due to its easy implementation and fast convergence, it has been applied in various engineering fields. To enhance the local search ability and preclude the blindness of position updates performed in BAS, an improved beetle antennae search algorithm with Lévy flight (IBAS) is proposed in this paper. Additionally, to further enhance the algorithm performance, the features of inertia, normalization, and elitist selection are adopted into this proposed hybrid algorithm. In a comparison with other algorithms based on 15 well-known benchmark functions and 4 classic engineering problems, IBAS maintains good performance under the premise of a trade-off between efficacy and efficiency. The detailed results suggest that the improvements adapted in IBAS alleviate the shortcomings of BAS. In addition, a real-world engineering problem, laser energy prediction in micro-laser assisted turning, is used to further validate the potential of the proposed algorithm for industrial applications. The results indicate the feasibility of the new solution based on the IBAS algorithm. Two further applications of IBAS in the reconstruction of the temperature field and online laser energy detection are also discussed.

Enhanced beetle antennae search algorithm for complex and unbiased optimization

Beetle Antennae Search algorithm is a kind of intelligent optimization algorithms, which has the advantages of few parameters and simplicity. However, due to its inherent limitations, BAS has poor performance in complex optimization problems. The existing improvements of BAS are mainly based on the utilization of multiple beetles or combining BAS with other algorithms. The present study improves BAS from its origin and keeps the simplicity of the algorithm. First, an adaptive step size reduction method is used to increase the usability of the algorithm, which is based on an accurate factor and curvilinearly reduces the step size; second, the calculated information of fitness functions during each iteration are fully utilized with a contemporary optimal update strategy to promote the optimization processes; third, the theoretical analysis of the multi-directional sensing method is conducted and utilized to further improve the efficiency of the algorithm. Finally, the proposed Enhanced Beetle Antennae Search algorithm is compared with many other algorithms based on unbiased test functions. The test functions are unbiased when their solution space does not contain simple patterns, which may be used to facilitate the searching processes. As a result, EBAS outperformed BAS with at least 1 orders of magnitude difference. The performance of EBAS was even better than several state-of-the-art swarm-based algorithms, such as Slime Mold Algorithm and Grey Wolf Optimization, with similar running times. In addition, a WSN coverage optimization problem is tested to demonstrate the applicability of EBAS on real-world optimizations.

Prediction of fatigue crack propagation in metals based on IBAS-PF

Owing to particle leanness, the standard Particle Filter (PF) algorithm is prone to the problem of reduced prediction accuracy when predicting fatigue crack propagation. An improved particle filter algorithm based on the optimization algorithm of beetle antenna search (IBAS-PF) for fatigue crack propagation in metals is proposed in this paper. The discrete Paris formula was used to establish the state equation of fatigue crack propagation, in which the uncertainty of material and crack propagation process were considered. Meanwhile, the characteristics of Lamb wave signals under different crack lengths were extracted to establish the observation equation. The sampling process of the PF algorithm was optimized based on the beetle antennae search algorithm to improve the particle diversity and the prediction accuracy. Compared with the standard PF algorithm, the improved BASO-PF algorithm has higher accuracy for metal fatigue crack propagation, as well as better state estimation ability.

A Comparative Study of AI-Based International Roughness Index (IRI) Prediction Models for Jointed Plain Concrete Pavement (JPCP).

The international roughness index (IRI) can be employed to evaluate the smoothness of pavement. The previously proposed mechanical-empirical pavement design guide (MEPDG), which is used to model the IRI of joint plain concrete pavement (JPCP), has been modified in this study considering its disadvantage of low prediction accuracy. To improve the reliability of the prediction effect of the IRI for JPCP, this study compares the prediction accuracy of the IRI of JPCP by using the machine-learning methods of support vector machine (SVM), decision tree (DT), and random forest (RF), optimized by the hyperparameter of the beetle antennae search (BAS) algorithm. The results from the machine-learning process show that the BAS algorithm can effectively improve the effectiveness of hyperparameter tuning, and then improve the speed and accuracy of optimization. The RF model proved to be the one with the highest prediction accuracy among the above three models. Finally, this study analyzes the importance score of input variables to the IRI, and the results show that the IRI was proportional to all the input variables in this study, and the importance score of initial smoothness (IRII) and total joint faulting cumulated per km (TFAULT) were the highest for the IRI of JPCP.

An Improved Beetle Antennae Search Algorithm Based on Inertia Weight and Attenuation Factor

The beetle antennae search algorithm is an effective bio-inspired algorithm. However, the algorithm is easy to fall into local optimal solution when dealing with high-dimensional and multimodal problems. An improved beetle antennae search algorithm based on inertia weight and attenuation factor was proposed in order to solve the problems. The inertia weights of normal distribution, versoria distribution, and random distribution are introduced into the weight to improve the search strategy, which was introduced to control the proportion of global search and local search so that the algorithm will escape the local optimum. Meanwhile, the randomness is introduced in the process of beetle’s step-size update, which can better help beetle to escape from the local optimum. Experiments on some benchmarks show that the algorithm has obvious performance improvement in dealing with high-dimensional and multimodal problems.

Fractional Order PID Control Based on Ball Screw Energy Regenerative Active Suspension

A ball screw type energy regenerative active suspension under fractional order PID control is proposed and studied in order to improve the vibration damping performance of the suspension. A mathematical model of the energy regenerative actuator is established, the energy recovery power at different frequencies is measured through experiments, and then the electromagnetic torque constant, representing the proportional relationship between the output torque and current of the motor, is calculated according to the experimental results. A mathematical model of the control circuit is established and the feasibility and the superiority of the fractional order PID control are verified by simulation and experiments. To achieve a better damping effect, the fractional order PID controller of the whole vehicle suspension system is parameterized using the Beetle Antenna Search (BAS) algorithm. The results showed that the mean energy recovery power of the actuator was about 3.5091 W at a vibration frequency of 11/6 Hz, and the electromagnetic torque constant of the motor was about 0.2885. The actuator control circuit was feasible, and the root mean square value of current deviation under fractional order PID control was 1.1158 mA, which was optimized by 9.40%, compared to the PID control. The BAS algorithm effectively realized the parameter tuning of the controller, and both the tuned PID and fractional order PID controllers, achieved optimization of suspension damping performance. The optimal value of the damping performance objective function under fractional order PID control was 0.3270, which was optimized by 62.93%, compared to the PID control. In addition, all suspension performance indices under fractional order PID control were optimized to a certain extent, compared with the PID control.

Arc Grounding Fault Monitoring and Fire Prediction Method Based on EEMD and Reconstruction

To solve the problem of the single-phase ground fault and occurrence of electrical fires due to the residual current in substation AC power systems, a residual current intelligent sensing technology is proposed based on ensemble empirical modal decomposition (EEMD), sample entropy (SE) reconstruction, and fire warning technology using a beetle antennae search algorithm. First, through the residual current monitoring device to collect residual current information, EEMD and SE reconstruction for arc-earth fault diagnosis and an analysis of the differences in the current characteristics of each line after reconstruction are used to determine the fault line. Second, residual current, temperature, and operating voltage as input parameters and fire probability are the output parameters. The input–output relationship is established by a back-propagation neural network (BPNN) and optimized by the beetle antennae search (BAS) algorithm to speed up the convergence and improve the prediction accuracy to establish a substation fire warning scheme. Through simulation experiments, this paper proposes the residual current as a monitoring object method can effectively diagnose ground faults and accurately predict the probability of fire occurrence to ensure the safe and stable operation of substations.

Optimal Energy-Delay Scheduling using Improved Beetle Antennae Search (BAS) for Energy-Harvesting WSNs

Optimal energy delay scheduling for capacity allocation problems in interference channel energy harvesting wireless sensor networks (EH-WSN) address of static data streams as well as power surveying. The goal is to diminish the general regression of network. However, the optimization problem is not curved, which makes it difficult to obtain optimal solution. The major goal of this document is to directly address that original non-convex formulation with enhanced Beetle Antennae Search (BAS) algorithm competently gets optimal solution. The simulations in delay, delivery ratio, drop, energy, network life, overload, and throughput are performed; the predictable result shows the improved BAS algorithm performs better convex approximation system. This plan is helpful for other complex optimization problems in WSN.

Intelligent Design of Construction Materials: A Comparative Study of AI Approaches for Predicting the Strength of Concrete with Blast Furnace Slag

Concrete production by replacing cement with green materials has been conducted in recent years considering the strategy of sustainable development. This study researched the topic of compressive strength regarding one type of green concrete containing blast furnace slag. Although some researchers have proposed using machine learning models to predict the compressive strength of concrete, few researchers have compared the prediction accuracy of different machine learning models on the compressive strength of concrete. Firstly, the hyperparameters of BP neural network (BPNN), support vector machine (SVM), decision tree (DT), random forest (RF), K-nearest neighbor algorithm (KNN), logistic regression (LR), and multiple linear regression (MLR) are tuned by the beetle antennae search algorithm (BAS). Then, the prediction effects of the above seven machine learning models on the compressive strength of concrete are evaluated and compared. The comparison results show that KNN has higher R values and lower RSME values both in the training set and test set; that is, KNN is the best model for predicting the compressive strength of concrete among the seven machine learning models mentioned above.

Software Defects Prediction Based on Hybrid Beetle Antennae Search Algorithm and Artificial Bee Colony Algorithm with Comparison

Software defects are problems in software that destroy normal operation ability and reflect the quality of the software. Software fault can be predicted by the software reliability model. In this paper, the hybrid algorithm is applied to parameter estimation in software defect prediction. As a biological heuristic algorithm, BAS (Beetle Antennae Search Algorithm) has fast convergence speed and is easy to implement. ABC (Artificial Bee Colony Algorithm) is better in optimization and has strong robustness. In this paper, the BAS-ABC hybrid algorithm is proposed by mixing the two algorithms and the goal of the proposed method is to improve the convergence and stability of the algorithm. Five datasets were used to carry out the experiments, and the data results showed that the hybrid algorithm was more accurate than the single algorithm, with stronger convergence and stability, so it was more suitable for parameter estimation of the software reliability model. Meanwhile, this paper implemented the comparison between hybrid BAS + ABC and hybrid PSO + SSA, and the result shows that BAS + ABC has better performance both in convergence and stability. The comparison result shows the strong ability in estimation and prediction of software defects hybrid BAS and ABC.

Adopting Nonlinear Activated Beetle Antennae Search Algorithm for Fraud Detection of Public Trading Companies: A Computational Finance Approach

With the emergence of various online trading technologies, fraudulent cases begin to occur frequently. The problem of fraud in public trading companies is a hot topic in financial field. This paper proposes a fraud detection model for public trading companies using datasets collected from SEC’s Accounting and Auditing Enforcement Releases (AAERs). At the same time, this computational finance model is solved with a nonlinear activated Beetle Antennae Search (NABAS) algorithm, which is a variant of the meta-heuristic optimization algorithm named Beetle Antennae Search (BAS) algorithm. Firstly, the fraud detection model is transformed into an optimization problem of minimizing loss function and using the NABAS algorithm to find the optimal solution. NABAS has only one search particle and explores the space under a given gradient estimation until it is less than an “Activated Threshold” and the algorithm is efficient in computation. Then, the random under-sampling with AdaBoost (RUSBoost) algorithm is employed to comprehensively evaluate the performance of NABAS. In addition, to reflect the superiority of NABAS in the fraud detection problem, it is compared with some popular methods in recent years, such as the logistic regression model and Support Vector Machine with Financial Kernel (SVM-FK) algorithm. Finally, the experimental results show that the NABAS algorithm has higher accuracy and efficiency than other methods in the fraud detection of public datasets.

Optimised trajectory tracking control for quadrotors based on an improved beetle antennae search algorithm

This paper focuses on the trajectory tracking of quadrotors under bounded external disturbances. An optimised robust controller is proposed to drive the position and attitude of a quadrotor converge to their references quickly. At first, nonsingular fast terminal sliding mode control is developed, which can guarantee not only the stability but also finite-time convergence of the closed-loop system. As the parameters of the designed controllers play a vital role for control performance, an improved beetle antennae search algorithm is proposed to optimise them. By employing the historical information of the beetle's antennae and dynamically updating the step size as well as the range of its searching, the optimising is accelerated considerably to ensure the efficiency of the quadrotor control. The superiority of the proposed control scheme is demonstrated by simulation experiments, from which one can see that both the error and the overshooting of the trajectory tracking are reduced effectively.

A compensation method for gyroscope random drift based on unscented Kalman filter and support vector regression optimized by adaptive beetle antennae search algorithm

A compensation method for gyroscope random drift based on unscented Kalman filter and support vector regression optimized by adaptive beetle antennae search algorithm

Intelligent Design of Building Materials: Development of an AI-Based Method for Cement-Slag Concrete Design.

Cement-slag concrete has become one of the most widely used building materials considering its economical advantage and satisfying uniaxial compressive strength (UCS). In this study, an AI-based method for cement-slag concrete design was developed based on the balance of economic and mechanical properties. Firstly, the hyperparameters of random forest (RF), decision tree (DT), and support vector machine (SVM) were tuned by the beetle antennae search algorithm (BAS). The results of the model evaluation showed the RF with the best prediction effect on the UCS of concrete was selected as the objective function of UCS optimization. Afterward, the objective function of concrete cost optimization was established according to the linear relationship between concrete cost and each mixture. The obtained results showed that the weighted method can be used to construct the multi-objective optimization function of UCS and cost for cement-slag concrete, which is solved by the multi-objective beetle antennae search (MOBAS) algorithm. An optimal concrete mixture ratio can be obtained by Technique for Order Preference by Similarity to Ideal Solution. Considering the current global environment trend of “Net Carbon Zero”, the multi-objective optimization design should be proposed based on the objectives of economy-carbon emission-mechanical properties for future studies.

An Enhanced Beetle Antennae Search Algorithm Based Comprehensive Water Quality Index for Urban River Water Quality Assessment

Urban river not only has the important function in urban hydrological environment, but also is an area for entertainment. Water quality assessment is the core technique in water resource management. As the typical urban river, water samples were collected at 5 sampling points in Xi’an moat from January 2018 to December 2020, and 10 physicochemical parameters were analyzed. In this paper, a comprehensive water quality index (WQI) is designed based on the criterion of water quality classes and entropy weight method firstly. Secondly, the crucial water quality parameters is determined by using mutual information, coefficient of variation and the water quality difference. Finally, an enhanced beetle antennae search algorithm is proposed to optimize the weight values of the crucial parameters in the range 0 to 1, which represent the ratio of the crucial parameter in the minimum WQI (WQImin) model. The WQImin models with different number of crucial water quality parameters are implemented for water quality assessment. The effectiveness and superiority of the proposed enhanced beetle antennae search algorithm are validated in comparison with other evolutionary algorithms. The results show that the proposed WQImin model can assess the water quality accurately.