Beetle Antennae Search Algorithm Research Articles

A Novel Neural Computing Model Applied to Estimate the Dynamic Modulus (DM) of Asphalt Mixtures by the Improved Beetle Antennae Search

To accurately estimate the dynamic properties of the asphalt mixtures to be used in the Mechanistic-Empirical Pavement Design Guide (MEPDG), a novel neural computing model using the improved beetle antennae search was developed. Asphalt mixtures were designed conventionally by eight types of aggregate gradations and two types of asphalt binders. The dynamic modulus (DM) tests were conducted under 3 temperatures and 3 loading frequencies to construct 144 datasets for the machine learning process. A novel neural network model was developed by using an improved beetle antennae search (BAS) algorithm to adjust the hyperparameters more efficiently. The predictive results of the proposed model were determined by R and RMSE and the importance score of the input parameters was assessed as well. The prediction performance showed that the improved BAS algorithm can effectively adjust the hyperparameters of the neural network calculation model, and built the asphalt mixture DM prediction model has higher reliability and effectiveness than the random hyperparameter selection. The mixture model can accurately evaluate and predict the DM of the asphalt mixture to be used in MEPDG. The dynamic shear modulus of the asphalt binder is the most important parameter that affects the DM of the asphalt mixtures because of its high correlation with the adhesive effect in the composition. The phase angle of the binder showed the highest influence on the DM of the asphalt mixtures in the remaining variables. The importance of these influences can provide a reference for the future design of asphalt mixtures.

Optimum Design of Reinforced Concrete Folded Plate Structures to ACI 318-11 Using Soft Computing Algorithm

In this paper, an optimum design algorithm is presented for reinforced concrete folded plate structures. The design provisions are implemented by ACI 318-11 and ACI 318.2-14, which are quite complex to apply. The design variables are divided into three classes. The first class refers to the variables involving the plates, which are the number of supports, thicknesses of the plates, configurations of longitudinal and transverse reinforcement, span length of each plate, and angle of inclination of the inclined plates. The second class consists of the variables involving the auxiliary members’ (beams and diaphragms) depth and breadth and the configurations of longitudinal and shear reinforcement. The third class of variables can be the supporting columns, which involve the dimensions of the column along each axis and the configurations of longitudinal and shear reinforcement. The objective function is considered as the total cost of the folded plate structure, which consists of the cost of concrete, reinforcement, and formwork that is required to construct the building. With such formulation, the design problem becomes a discrete nonlinear programming problem. Its solution is obtained by using three different soft computing techniques, which are artificial bee colony, differential evolution, and enhanced beetle antennae search. The enhancement suggested makes use of the population of beetles instead of one, as is the case in the standard algorithm. With this novel improvement, the beetle antennae search algorithm became very efficient. Two folded plate structures are designed by the proposed optimum design algorithm. It is observed that the differential evolution algorithm performed better than the other two metaheuristics and achieved the cheapest solution.

A New Parameter Identification Method for Industrial Robots with Friction

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.

Adaptive Kalman filter based on multiple fading factors for fast in-motion initial alignment with rotation modulation technique

In this paper, a novel fast fine alignment method is proposed on the basis of micro electro mechanical systems (MEMS) for unmanned aerial vehicle (UAV) under mooring conditions. Firstly, the model of single-axis rotation modulation with extended angular rate measurement is built to improve the speed of convergence, especially the azimuth misalignment angle and the constant error of gyroscope along the rotation axis. Both model mismatch and noise uncertainty of the extended measurement will arise for the difficulty to obtain the exact angular velocity of the carrier without auxiliary sensors. To address this problem, the adaptive Kalman filter based on multiple fading factors is proposed. Besides, the meta-heuristic beetle antennae search (BAS) algorithm is applied for the first time to optimize the fading factors in real time. Then, the effectiveness of the proposed method is verified by means of singular value decomposition and error covariance matrix analysis. Finally, the result of comparative simulations and experiments show that the observability degree and convergence speed of misalignment angles improve significantly compared to those conventional methods and the estimated values can satisfy the requirements of fine alignment. More importantly, the constant error of gyroscope along the rotation axis is estimated accurately and can be compensated in the subsequent navigation process.

Computational and Mathematical Methods in Medicine Prediction of COVID-19 in BRICS Countries: An Integrated Deep Learning Model of CEEMDAN-R-ILSTM-Elman.

Since the outbreak of COVID-19, BRICS countries have experienced different epidemic spread due to different health conditions, social isolation measures, vaccination rates, and other factors. A descriptive analysis is conducted for the spread of the epidemic in the BRICS countries. Considering the nonlinear and nonstationary characteristics of COVID-19 data, a principle of decomposition-reconstruction(R)-prediction-integration is proposed. Correspondingly, this paper constructs an integrated deep learning prediction model of CEEMDAN-R-ILSTM-Elman. Specifically, the prediction model is integrated by complete ensemble empirical mode decomposition (CEEMDAN), improved long-term and short-term memory network (ILSTM), and Elman neural network. First, the data is decomposed by adopting CEEMDAN. Then, by calculating the permutation entropy and average period, the decomposed eigenmode component IMFs are reconstructed into four sequences of high, medium, low level, and trend term. Thus, ILSTM and Elman algorithms are used for component sequence prediction, whose results are integrated as the final results. The ILSTM is established based on the LSTM model and the improved beetle antennae search algorithm (IBAS). The ILSTM mainly considers that the prediction accuracy of LSTM model is vulnerable to the influence of parameter selection. The IBAS with adaptive step size is used to automatically optimize the super parameters of LSTM model and to improve the modeling efficiency and prediction accuracy. Experimental results indicate that compared with other benchmark models, CEEMDAN-R-ILSTM-Elman integrated model predicts the number of newly confirmed cases of COVID-19 in BRICS countries with higher accuracy and lower error. Strict social policies have a greater impact on the infection rate and mortality rate of the epidemic. During July-August 2021, epidemic spread in BRICS countries will slow down, and the overall situation is still quite severe.

Hybrid intelligence approach for performance estimation of rectangular CFST columns under different loading conditions

The ultimate bearing capacity (UBC) is a crucial design parameter of compressed rectangular concrete-filled steel tube (RCFST) members. Past extensive experimental studies have demonstrated that the UBC of RCFST has a highly nonlinear relationship with its constitutes due to the sophisticated interaction mechanism between steel tube and concrete core. 1) Although massive experimental data are available in literatures, conventional methods still struggle in accurately modeling and predicting the UBC of RCFST based on existing dataset. 2) RCFST columns with various loading conditions are treated differently in an analytical method, which does not satisfy the consistency of modern design. In this study, a hybrid data-driven model is developed to predict the UBC of both axially and eccentrically loaded RCFST using multilayer perceptron (MLP) neural network integrated with beetle antennae search (BAS) algorithm, abbreviated as BAS-MLP. MLP is developed using existing experimental data, while BAS is introduced to optimize the initial connection weights and biases of MLP to further enhance its nonlinear mapping capacity. In the proposed BAS-MLP model, input variables include geometrical and material properties of RCFST columns with different loads, and the model output is UBC. The proposed model is evaluated using additional testing datasets, and the prediction accuracy of BAS-MLP is verified by comparing obtained results with those from baseline models using conventional modeling methods. A global sensitivity analysis is further conducted to measure the contribution of each input variable affecting the UBC of RCFST. The results show that the adaptive BAS-MLP model performs better than several baseline models and conventional methods (analytical methods and numerical simulation), and geometrical properties of RCFST columns are highly correlated with UBC. As a result, it can be regarded as an effective tool to predict the mechanical behavior of RCFST columns and other composite members, thereby reducing the cost and time in material planning and design.

An efficient energy consumption and delay aware autonomous data gathering routing protocol scheme using a deep learning mobile edge model and beetle antennae search algorithm for underwater wireless sensor network

AbstractUnderwater wireless sensor network (UWSN) is used to monitor the compactness of ocean surveillance, marine and harsh underwater environment. In this article, an efficient energy consumption and delay aware autonomous data gathering routing protocol (ADGRP) scheme based on deep learning (dl) mobile edge model (mem) and beetle antennae search algorithm (BASA) for UWSN is proposed to overcome above problems. ADGRP is used to gather more data from the underwater environment by the use of the autonomous underwater vehicle (AUV). DL‐MEM is used to increase the network life time. Then the deep learning parameters are optimized by using BAS. The objective function is “to increase the efficiency and lifetime of network by decreasing the energy consumptions and delay.” The simulation process is carried out in MATLAB site. The proposed ADGRP‐DL‐MEM‐BASA provides lower energy consumption 20.83%, 34.66%, 18.03%, 20.92%, 22.34%, lower energy drop 7.85%, 23.94%, 17.93%, 21.93%, 31.94% is compared with the existing energy‐efficient probabilistic depth‐based routing (EEPDBR‐UWSN), ordered contention MAC (OCMAC‐UWSN), Q‐learning based energy‐efficient and void avoidance routing protocol for underwater acoustic sensor networks (QL‐EEBDG‐UWSN), energy‐efficient depth‐base opportunistic routing along Q‐learning for underwater wireless sensor networks (EDORQ‐UWSN), channel‐aware reinforcement learning‐based multipath adaptive routing for underwater wireless sensor networks (CARMA‐ EE‐UWSN) respectively.

AI-Based Secure Construction of University Information Services Platform

The service efficiency of the university information services platform directly affects the efficiency of university management. However, the university has rich data resources and complex management, and how to construct a comprehensive, standardized, efficient, and shared university information services platform has become a research hotspot. In recent years, the progress of artificial intelligence (AI) in many aspects has created opportunities for its large-scale application in smart campuses. With the wide penetration of AI technology into all walks of life, especially in the fields of industry, commerce, finance, security, and so on, some technologies have experienced practical tests. Many scholars have discussed the significance and possibility of the application of AI in the field of education from a theoretical level. The university’s secure information services platform can penetrate into all details of university management. Therefore, this paper studies the construction of a university secure information services platform based on AI, taking the dormitory allocation of freshmen and the face recognition of each building as the entry points. First, the beetle antennae search algorithm was introduced to improve the clustering efficiency and accuracy of the K-means algorithm for intelligent dormitory allocation. Then, the improved variational auto encoder-generative adversarial networks (VAE-GAN) model and convolutional neural networks (CNN)-based face recognition algorithm are proposed to enhance the security of building entry in universities. Finally, the simulation results reveal that the proposed two algorithms improve the clustering efficiency in dormitory allocation and the security of the university in the basic construction of the university information services platform, respectively.

Parameter optimization of unmanned surface vessel propulsion motor based on BAS-PSO

Despite advances in modern control theory and artificial intelligence technology, current methods for tuning proportional-integral-derivative (PID) controller parameters based on the traditional particle swarm optimization (PSO) algorithm do not meet the requirements for controlling an unmanned surface vessel (USV) propulsion motor. To overcome the disadvantages of the PSO algorithm, such as low precision and easily falling into a local optimum, the beetle antennae search (BAS) algorithm can be introduced into the PSO algorithm by replacing particles with beetles, and effectively prevents the PSO algorithm from easily falling into the local optimum. At the same time, the BAS algorithm will no longer be limited to single objective parameterization. Herein, we propose a PID parameter optimization method based on the hybrid BAS-PSO algorithm for a USV propulsion motor. The PID parameter optimization of propulsion motor effectively becomes a beetle foraging problem with group optimization. Numerical results show that the method can effectively solve the problems of PSO and greatly improve convergence speed. Compared with the genetic algorithm and standard PSO algorithm, the BAS-PSO algorithm is superior for PID parameter tuning and can improve performance of USV propulsion system.

Parking Demand Prediction Method of Urban Commercial-Office Complex Buildings Based on the MRA-BAS-BP Algorithm

With the increasingly significant trend of developing urban land for mixed-use, an increasing number of urban commercial and office complexes have been built. The parking demand characteristics of such buildings are more complex than the parking demand characteristics of single-use buildings due to more diverse influencing factors. As there are complicated linear and nonlinear relationships between parking demand and influencing factors, it is difficult to accurately predict parking demand using a single multiple regression analysis (MRA) model. Hence, in this paper, a combined algorithm based on the MRA model, beetle antennae search (BAS) algorithm, and BP neural network is proposed for demand prediction. In this paper, a two-level and ten-category index system is established and then mixed with the BP algorithm through the MRA model to improve the overall robustness and accuracy of the algorithm. Then, the BAS algorithm is used to search for optimal parameters involved in the BP neural network to avoid local optimization and improve the accuracy and efficiency of prediction. Finally, an instance analysis is carried out for verification, and the result indicates that the parking demand prediction accuracy of the MRA-BAS-BP algorithm is higher than the prediction accuracy of the traditional algorithm.

Control System of Liquid Fertilizer Variable-Rate Fertilization Based on Beetle Antennae Search Algorithm

In order to solve the problems of low precision of variable-rate fertilization and uneven fertilization flow of field liquid fertilizer applicator, a control system of variable-rate fertilization of liquid fertilizer based on beetle antennae search algorithm was proposed. First of all, this study established a mathematical model for the variable-rate fertilization control system of liquid fertilizer. Then, according to the control requirements, the search algorithm is used to optimize the three parameters of Proportion Integration Differentiation (PID). Finally, the response time and overshoot of the system are analyzed by software simulation, and the PID control based on beetle antennae search algorithm is compared and analyzed with fuzzy PID control and traditional PID control. The control effect of the control system is verified by a bench test. The results show that the actual response time of the variable-rate fertilization control system based on the beetle antennae search algorithm can reach 2 s, and the average relative error can reach 1.27%. Therefore, the control system of this study can achieve a better control effect, and the control method of this study provides a feasible scheme for the study of variable-rate fertilization.

A new localization method in internet of things by improving beetle antenna search algorithm

A new localization method in internet of things by improving beetle antenna search algorithm

Adaptive Control Based on Neural Network and Beetle Antennae Search Algorithm for an Active Heave Compensation System

Adaptive Control Based on Neural Network and Beetle Antennae Search Algorithm for an Active Heave Compensation System

Geometric Parameter Identification of Medical Robot Based on Improved Beetle Antennae Search Algorithm.

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 the search process of the beetle antennae search algorithm to strengthen its capability for local search. Following this, we improve the global convergence ability of the beetle antennae search algorithm by using the simulated annealing algorithm. We compare the accuracy of end positioning of the proposed algorithm with the frog-jumping algorithm and the beetle antennae search algorithm with variable step length through simulations. The results show that the proposed algorithm has a higher accuracy of convergence, and can significantly improve the accuracy of end positioning of the medical robot.

Dual Beetle Antennae Search system for optimal planning and robust control of 5-link biped robots

In this paper, we presented an optimization-based control framework for the trajectory planning and the stable control of multiple non-linear systems, i.e., 5-link Biped Robot, inverted pendulum, and acrobot using the BAS (Beetle Antennae Search) algorithm with PID (Proportional–Integral–Derivative) controller. Traditional approaches treat path planning and stable control of the system as a separate problem, or multiple controls are employed to achieve both goals. Therefore, these approaches are complex, computationally, and time-wise expensive. The addition of disturbance to the system makes the control further intricate. In this paper, we proposed an optimization-based approach for the path planning and stable control of the non-linear systems by employing a hybrid controller, which includes BAS and PID. We devised a switching mechanism, where BAS will first perform the trajectory optimization through feedforward control. In the second phase, a disturbance is introduced in the system, and BAS is used to optimize the K values of PID for the control stability. In the simulation, we applied this technique on three non-linear systems, i.e., 5-link Biped Robot, inverted pendulum, and acrobot, and promising results are obtained. In addition we performed a comparison of dual Beetle Antennae Search algorithm with known swarm algorithms, i.e. PSO (Particle Swarm Optimization) and GA (Genetic Algorithm). The results showed that BAS and PSO are comparable. For example, in inverted pendulum the objective function converges to 7.566×10−4 and 0.029 by BAS and PSO respectively. Likewise, in 5-link Biped Robot the objective function converges to 0.0250 and 0.0351 by BAS and PSO respectively. In addition, the number of function evaluations in case of BAS increases linearly, i.e., 3 times of the iterations. However, the function evaluations increase exponentially in case of PSO and GA.

Mechanical Performance Prediction for Sustainable High-Strength Concrete Using Bio-Inspired Neural Network

High-strength concrete (HSC) is a functional material possessing superior mechanical performance and considerable durability, which has been widely used in long-span bridges and high-rise buildings. Unconfined compressive strength (UCS) is one of the most crucial parameters for evaluating HSC performance. Previously, the mix design of HSC is based on the laboratory test results which is time and money consuming. Nowadays, the UCS can be predicted based on the existing database to guide the mix design with the development of machine learning (ML) such as back-propagation neural network (BPNN). However, the BPNN’s hyperparameters (the number of hidden layers, the number of neurons in each layer), which is commonly adjusted by the traditional trial and error method, usually influence the prediction accuracy. Therefore, in this study, BPNN is utilised to predict the UCS of HSC with the hyperparameters tuned by a bio-inspired beetle antennae search (BAS) algorithm. The database is established based on the results of 324 HSC samples from previous literature. The established BAS-BPNN model possesses excellent prediction reliability and accuracy as shown in the high correlation coefficient (R = 0.9893) and low Root-mean-square error (RMSE = 1.5158 MPa). By introducing the BAS algorithm, the prediction process can be totally automatical since the optimal hyperparameters of BPNN are obtained automatically. The established BPNN model has the benefit of being applied in practice to support the HSC mix design. In addition, sensitivity analysis is conducted to investigate the significance of input variables. Cement content is proved to influence the UCS most significantly while superplasticizer content has the least significance. However, owing to the dataset limitation and limited performance of ML models which affect the UCS prediction accuracy, further data collection and model update must be implemented.

Multi-objective optimisation design for GFRP tendon reinforced cemented soil

Rebar reinforced cemented soil is employed widely to solve the weak foundation problem led by sludge particularly. Nowadays, the glass fiber-reinforced polymer (GFRP) becomes a new tendon material instead of steel to avoid the performance degradation resulting from steel corrosion. The interface bond strength of GFRP tendon-reinforced cemented soils (GTRCS) displays its excellent mechanical capacity. Nevertheless, its application is obstructed by the deficient studies between the bond strength and influence factors. Therefore, this study investigates the effects of varying water contents (Cw: 50%-90%), cement proportions (Cc: 6%-30%), and curing periods (Tc: 28 days, 90 days) on both pullout strength (Tp) and unconfined compression strength (UCS) of GTRCS. The results showed that the pullout strength and compressive strength were positively related to Tc and Cc and negatively related to Cw. Besides, these experimental results were also utilised to develop support vector regression (SVR) models. The beetle antennae search (BAS) algorithm was used to adjust the SVR’s hyperparameters. The high correlation coefficients (0.988 for UCS and 0.972 for Tp) proved the reliability of the established BAS-SVR models. In addition, the multi-objective beetle antennae search algorithm (MOBAS-SVR) was developed for bi-objective optimisation designs (UCS-cost and Tp-cost). Finally, sensitivity analysis was conducted to range the significance of variables for Tp and UCS.

Lower limb exoskeleton robots’ dynamics parameters identification based on improved beetle swarm optimization algorithm

AbstractEfficient and high-precision identification of dynamic parameters is the basis of model-based robot control. Firstly, this paper designed the structure and control system of the developed lower extremity exoskeleton robot. The dynamics modeling of the exoskeleton robot is performed. The minimum parameter set of the identified parameters is determined. The dynamic model is linearized based on the parallel axis theory. Based on the beetle antennae search algorithm (BAS) and particle swarm optimization (PSO), the beetle swarm optimization algorithm (BSO) was designed and applied to the identification of dynamic parameters. The update rule of each particle originates from BAS, and there is an individual’s judgment on the environment space in each iteration. This method does not rely on the historical best solution in the PSO and the current global optimal solution of the individual particle, thereby reducing the number of iterations and improving the search speed and accuracy. Four groups of test functions with different characteristics were used to verify the performance of the proposed algorithm. Experimental results show that the BSO algorithm has a good balance between exploration and exploitation capabilities to promote the beetle to move to the global optimum. Besides, the test was carried out on the exoskeleton dynamics model. This method can obtain independent dynamic parameters and achieve ideal identification accuracy. The prediction result of torque based on the identification method is in good agreement with the ideal torque of the robot control.

Weak signal enhancement for machinery fault diagnosis based on a novel adaptive multi-parameter unsaturated stochastic resonance

The output saturation problem limits the application of classical bistable stochastic resonance (CBSR) in weak fault diagnosis. Furthermore, the adjustment of system parameters is essential to generate stochastic resonance (SR) for achieving the optimal output. In this paper, a novel adaptive multi-parameter unsaturation bistable stochastic resonance (AMUBSR) system based on piecewise linearization of potential function is proposed. The beetle antennae search (BAS) is adopted to optimize the system parameters, and the output signal-to-noise ratio (SNR) is selected as the objective function. The optimization results of the simulation signal prove that the validity and superiority of BAS algorithm in parameter matching compared with particle swarm optimization (PSO) algorithm and ant colony optimization (ACO) algorithm. Finally, the proposed method obtains the further improvement of the output SNR, higher spectrum peak value at characteristic frequency and bigger recognition quantity than the CBSR method from the diagnosis results of bearing inner ring and outer ring fault signals.

Data-driven two-stage fuzzy random mixed integer optimization model for facility location problems under uncertain environment

<abstract><p>This paper studies the problem of facility location in a hybrid uncertain environment with both randomness and fuzziness. We establish a data-driven two-stage fuzzy random mixed integer optimization model, by considering the uncertainty of transportation cost and customer demand. Given the complexity of the model, this paper based on particle swarm optimization (PSO), beetle antenna search algorithm (BAS) and interior point algorithm, a hybrid intelligent algorithm (HIA) is proposed to solve two-stage fuzzy random mixed integer optimization model, yielding the optimal facility location and maximal expected return of supply chain simultaneously. Finally, taking the supply chain of medical mask in Shanghai as an example, the influence of uncertainty on the location of processing factory was studied. We compare the HIA with hybrid PSO and hybrid genetic algorithm (GA), to validate the proposed algorithm based on the computational time and the convergence rate.</p></abstract>