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This paper presents a computational model for one kind of two-degree-of-freedom (2-DOF) upper limb exoskeleton robot. This robot consists of two connected body segments to imitate the upper shoulder and elbow joints of human hand. Two sets of direct current (DC) motors are severally used to drive the robotic shoulder and elbow joints. The dynamic of the DC motor is accurately developed to establish the relationship between the input current and the actuated force/torque. The PID controller is considered as a standard in technical and industrial applications, and selected to control the whole system in this paper. Therefore, the tuning of PID gains is extremely important, while utilizing optimization algorithms to reduce the tracking error is one approach of tuning PID gains. This paper presented the improved beetle antennae search (IBAS) algorithm to optimize the PID gains. The original BAS algorithm can achieve a wide search range, cost low time complexity and gain high search accuracy. Moreover, the proposed IBAS algorithm can realize more extensive search scope and more precise search compared with the original BAS algorithm. Finally, the IBAS algorithm is compared with the original BAS, PSO and GA algorithms on the basis of the optimization results. The comparison results demonstrated that the IBAS algorithm gains superior performance in addressing PID tuning problems. [ABSTRACT FROM AUTHOR]

The efficacy of meta-heuristic algorithms has been demonstrated in solving unconstrained optimization problems. Inspired by the food foraging behavior of beetles, we propose herein a model-free meta-heuristic optimization algorithm, referred to as the Beetle Antennae Search-Bellwether Swarm, which incorporates the schemes of adaptive antenna fiber length and direction vector. A joint annealing-heating scheme along with the re-initialization mechanism is proposed such that each antenna fiber length could simultaneously gradually decrease or increase in different circumstances. We also propose to adapt each individual antenna fiber length based on the best smell perception direction (bellwether). The proposed schemes of adaptive antenna fiber length yield the algorithm almost insusceptible to different initial antenna fiber lengths for a relatively large range of initial antenna fiber lengths. The softmax combiner is leveraged to obtain the direction vector such that the beetle would be steered to step toward the new randomly combined direction and arrive at a candidate position. The beetle position is further updated by evaluating whether the candidate position is conducive to minimizing the objective function. The illustrative simulations demonstrate the fast initial convergence, cost-effectiveness and feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Continuum robot has become a research hotspot due to its excellent dexterity, flexibility and applicability to constrained environments. However, the effective, secure and accurate path planning for the continuum robot remains a challenging issue, for that it is difficult to choose a suitable inverse kinematics solution due to its redundancy in the confined environment. This paper presents a collision-free path planning method based on the improved artificial potential field (APF) for the cable-driven continuum robot, in which the beetle antennae search algorithm is adopted to deal with the optimal problem of APF without the necessary for velocity kinematics. In addition, the local optimum problem of traditional APF is solved by the randomness of the antennae's direction vector which can make the algorithm easily jump out of local minima. The simulation and experimental results verify the efficiency of the proposed path planning method. [ABSTRACT FROM AUTHOR]

The beetle antennae search algorithm (BAS) is inspired by the search behavior of long-horned beetles and is widely used in optimization problems. However, in the typical BAS algorithm, there are problems such as the insufficient optimization ability of beetle individuals and the lack of consideration of historical dynamic information in the algorithm search process, resulting in sufferings from local optimum, low diversity, and imbalanced utilization, etc. To solve these problems, an improved beetle swarm antennae search algorithm based on multiple operators, dubbed MO-BSAS, is proposed. First, the elite opposition-based learning is used to initialize the beetle population, which improves the population diversity and optimization ability of the algorithm; then, the Lévy Flight strategy is used to improve the traditional beetle moving operator. In addition, a multi-operator search strategy determined by the damped sinusoidal probability factor is designed to balance the exploitation and exploration capabilities of the algorithm and speed up its convergence. The MO-BSAS algorithm is compared with BSA and 8 popular or state-of-the-art smart algorithms under 14 different benchmark functions, and the experimental results show that the MO-BSAS algorithm has a shorter convergence time and better convergence accuracy than the algorithms compared, which verifies the effectiveness of the improved algorithm. [ABSTRACT FROM AUTHOR]

Copyright of China Mining Magazine is the property of China Mining Magazine Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

The design of geopolymer concrete must meet more stringent requirements for the landscape, so understanding and designing geopolymer concrete with a higher compressive strength challenging. In the performance prediction of geopolymer concrete compressive strength, machine learning models have the advantage of being more accurate and faster. However, only a single machine learning model is usually used at present, there are few applications of ensemble learning models, and model optimization processes is lacking. Therefore, this paper proposes to use the Firefly Algorithm (AF) as an optimization tool to perform hyperparameter tuning on Logistic Regression (LR), Multiple Logistic Regression (MLR), decision tree (DT), and Random Forest (RF) models. At the same time, the reliability and efficiency of four integrated learning models were analyzed. The model was used to analyze the influencing factors of geopolymer concrete and determine the strength of their influencing ability. According to the experimental data, the RF-AF model had the lowest RMSE value. The RMSE value of the training set and test set were 4.0364 and 8.7202, respectively. The R value of the training set and test set were 0.9774 and 0.8915, respectively. Therefore, compared with the other three models, RF-AF has a stronger generalization ability and higher prediction accuracy. In addition, the molar concentration of NaOH was the most important influencing factors, and its influence was far greater than the other possible factors including NaOH content. Therefore, it is necessary to pay more attention to NaOH molarity when designing geopolymer concrete. [ABSTRACT FROM AUTHOR]

Undoubtedly, one of the most common machine learning challenges is multiclass classification. In light of this, a novel bio-inspired neural network (NN) has been developed to address multiclass classification-related issues. Given that weights and structure determination (WASD) NNs have been acknowledged to alleviate the disadvantages of conventional back-propagation NNs, such as slow training pace and trapping in a local minimum, we developed a bio-inspired WASD algorithm for multiclass classification problems (BWASDC) by using the metaheuristic beetle antennae search (BAS) algorithm to enhance the WASD algorithm's learning process. The BWASDC's effectiveness is then evaluated through applications in occupational classification systems. It is important to mention that systems of occupational classification serve as a fundamental indicator of occupational exposure. For this reason, they are highly significant in social science research. According to the findings of four occupational classification experiments, the BWASDC model outperformed some of the most modern classification models obtainable through MATLAB's classification learner app on all fronts.

Numerous people are applying for bank loans as a result of the banking industry's expansion, but because banks only have a certain amount of assets to lend to, they can only do so to a certain number of applicants. Therefore, the banking industry is very interested in finding ways to reduce the risk factor involved in choosing the safe applicant in order to save lots of bank resources. These days, machine learning greatly reduces the amount of work needed to choose the safe applicant. Taking this into account, a novel weights and structure determination (WASD) neural network has been built to meet the aforementioned two challenges of credit approval and loan approval, as well as to handle the unique characteristics of each. Motivated by the observation that WASD neural networks outperform conventional back-propagation neural networks in terms of sluggish training speed and being stuck in local minima, we created a bio-inspired WASD algorithm for binary classification problems (BWASD) for best adapting to the credit or loan approval model by utilizing the metaheuristic beetle antennae search (BAS) algorithm to improve the learning procedure of the WASD algorithm. Theoretical and experimental study demonstrate superior performance and problem adaptability. Furthermore, we provide a complete MATLAB package to support our experiments together with full implementation and extensive installation instructions. [ABSTRACT FROM AUTHOR]

In this paper, we presented a Quadratic Interpolated Beetle Antennae Search (QIBAS), a variant of the Beetle Antennae Search (BAS) algorithm to solve the higher dimensional portfolio selection problem. The computational efficiency of BAS and its probabilistic global convergence made it viable to solve real-world optimization-based problems. Despite its numerous application, it is less accurate, not scalable, and its performance deteriorates as the dimension of the problem increases. To overcome this, QIBAS integrates BAS with the robust approximation of quadratic interpolation. We employed QIBAS to a well-known finance problem known as Portfolio Selection as a testbed. Traditionally, the portfolio problem is modeled as a convex optimization problem, which is efficient to solve but inaccurate. The cardinality constrained model with higher dimensional stock data includes stringent real-world constraints. It is more accurate but computationally challenging and not tractable, making it a perfect candidate to test QIBAS. The primary goal is to minimize the risk and maximize the profit while selecting the portfolio. We included up to 250 companies in simulation and compared the results with BAS and two state-of-the-art swarm metaheuristic algorithms, i.e., Particle Swarm Optimization and Genetic algorithm. The results showed the promising performance of QIBAS in comparison with other algorithms. [ABSTRACT FROM AUTHOR]

As an essential component of a universal CNC machine tool, the spindle plays a critical role in determining the accuracy of machining parts. The three cutting process parameters (cutting speed, feed speed, and cutting depth) are the most important optimization input parameters for studying process optimization. Better processing quality is often achieved through their optimization. Therefore, it is necessary to study the three cutting process parameters of the CNC machine tool spindle. In this paper, we proposed an improved algorithm incorporated with the beetle antennae search algorithm for the most probable explanation in Bayesian networks to achieve optimization calculation of process parameters. This work focuses on building adaptive dynamic step parameters to improve detection behavior. The chaotic strategy is discretized and used to establish the dominant initial population during the population initialization. This article uses four standard network data sets to compare the time and fitness values based on the improved algorithm. The experimental results show that the proposed algorithm is superior in time and accuracy compared to similar algorithms. At the same time, an optimization example for the actual machining of a universal CNC machine tool spindle was provided. Through the optimization of this algorithm, the true machining quality was improved. [ABSTRACT FROM AUTHOR]

Currently, the widely used blind source separation algorithm is typically associated with issues such as a sluggish rate of convergence and unstable accuracy, and it is mostly suitable for the separation of independent source signals. Nevertheless, source signals are not always independent of one another in practical applications. This paper suggests a blind source separation algorithm based on the bounded component analysis of the enhanced Beetle Antennae Search algorithm (BAS). Firstly, the restrictive assumptions of the bounded component analysis method are more relaxed and do not require the signal sources to be independent of each other, broadening the applicability of this blind source separation algorithm. Second, the objective function of bounded component analysis is optimized using the improved Beetle Antennae Search optimization algorithm. A step decay factor is introduced to ensure that the beetle does not miss the optimal point when approaching the target, improving the optimization accuracy. At the same time, since only one beetle is required, the optimization speed is also improved. Finally, simulation experiments show that the algorithm can effectively separate independent and dependent source signals and can be applied to blind source separation of images. Compared to traditional blind source separation algorithms, it has stronger universality and has faster convergence speed and higher accuracy compared to the original independent component analysis algorithm. [ABSTRACT FROM AUTHOR]

There are few studies on the intelligent guidance of unmanned sailboats, which should coordinate pluralistic tasks at sea in the nature of its maneuvring intractability. To ensure the algorithmic practicability, this paper proposes a path-following and collision-avoidance guidance approach of unmanned sailboats with total formulaic description. The risk-detecting mechanism is fabricated by setting a circular detecting zone and using the time to the closest point of approach. Then, the risk of collision, the path deviation, the speed loss, and the course loss can be judged by constructing the cost functions and applying the distance to closest point of approach. The optimized heading angle is deemed as the one minimizing the aggregate cost functions, which is sought by applying and improving the beetle antennae search (BAS) algorithm. In the proposed modified BAS, the searching step is redesigned to enhance the searching efficiency. To ensure the convergence of the real heading angle to the reference, the backstepping-based control law is fabricated for the high-order sailboat model and in the linear form. The control parameters are offline optimized through the modified BAS. Compared with the adaptive control, this controller can guarantee more computation simplicity and the optimized control performance. Finally, simulation corroborates that the sailboat can successfully complete path following and collision avoidance while encountering multiple static and moving obstacles under the proposed schemes. [ABSTRACT FROM AUTHOR]

The core components of both traditional and contemporary control systems are the proportional–integral–derivative (PID) control systems, which have established themselves as standards for technical and industrial applications. Therefore, the tuning of the PID controllers is of high importance. Utilizing optimization algorithms to reduce the mean square error of the controller’s output is one approach of tuning PID controllers. In this paper, an appropriately modified metaheuristic optimization algorithm dubbed beetle antennae search (BAS) is employed for robust tuning of PID controllers. The findings of three simulated experiments on stabilizing feedback control systems show that BAS produces comparable or higher performance than three other well-known optimization algorithms while only consuming a tenth of their time.

Considering that the convergence of beetle antennae search algorithm (BAS) is of highly individual dependence, poor exploration ability and easily falling into local optimal solution, a beetle antennae search algorithm based on differential evolution strategy (BASD) is proposed. The algorithm not only uses the good point set method to initialize the beetle population to enhance the population diversity, but also introduces the concept of dynamic differential evolution to an elite evolutionary competition guidance strategy, which better balances the mining and exploration capabilities of the algorithm. The BASD algorithm is tested on 14 benchmark functions and compared with the optimization results of several advanced algorithms. The results show that the overall optimization performance of the BASD algorithm is better. Finally, the BASD algorithm is applied in image enhancement, and the result shows that the gray distribution of the image enhanced by the BASD algorithm is more uniform and the distribution range is larger. [ABSTRACT FROM AUTHOR]

Aiming at the intelligent production line scheduling problem, a production line scheduling method considering multiple constraints was proposed. Considering the constraints of production task priority, time limit, and urgent task insertion, a production process optimization scheduling calculation model was established with the minimum waiting time and minimum completion time as objectives. The BAS was used to solve the problem, and a fast response mechanism for emergency processing under multiple constraints was established. Compared with adaptive particle swarm optimization (APSO) and non-dominated sorting genetic algorithm-II (NSGA-II) operation, this algorithm showed its superiority. The practical application in garment processing enterprises showed that the method was effective and can reduce the completion time and waiting time. [ABSTRACT FROM AUTHOR]

In order to simplify the solution method of inverse kinematics of the manipulator， improve the accuracy of the solution， and enhance the universality of the method， the intelligent optimization algorithms are used to turn the inverse kinematic solution into an optimization problem. Firstly， the objective function of the end pose error according to the positive kinematic equation is established， and the best flexibility and energy consumption constraints to construct the fitness function are combined so that the manipulator has the best flexibility and lower energy consumption when meeting the requirements of the pose error； In order to unify the accuracy of position and attitude convergence and control the evolution direction， the pose balance coefficient and the pose error adjustment coefficient are introduced； Then， the individual optimization of the beetle search with excellent performance in group optimization is integrated with the differential evolution algorithm， so as to improve the convergence accuracy and speed of the algorithm； Finally， inverse solution experiments are conducted on the 6R manipulator and the redundant manipulator. The results show that the proposed algorithm has high convergence accuracy， a fast speed and a good versatility.

The design of geopolymer concrete must meet more stringent requirements for the landscape, so understanding and designing geopolymer concrete with a higher compressive strength challenging. In the performance prediction of geopolymer concrete compressive strength, machine learning models have the advantage of being more accurate and faster. However, only a single machine learning model is usually used at present, there are few applications of ensemble learning models, and model optimization processes is lacking. Therefore, this paper proposes to use the Firefly Algorithm (AF) as an optimization tool to perform hyperparameter tuning on Logistic Regression (LR), Multiple Logistic Regression (MLR), decision tree (DT), and Random Forest (RF) models. At the same time, the reliability and efficiency of four integrated learning models were analyzed. The model was used to analyze the influencing factors of geopolymer concrete and determine the strength of their influencing ability. According to the experimental data, the RF-AF model had the lowest RMSE value. The RMSE value of the training set and test set were 4.0364 and 8.7202, respectively. The R value of the training set and test set were 0.9774 and 0.8915, respectively. Therefore, compared with the other three models, RF-AF has a stronger generalization ability and higher prediction accuracy. In addition, the molar concentration of NaOH was the most important influencing factors, and its influence was far greater than the other possible factors including NaOH content. Therefore, it is necessary to pay more attention to NaOH molarity when designing geopolymer concrete.

Aiming at the fact that the performance of support vector machine （SVM） in vehicle gearbox fault diagnosis is greatly affected by parameters，a new method of vehicle gearbox fault diagnosis based on improved SVM is proposed based on the research of beetle antennae search （BAS）. Compared with BAS，IBAS modified the step size formula and added Gaussian variation behavior，so that the search ability of the algorithm in the early and late period is balanced and has the ability to jump out of the local optimal，and better SVM parameters could be obtained. The results of gearbox fault diagnosis show that the proposed method can effectively improve the accuracy of fault diagnosis，and the results are better than those of other methods.

NOx concentration is an important indicator of the response to ammonia dosage and nitrogen emissions, and its accurate prediction allows for efficient and rational optimal control of ammonia dosage. Due to the large external noise, time lag and non-linearity of the cement denitrification process, it is difficult to derive accurate mathematical prediction models. Therefore, a new machine learning model, namely EEMD-MImRMR-BASBP, is developed. Firstly, Ensemble Empirical Mode Decomposition (EEMD) and median-averaged filtering is used to process the data and remove the noise. In order to handle the large time lags, non-linearity and non-smoothness among the variables, mutual information (MI) based on the entropy principle is proposed to calculate the lag time of the non-linear system; furthermore, according to the feature variable selection method of Max-Relevance and Min-Redundancy (mRMR), the factors with strong influence are selected as the input variables of the prediction model in combination with the results of the mechanism analysis. Then, the EEMD-MImRMR-BASBP model to predict NOX concentration is constructed, in which the initialization parameters of the Back Propagation Neural Network (BP) are searched by Beetle Antennae Search (BAS) to effectively overcome the parameter selection problem of traditional BP prediction models. Finally, the model was applied for the NOX concentration prediction of a real cement plant in Jiang xi and Fu ping and compared with the classical BP-based prediction model, BASBP model, the root means square error (RMSE) and mean absolute error (MAE) of the EEMD-MImRMR-BASBP model for the two production lines are only 0.2927, 0.3513 and 0.1795, and 0.2383, which have better prediction performance compared with the current model. [ABSTRACT FROM AUTHOR]

The Beetle Swarm Optimization (BSO) algorithm is a high-performance swarm intelligent algorithm based on beetle behaviors. However, it suffers from poor search speeds and is prone to local optimization due to the size of the step length. To address this further, a novel improved opposition-based learning mechanism is utilized, and an adaptive beetle swarm optimization algorithm with novel opposition-based learning (NOBBSO) is proposed. In the proposed NOBBSO algorithm, the novel opposition-based learning is designed as follows. Firstly, according to the characteristics of the swarm intelligence algorithms, a new opposite solution is obtained to generate the current optimal solution by iterations in the current population. The novel opposition-based learning strategy is easy to converge quickly. Secondly, an adaptive strategy is used to make NOBBSO parameters self-adaptive, which makes the results tend to converge more easily. Finally, 27 CEC2017 benchmark functions are tested to verify its effectiveness. Comprehensive numerical experiment outcomes demonstrate that the NOBBSO algorithm has obtained faster convergent speed and higher convergent accuracy in comparison with other outstanding competitors. [ABSTRACT FROM AUTHOR]

The recently emerging multi-portfolio selection problem lacks a proper framework to ensure that client privacy and database secrecy remain intact. Since privacy is of major concern these days, in this paper, we propose a variant of Beetle Antennae Search (BAS) known as Distributed Beetle Antennae Search (DBAS) to optimize multi-portfolio selection problems without violating the privacy of individual portfolios. DBAS is a swarm-based optimization algorithm that solely shares the gradients of portfolios among the swarm without sharing private data or portfolio stock information. DBAS is a hybrid framework, and it inherits the swarm-like nature of the Particle Swarm Optimization (PSO) algorithm with the BAS updating criteria. It ensures a robust and fast optimization of the multi-portfolio selection problem whilst keeping the privacy and secrecy of each portfolio intact. Since multi-portfolio selection problems are a recent direction for the field, no work has been done concerning the privacy of the database nor the privacy of stock information of individual portfolios. To test the robustness of DBAS, simulations were conducted consisting of four categories of multi-portfolio problems, where in each category, three portfolios were selected. To achieve this, 200 days worth of real-world stock data were utilized from 25 NASDAQ stock companies. The simulation results prove that DBAS not only ensures portfolio privacy but is also efficient and robust in selecting optimal portfolios. [ABSTRACT FROM AUTHOR]

Cement stabilized soil (CSS) yields wide application as a routine cementitious material due to cost-effectiveness. However, the mechanical strength of CSS impedes development. This research assesses the feasible combined enhancement of unconfined compressive strength (UCS) and flexural strength (FS) of construction and demolition (C&D) waste, polypropylene fiber, and sodium sulfate. Moreover, machine learning (ML) techniques including Back Propagation Neural Network (BPNN) and Random Forest (FR) were applied to estimate UCS and FS based on the comprehensive dataset. The laboratory tests were conducted at 7-, 14-, and 28-day curing age, indicating the positive effect of cement, C&D waste, and sodium sulfate. The improvement caused by polypropylene fiber on FS was also evaluated from the 81 experimental results. In addition, the beetle antennae search (BAS) approach and 10-fold cross-validation were employed to automatically tune the hyperparameters, avoiding tedious effort. The consequent correlation coefficients (R) ranged from 0.9295 to 0.9717 for BPNN, and 0.9262 to 0.9877 for RF, respectively, indicating the accuracy and reliability of the prediction. K-Nearest Neighbor (KNN), logistic regression (LR), and multiple linear regression (MLR) were conducted to validate the BPNN and RF algorithms. Furthermore, box and Taylor diagrams proved the BAS-BPNN and BAS-RF as the best-performed model for UCS and FS prediction, respectively. The optimal mixture design was proposed as 30% cement, 20% C&D waste, 4% fiber, and 0.8% sodium sulfate based on the importance score for each variable. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Autonomous sailing robots are a new type of green ship that use wind energy to maintain continuous cruising operations. Compared with traditional algorithms, swarm intelligence optimization algorithms have better intelligence and adaptation. An intelligent algorithm acts as one of the most important solutions to the path planning problem of autonomous sailing robots. The beetle swarm optimization, which is a novel intelligent method that combines the search mechanism of a single beetle with the particle swarm optimization algorithm, is utilized to obtain the optimal path. In this study, the track navigation control of an improved mathematical model of a sailing ship is introduced, and the navigation is tested using a downsized prototype of an autonomous sailing robot. The improved beetle swarm optimization is proposed here by dynamically changing the step size factor and the inertia weight formula. In the iteration of the improved beetle swarm optimization algorithm, the location update cooperates with the beetle monomer search mechanism to learn the update strategy of the particle swarm optimization algorithm. Combinatorial strategies can speed up the overall iterative convergence speed and reduce the possibility that the population will fall into a locally optimal solution. The simulation results demonstrate the robustness, efficiency, and feasibility of the improved beetle swarm optimization in different cases. The research results can provide some references and ideas for the autonomous intelligent navigation control design of autonomous sailing robots.

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. [ABSTRACT FROM AUTHOR]

For the insufficient research on the simultaneously positioning and orientating system, a VLC positioning and orientating algorithm based on combining particle swarm optimization (PSO) and beetle antennae search (BAS) was proposed.The PSO algorithm was used to explore the optimal direction of the receiver and meanwhile the BAS algorithm was used to search the best three-dimensional coordinates of each particle in the current direction.Firstly, the indoor space of 3 m×3 m×5 m and the SNR of 40 dB were assumed in the simulation, the proposed VLC indoor positioning and orientating system could achieve the average positioning error of 4.82 cm and average orientating error of 2.24°.Then, in the laboratory space of 0.9 m×0.9 m×1.5 m, the experimental demonstration was accomplished for the first time, and the average positioning and orientating errors of this experimental system were 5.32 cm and 5.99°, respectively.Compared with the traditional VLC indoor positioning schemes, the proposed VLC positioning and orientating system does not need the prior knowledge of the height and direction of the receiver, which greatly reduces the system complexity and is applicable to a wider range of applications.

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rapid development of the Internet, more and more people pay attention to wireless sensor networks. Localization technology plays a vital role in wireless sensor networks. To reduce the localization error and improve the localization stability, a gray wolf localization algorithm based on beetle antennae search (BASGWO) is proposed, transforming the node localization problem into function constrained optimization. Firstly, the excellent point set method is used to initialize the gray wolf population, improving the richness. Secondly, the beetle antennae search mechanism with good global search ability is introduced into the gray wolf algorithm to avoid the gray wolf algorithm falling into local optimization in the late iteration. The gray wolf is the beetle antennae in search of excellence. The location of the gray wolf was updated according to the fitness value of the gray wolf and beetle antennae. The optimal global solution can be obtained, and then the unknown node coordinates can be obtained. The improved gray wolf algorithm improves the localization accuracy by 24% through simulation comparison and reduces the localization error fluctuation by 23%. Compared with the classical localization algorithm of WSN, the solution ability and localization accuracy of the BASGWO algorithm are improved. [ABSTRACT FROM AUTHOR]

In this paper, we propose a control framework for cooperative robotic agents, which constitutes an essential component in the construction of futuristic smart-homes. Such agents assist humans in efficiently completing household chores. Usability, human friendliness, autonomy, and intelligent decision-making are the top considerations for designing such a system along with reliability, accuracy, and efficiency. Implementing a distributed control algorithm considering these goals is a complicated task since classical control frameworks focus on specialized robots working in an industrial environment and do not capture unique features of the home environment. For example, a household robotic agent needs to perform several general-purpose tasks without assuming that the user has specialized training similar to an industrial operator. Since the challenges and goals in designing a household robotic agent are different, there is a need for a control framework centered around the required goals. The proposed control framework considers the collision problem between several cooperative robotic agents while assisting the human user. We propose an optimization-driven approach to avoid static and dynamic obstacles present in the environment while simultaneously controlling the robots as commanded by the user. We formulate the optimization problem that incorporates the required goals and then use a neural network to solve the optimization problem efficiently. The neural network, beetle antennae search zeroing neural network (BASZNN), is inspired by the natural behavior of beetles. It solves the optimization problem in a gradient-free manner contributing to the computational efficiency of the neural network. Additionally, the distributed-processing capability of the neural networks contributes to computational efficiency and matches the distributed nature of the underlying problem. For testing the performance of BASZNN, we use V-REP and MATLAB to simulate a household environment. Three cooperative agents (KUKA LBR IIWA 7 mounted on P3-DX) assist a person in moving a table around the room. The simulation results show that the BASZNN can accurately and robustly accomplish the required task. [ABSTRACT FROM AUTHOR]

In this paper, we propose a control framework for intelligent surgical robots under the Remote Center of Motion (RCM). The goal of a surgical robot is to assist surgeons in performing complex surgeries. RCM constraint implies that the surgical tip attached to the end-effector of the surgical robot does not slide away from the point of the incision while performing surgery. Implementation of a control algorithm to comply with RCM constraints is a complicated task because of the nonlinear model of the surgical robots and stringent conditions of accuracy imposed by the patient's safety. This paper proposes an optimization-driven approach to perform the surgical maneuver under RCM constraints. We then applied a bio-inspired optimization algorithm to solve the problem efficiently. For testing the performance of ZNNBAS, we used MATLAB to simulate a surgical procedure. A 7-DOF surgical robot (KUKA LBR IIWA 7) was used as a test bench for running the simulations. The simulation results show that the ZNNBAS is comparable with BAS, PSO, and GA and efficiently and robustly performed the task commanded maneuvers while enforcing the RCM constraints. [ABSTRACT FROM AUTHOR]

The proportional–integral–derivative (PID) control systems, which have become a standard for technical and industrial applications, are the fundamental building blocks of classical and modern control systems. In this paper, a three-layer feed-forward neural network (NN) model trained to replicate the behavior of a PID controller is employed to stabilize control systems through a NN feedback controller. A novel bio-inspired weights-and-structure-determination (BIWASD) algorithm, which incorporates a meta-heuristic optimization algorithm dubbed beetle antennae search (BAS), is used to train the NN model. More presicely, the BIWASD algorithm identifies the ideal weights and structure of the BIWASD-based NN (BIWASDNN) model utilizing a power sigmoid activation function while handling model fitting and validation. The results of three simulated trials on stabilizing feedback control systems validate and demonstrate the BIWASDNN model’s exceptional learning and prediction capabilities, while achieving similar or better performance than the corresponding PID controller. The BIWASDNN model is compared to five other high-performing NN models, and a MATLAB repository is accessible in public through GitHub to encourage and enhance this work. [ABSTRACT FROM AUTHOR]

This paper presents a model-free real-time kinematic tracking controller for a redundant manipulator. Redundant manipulators are common in industrial applications because of the flexibility and dexterity they get from redundant joints. However, at the same time, the modeling of these systems becomes quite challenging, even for simple tasks like trajectory tracking. Some classical approaches are being used to tackle the issue, including a numerical approximation of the Jacobian and pseudo-inverse of the Jacobian matrix. These approaches have their limitations as they require exact parameters for the modeling of the manipulator; they are not immune to position error accumulation with time and put the manipulator way off the target position. Swarm-based meta-heuristic algorithms have given a new direction to the solution of the redundancy resolution problem. However, they are computationally intensive, formulated in discrete-time, and better suited for offline computation rather than real-time. We proposed a novel continuous-time Zeroing Neural Network with Beetle Antennae Search (ZNNBAS). The ZNNBAS algorithm can solve the quadratic optimization problem for redundancy resolution in real-time. To test its performance, we applied it on 7-DOF redundant manipulator with two trajectories to follow: character “M" and hypotrochoid. The manipulator was able to trace the reference trajectories with minimal tracking errors. [ABSTRACT FROM AUTHOR]

In this brief, we presented a framework for the trajectory optimization of the 5-link Biped Robot using the Beetle Antennae Search (BAS) algorithm. The biped robot is highly non-linear and has complex dynamical modeling. It is challenging to obtain a closed-form solution for the Robot. The robot modeling has two stages, i.e., the optimal trajectory generation and the robust control of a robot. The conventional methodologies treat both problems separately and are computationally expensive. This brief presented an optimization problem that combines the trajectory generation and control problem of a 5-link biped robot. We employed Beetle Antennae Search (BAS) for the problem’s online-solution, an intelligent online optimization approach. BAS is a single-particle searching algorithm, which makes it robust, computationally, and memory-wise efficient. We applied BAS on two non-linear systems, i.e., inverted pendulum and 5-link biped robot, and convincing results are obtained with BAS. [ABSTRACT FROM AUTHOR]

In order to solve the problem of low face recognition accuracy of traditional algorithms and excessive long training time in deep learning methods, a novel lightweight and short training time multiface recognition method is proposed in this paper. Firstly, an extraction model of facial feature vectors is established based on local binary mode and principal component analysis. Secondly, the beetle antennae search algorithm (BAS) is optimized using adaptive factors, and the ABAS algorithm is proposed. This paper uses the ABAS algorithm to optimize the initial threshold of the neural network and proposes the ABASNet method. Thirdly, ABASNet method is combined with the face features extracted by the face feature vector extraction model to be used for multi-face classification tasks. Finally, H-softmax (hierarchical softmax) is used to replace softmax in neural networks. By reducing the amount of calculation in the process of multi-category face classification, the training time of the ABASNet network is reduced. Through ablation experiments and comparative experiments with various methods such as IKDA + PNN algorithm and PCANet algorithm, to verify the accuracy, robustness and short training time of the ABASNet method. Among them, the maximum accuracy of the method in the ORL face dataset, ExtYaleB face dataset and FERET face dataset is 99.35%, 99.54% and 99.18% respectively. In addition, actual test results indicate that the training time and recognition time of the method in this paper are 21 seconds and 0.06 seconds respectively, which has demonstrated the lightweight and real-time performance of the proposed method. At the same time, more test results show that the proposed ABASNet method can also be implemented in embedded devices and other classification tasks.

A nonsingular terminal sliding mode control (NTSMC) scheme based on beetle antennae search (BAS) algorithm is employed in this paper to overcome the internal interference of permanent magnet in-wheel motor (PMIWM) applied in the hybrid electric vehicles (HEVs). Firstly, a nonsingular terminal sliding mode approaching is applied to accelerate approaching speed and improve the response speed of the PMIWM system. Secondly, aiming at the undesired chattering caused by the large switching gain during the sliding mode control, fuzzy rules and fuzzy controller are designed to adjust sliding mode approaching parameter in online to inhibit the chattering phenomenon. Finally, a BAS algorithm based on sensor-cloud system (SCS) is adopted in this paper to obtain the ideal control parameters for the NTSMC control strategy, which can effectively improve the dynamic performance of the PMIWM. Through various simulation and experiments, the results demonstrate the proposed BAS-NTSMC strategy has optimal control performance and robustness, as well as reducing the chattering phenomenon during the control process significantly.

The beetle antennae search algorithm (BAS) is a new heuristic algorithm proposed in 2017. BAS is a simulation optimization algorithm based on beetle antennae, which determine the direction of flight by sensing the smell of food and thereby find the food. The BAS algorithm has been proven to have better optimization speed and precision when searching for solutions to low-dimensional problems. However, when solving high-dimensional problems, the algorithm easily falls into a local optimum. To improve the search ability of BAS, this paper introduces inertia weight, which leads the algorithm to perform a global search in early stages and a local search in later stages, which greatly improves the optimization precision of the algorithm. Economic load distribution (ELD) is a typical optimization problem in power systems. This paper analyses the problem of ELD and its mathematical model and then uses a 3-machine 6-node example to apply the improved BAS algorithm to ELD. Finally, the improved BAS algorithm is compared with PSO and other algorithms on the basis of the optimization results of test functions. The conclusion is drawn that the improved BAS algorithm has advantages in dealing with ELD problems.

Intelligent algorithm acts as one of the most important solutions to path planning problem. In order to solve the problems of poor real-time and low accuracy of the heuristic optimization algorithm in 3D path planning, this paper proposes a novel heuristic intelligent algorithm derived from the Beetle Antennae Search (BAS) algorithm. The algorithm proposed in this paper has the advantages of wide search range and high search accuracy, and can still maintain a low time complexity when multiple mechanisms are introduced. This paper combines the BAS algorithm with three non-trivial mechanisms proposed to solve the problems of low search efficiency and poor convergence accuracy in 3D path planning. The algorithm contains three non-trivial mechanisms, including local fast search, aco initial path generation, and searching information orientation. At first, local fast search mechanism presents a specific bounded area and add fast iterative exploration to speed up the convergence of path finding. Then aco initial path generation mechanism is initialized by Ant Colony Optimization (ACO) as a pruning basis. The initialization of the ACO algorithm can quickly obtain an effective path. Using the exploration trend of this path, the algorithm can quickly obtain a locally optimal path. Thirdly, searching information orientation mechanism is employed for BAS algorithm to guarantee the stability of the path finding, thereby avoiding blind exploration and reducing wasted computing resources. Simulation results show that the algorithm proposed in this paper has higher search accuracy and exploration speed than other intelligent algorithms, and improves the adaptability of the path planning algorithms in different environments. The effectiveness of the proposed algorithm is verified in simulation.

Beetle antennae search (BAS) is a newly developed meta-heuristic algorithm which is effectively used for optimizing objective functions of complex forms or even unknown forms. The common practice for implementing meta-heuristic algorithms including the BAS largely relies on programming in a high-level language and executing the code on a computer platform. However, the high-level implementation of the BAS algorithm hinders it from being used in an embedding system, where real-time operations are normally required. To address this limitation, we present an approach to implementing the BAS algorithm on a field-programmable gate array (FPGA). Specifically, we program the BAS function in the Verilog hardware description language (HDL), which provides a tractable vehicle for implementing the BAS algorithm at the gate level on the FPGA chip. We simulate our Verilog HDL based BAS module with the Modelsim platform. Simulation results validate the feasibility of our proposed Verilog HDL implementation of the BAS. Additionally, we implement the BAS model on the Zynq XC7Z010 platform, with $132.5~\mu $ s latency for model implementation.

This paper proposes a continuous-time enhanced variant of Beetle Antennae Search (BAS), a metaheuristic algorithm that mimics the food searching nature of beetles. Beetles register the smell of the food on their two antennae, and based on the intensity of smell, they move left or right. Likewise, discrete-time BAS computes the value of objective function three times and moves toward the optimal solution. However, the computation of objective function three times in each iteration known as a "virtual particle," makes it computationally expensive, inefficient, and time-consuming, especially while dealing with complex systems, e.g., redundant manipulators. Our proposed, Enhanced Beetle Antennae Search with Zeroing Neural Network (BASZNN) algorithm overcomes this problem by introducing a delay factor in objective function and input. This delay allows BASZNN to compute objective function value once, making it computationally robust and efficient. BASZNN includes the flexible random searching nature of BAS and the parallel processing nature of ZNN, making it computationally fast and less time-consuming, especially for complex problems. As a testbed, we employed BASZNN on two types of problems: Unconstrained (unimodal, multimodal) and Constrained (real-world) and compared the results with state-of-the-art metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

A novel performance seeking control) method based on Beetle Antennae Search algorithm is proposed to improve the real-time performance of performance seeking control. The Beetle Antennae Search imitates the function of antennae of beetle. The Beetle Antennae Search has better real-time performance because of the objective function only calculated twice in Beetle Antennae Search at each iteration. Moreover, the Beetle Antennae Search has global search ability. The performance seeking control simulations based on Beetle Antennae Search, Genetic Algorithm and particle swarm optimization are carried out. The simulations show that the Beetle Antennae Search has much better real-time performance than the conventional probability-based algorithms Genetic Algorithm and particle swarm optimization. The simulations also show that these three probability-based algorithms can get better engine performance, such as more thrust, less specific fuel consumption and less turbine inlet temperature. [ABSTRACT FROM AUTHOR]

Waste glass (WG) is unsustainable due to its nonbiodegradable property. However, its main ingredient is silicon dioxide, which can be utilised as a supplementary cementitious material. Before reusing WG, the flexural strength (FS) and alkali–silica reaction (ASR) expansion of WG concrete are two essential properties that must be investigated. This study produced mortar containing activated glass powder using mechanical, chemical, and mechanical–chemical (combined) approaches. The results showed that mortar containing 30% WG powder using the combined method was optimal for improving the FS and mitigating the ASR expansion. The microstructure analysis was implemented to explore the activation effect on the glass powder and mortar. Moreover, a random forest (RF) model was proposed with hyperparameters tuned by beetle antennae search (BAS), aiming at predicting FS and ASR expansion precisely. A large database was established from the experimental results based on 549 samples prepared for the FS test and 183 samples produced for the expansion test. The BAS-RF model presented high correlation coefficients for both FS (0.9545) and ASR (0.9416) data sets, showing much higher accuracy than multiple linear regression and logistic regression. Finally, a sensitivity analysis was conducted to rank the variables based on importance. Apart from the curing time, the particle granularity and content of WG were demonstrated to be the most sensitive variable for FS and expansion, respectively. [ABSTRACT FROM AUTHOR]

The uncertainty failure of the used part leads the complexity selection of remanufacturing processes. The different remanufacturing process combinations among used parts of used products also make the formulation of tolerance schemes more difficult. It is hard to guarantee the optimality of process-tolerance schemes by traditional serial production modes in general, in which tolerance design is followed by process formulation. In order to generate the optimal remanufacturing scheme of process and tolerance for used products, an optimization method to integrate designs of process-tolerance (IDP-T) based on fault features was presented. In this work, the failure description set of used parts was constructed by combining the attribute characteristics and failure characteristics. Case-based reasoning (CBR) was first utilized to generate the feasible remanufacturing process plans of used parts. Then, based on the feasible process plans, the factors of cost, quality loss, closed-loop accuracy and machining ability of remanufacturing were comprehensively considered to construct the optimization model of IDP-T. The Beetle Antennae Search algorithm (BAS) was used for the optimal alternative selection. Finally, a used gearbox was taken as an example to illustrate the validity and practicality of the proposed method. The results showed that the proposed method was effective in the optimization of IDP-T for remanufacturing. [ABSTRACT FROM AUTHOR]