Artificial Bee Colony Optimization Algorithm Research Articles

Seismic design optimization of space steel frame buildings equipped with triple friction pendulum base isolators

To increase the seismic resilience of structures, it is necessary to utilize seismic isolation to increase structural periods and reduce story drifts. When seismic isolation is implemented, structures are capable of deforming by remaining within safe limits. Thus, more seismically resilient designs are achieved compared to conventional fixed-based structures since lighter structural design weights are attained. The principal aim of this study is to achieve the comparative design optimization of space steel frame buildings equipped with seismic base isolation and to examine the effect of seismic base isolation on optimum structural design weight by comparing the same structures with conventional rigid-based ones. For seismic base isolation, triple friction pendulums are implemented into steel frame buildings. For this aim, four different space steel frame buildings with 4-, 6-, 8-, and 10-story are treated as design examples. A novel design optimization algorithm is encoded, which includes nature-inspired metaheuristic artificial bee colony, crow search, and Archimedes optimization algorithms to achieve design optimizations of triple friction pendulums equipped with seismic-isolated space steel frame buildings. The practice code provisions of load and resistance factor design specification for structural steel buildings have been applied to control the structural design constraints of inter-story drift, top-story drift, strength, displacement, and connections geometry of beams and columns. Eventually, it has been remarked that seismic base isolation significantly reduces the structural optimal design weight of space steel frame buildings.

Multiobjective Optimization of Chaotic Image Encryption Based on ABC Algorithm and DNA Coding

As digital communication and storage continue to expand, the protection of image privacy information becomes increasingly critical. To safeguard sensitive visual information from unauthorized access, this paper proposes a novel image encryption scheme that integrates multiobjective Artificial Bee Colony (ABC) optimization algorithm and DNA coding. Multiple evaluation metrics including correlation relationship, Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), and information entropy are collaboratively optimized by the ABC algorithm. The proposed method begins with the application of the SHA-256 algorithm to generate keys and random sequences using chaotic systems. These sequences are then employed for shuffling, DNA coding, decoding, and diffusion, generating initial encrypted images. Subsequently, the encrypted images serve as individuals within the ABC algorithm to determine optimal parameters of the chaotic systems and the best ciphertext image. Simulation experiments demonstrate that the ciphertext images achieved excellent results in information entropy, pixel correlation coefficient, NPCR, and UACI. The integration of the multiobjective ABC optimization algorithm with DNA coding in our proposed image encryption scheme results in heightened security, as evidenced by superior performance in various metrics.

Design and Implementation of an Optimized PID Controller for Two-Limb Robot Arm Control

With developing technology, robot arms are used in more areas, and this is directly proportional to the work done for its development. Studies on robot arms are generally focused on control. The controllability of robot arms generally provides speed and precision.
 Within the scope of this study, the control optimization of a two-arm robot arm with an optimized proportional integral differential controller (PID) was carried out using a microcontroller. The kinematic operations required to control a two-arm robot arm have been developed by the MATLAB Support Package for Arduino Hardware. The transfer function required for the control system was used for a direct current (DC) brushed motor, using the values given in the motor data sheet. Feedback is provided for the control system thanks to the Hall effect encoder.
 The gripper end of the two-limbed robot arm follows the specified square-shaped reference. In this study, where PID controller was used, controller parameters were obtained with particle swarm, artificial bee colony and chaos game metaheuristic optimization algorithms for square orbit and these parameters were used on the produced robot arm.
 Many methods have been used in the literature to determine PID parameters. In this study, the chaos game metaheuristic optimization algorithm, which has become popular in recent years, was used to determine the parameters of the PID controller.

Land scene classification from remote sensing images using improved artificial bee colony optimization algorithm

The images obtained from remote sensing consist of background complexities and similarities among the objects that act as challenge during the classification of land scenes. Land scenes are utilized in various fields such as agriculture, urbanization, and disaster management, to detect the condition of land surfaces and help to identify the suitability of the land surfaces for planting crops, and building construction. The existing methods help in the classification of land scenes through the images obtained from remote sensing technology, but the background complexities and presence of similar objects act as a barricade against providing better results. To overcome these issues, an improved artificial bee colony optimization algorithm with convolutional neural network (IABC-CNN) model is proposed to achieve better results in classifying the land scenes. The images are collected from aerial image dataset (AID), Northwestern Polytechnical University-Remote Sensing Image Scene 45 (NWPU-RESIS45), and University of California Merced (UCM) datasets. IABC effectively selects the best features from the extracted features using visual geometry group-16 (VGG-16). The selected features from the IABC are provided for the classification process using multiclass-support vector machine (MSVM). Results obtained from the proposed IABC-CNN achieves a better classification accuracy of 96.40% with an error rate 3.6%.

ECG classification efficient modeling with artificial bee colony optimization data augmentation and attention mechanism.

In addressing the key issues of the data imbalance within ECG signals and modeling optimization, we employed the TimeGAN network and a local attention mechanism based on the artificial bee colony optimization algorithm to enhance the performance and accuracy of ECG modeling. Initially, the TimeGAN network was introduced to rectify data imbalance and create a balanced dataset. Furthermore, the artificial bee colony algorithm autonomously searched hyperparameter configurations by minimizing Wasserstein distance. Control experiments revealed that data augmentation significantly boosted classification accuracy to 99.51%, effectively addressing challenges with unbalanced datasets. Moreover, to overcome bottlenecks in the existing network, the introduction of the Efficient network was adopted to enhance the performance of modeling optimized with attention mechanisms. Experimental results demonstrated that this integrated approach achieved an impressive overall accuracy of 99.70% and an average positive prediction rate of 99.44%, successfully addressing challenges in ECG signal identification, classification, and diagnosis.

HETEROGENEOUS SERVER RETRIAL QUEUEING MODEL WITH FEEDBACK AND WORKING VACATION USING ARTIFICIAL BEE COLONY OPTIMIZATION ALGORITHM

This research delves into the dynamics of a retrial queueing system featuring heterogeneous servers with intermittent availability, incorporating feedback and working vacation mechanisms. Employing a matrix geometric approach, this study establishes the steady-state probability distribution for the queue size in this complex heterogeneous service model. Additionally, a range of system performance metrics is developed, alongside the formulation of a cost function to evaluate decision variable optimization within the service system. The Artificial Bee Colony (ABC) optimization algorithm is harnessed to determine service rates that minimize the overall cost. This work includes numerical examples and sensitivity analyses to validate the model's effectiveness. Also, a comparison between the numerical findings and the neuro-fuzzy results has been examined by the adaptive neuro fuzzy interface system (ANFIS).

Optimal sizing of battery energy storage systems and reliability analysis under diverse regulatory frameworks in microgrids

The integration of battery energy storage systems (BESS) with microgrids (MG) is crucial to improve the reliability and flexibility of renewable energy sources (RES) integration. However, the reliability and regulatory policies are critical factors that affect the optimal operation of MGs in the market. This study aims to enhance the reliability of MGs integrated with RES and BESS by evaluating their performance under different regulatory frameworks, namely feed-in tariff (FiT), net metering (NM), and energy storage incentive (ESI). Also, a dynamic FiT (D-FiT) framework is utilized to improve the reliability of the MG. An artificial bee colony optimization algorithm is utilized to optimize the size of BESS for each regulatory policy to minimize the total cost of the MG. Each policy is formulated based on its specific constraints in the problem. Subsequently, the reliability indices of Loss of Load Expectation (LOLE) and Expected Energy not Supplied (EENS) are calculated for each optimized solution. Moreover, we have integrated the dynamic thermal rating (DTR) system into our proposed model, focusing on the safe augmentation of system component ratings. The study finds that the D-FiT and standard FiT frameworks provide the best reliability level, whereas the reliability improvement under the ESI policy is not significant, as most of the MG's demand is supplied by the main grid. Furthermore, the study shows that the improvements in EENS are higher than LOLE, indicating that installing BESS reduces the loss of energy rather than the number of interruption hours. D-FiT framework has a significant positive impact on both reliability indices, unlike the other frameworks that have a greater effect on EENS. Furthermore, we have noticed a substantial improvement in reliability indices when the DTR system is taken into account, as compared to the static thermal rating (STR) system.

Real Coded Binary Artificial Bee Colony (RC-BABC) Based Feature Selection and Relieff Based Feature Extraction Techniques for Heart Disease Prediction

Diagnosing heart disease is really a challenging task for which several intelligent diagnostic systems were developed for enhancing the performance of diagnosing heart disease. However, in these systems, low accuracy of predicting heart disease is still a challenging task. To provide better accuracy in predicting heart risks, a novel feature selection approach is proposed which employs Real Coded Binary Artificial Bee Colony (RC-BABC) optimization algorithm with adaptive size for feature elimination. This method has the advantages of reducing algorithmic computational time, improving prediction accuracy, enhanced data quality, and saves resources in successive data collection phases. Once the features are selected, the important feature extraction phase uses ReliefF based feature extraction method to extract the features from the heart disease data set. The scores of features are computed by estimating a comparison of feature values and class values neighbor samples. The proposed Real Coded Binary Artificial Bee Colony (RC-BABC) optimization algorithm is compared with three well known methods namely an artificial neural network (ANN), K-means clustering approach and Classification and Regression Algorithm (C&RT) with measures like accuracy, precision, recall and F1-score. The proposed method achieved 96.77% of accuracy,98.8% of recall, 97.8% of precision and 98.34% of F1-score.

A NOVEL AND HYBRID WHALE OPTIMIZATION WITH RESTRICTED CROSSOVER AND MUTATION BASED FEATURE SELECTION METHOD FOR ANXIETY AND DEPRESSION.

Anxiety and depression are two leading human psychological disorders. In this work, several swarm intelligence-based metaheuristic techniques have been employed to find an optimal feature set for the diagnosis of these two human psychological disorders. To diagnose depression and anxiety among people, a random dataset comprising 1128 instances and 46 attributes has been considered and examined. The dataset was collected and compiled manually by visiting the number of clinics situated in different cities of Haryana (one of the states of India). Afterwards, nine emerging meta-heuristic techniques (Genetic algorithm, binary Grey Wolf Optimizer, Ant Colony Optimization, Particle Swarm Optimization, Artificial Bee Colony, Firefly Algorithm, Dragonfly Algorithm, Bat Algorithm and Whale Optimization Algorithm) have been employed to find the optimal feature set used to diagnose depression and anxiety among humans. To avoid local optima and to maintain the balance between exploration and exploitation, a new hybrid feature selection technique called Restricted Crossover Mutation based Whale Optimization Algorithm (RCM-WOA) has been designed. The swarm intelligence-based meta-heuristic algorithms have been applied to the datasets. The performance of these algorithms has been evaluated using different performance metrics such as accuracy, sensitivity, specificity, precision, recall, f-measure, error rate, execution time and convergence curve. The rate of accuracy reached utilizing the proposed method RCM-WOA is 91.4%. Depression and Anxiety are two critical psychological disorders that may lead to other chronic and life-threatening human disorders. The proposed algorithm (RCM-WOA) was found to be more suitable compared to the other state of art methods.

Efficient reinforcement learning-based method for plagiarism detection boosted by a population-based algorithm for pretraining weights

Plagiarism detection (PD) in natural language processing involves locating similar words in two distinct sources. The paper introduces a new approach to plagiarism detection utilizing bidirectional encoder representations from transformers (BERT)-generated embedding, an enhanced artificial bee colony (ABC) optimization algorithm for pre-training, and a training process based on reinforcement learning (RL). The BERT model can be incorporated into a subsequent task and meticulously refined to function as a model, enabling it to apprehend a variety of linguistic characteristics. Imbalanced classification is one of the fundamental obstacles to PD. To handle this predicament, we present a novel methodology utilizing RL, in which the problem is framed as a series of sequential decisions in which an agent receives a reward at each level for classifying a received instance. To address the disparity between classes, it is determined that the majority class will receive a lower reward than the minority class. We also focus on the training stage, which often utilizes gradient-based learning techniques like backpropagation (BP), leading to certain drawbacks such as sensitivity to initialization. In our proposed model, we utilize a mutual learning-based ABC (ML-ABC) approach that adjusts the food source with the most beneficial results for the candidate by considering a mutual learning factor that incorporates the initial weight. We evaluated the efficacy of our novel approach by contrasting its results with those of population-based techniques using three standard datasets, namely Stanford Natural Language Inference (SNLI), Microsoft Research Paraphrase Corpus (MSRP), and Semantic Evaluation Database (SemEval2014). Our model attained excellent results that outperformed state-of-the-art models. Optimal values for important parameters, including reward function are identified for the model based on experiments on the study dataset. Ablation studies that exclude the proposed ML-ABC and reinforcement learning from the model confirm the independent positive incremental impact of these components on model performance.

Voice pathology detection using optimized convolutional neural networks and explainable artificial intelligence-based analysis

This article proposes a noninvasive computer-aided assessment approach based on optimized convolutional neural network for healthy and pathological voice detection. Firstly, the input voice samples are first transformed into mel-spectrogram time-frequency visual representations and fed for training the CNN model. The time-frequency image captures inherent speech variations beneficial for healthy and pathological voice sample detection. The weights and biases of trained CNN network are further optimized using artificial bee colony (ABC) optimization algorithm resulting in optimum CNN network employed for testing unseen data. The proposed approach is evaluated using three popular and publicly available datasets: SVD, AVPD and VOICED. Experimental results emphasize that proposed ABC optimized CNN model shows improved accuracy performance by 1.02% compared to conventional CNN network illustrating data-independent discriminative representation ability. Finally, gradient-weighted class activation mapping (Grad-CAM) explainable artificial intelligence (XAI) is utilized to make the decision understandable.

Performance evaluation of evolutionary algorithms for analog integrated circuit design optimisation

An automated sizing approach for analog circuits using evolutionary algorithms is presented in this paper. A targeted search of the search space has been implemented using a particle generation function and a repair-bounds function that has resulted in faster convergence to the optimal solution. The algorithms are tuned and modified to converge to a better optimal solution with less standard deviation for multiple runs compared to standard versions. Modified versions of the artificial bee colony optimisation algorithm, genetic algorithm, grey wolf optimisation algorithm, and particle swarm optimisation algorithm are tested and compared for the optimal sizing of two operational amplifier topologies. An extensive performance evaluation of all the modified algorithms showed that the modifications have resulted in consistent performance with improved convergence for all the algorithms. The implementation of parallel computation in the algorithms has reduced run time. Among the considered algorithms, the modified artificial bee colony optimisation algorithm gave the most optimal solution with consistent results across multiple runs.

Test Case Prioritization Using Swarm Intelligence Algorithm to Improve Fault Detection and Time for Web Application

Prioritizing test cases based on several parameters where important ones are executed first is known as test case prioritization (TCP). Code coverage, functionality, and features are all possible factors of TCP for detecting bugs in software as early as possible. This research was carried out to test and compare the effectiveness Swarm Intelligence algorithms, where Artificial Bee Colony (ABC) and Ant Colony Optimization (ACO) algorithms were implemented to find the fault detected and execution time as these are the curial aspects in software testing to ensure good quality products are produced within the timeline. As web applications are commonly used by a board population, this research was carried out on an Online Shopping application represented as Case Study One and Education Administrative application known as Case Study Two. In recent years, TCP has been implemented widely, but none has implemented on web application which was conducted to fill the gaps and produce a new contribution in this area. The outcome was compared using Average Percentage Fault Detected (APFD) and execution time. For Case Study One, the APFD value was 0.80 and 0.71 while the execution time was 8.64 seconds and 0.69 seconds respectively for ABC and ACO. For Case Study Two, the APFD values were 0.81 and 0.64 while the execution time was 8.83 seconds and 1.22 seconds for ABC and ACO. It was seen that both algorithms performed well in their respective ways. ABC had shown to give a higher value for APFD while ACO had converged faster for execution time.

Sentiment analysis for predicting stress among workers and classification utilizing CNN: Unveiling the mechanism

Stress is one of the main factors that affect the lives of many individuals' lives. Stressful work situations cause significant levels of stress in many employees. Extreme stress can cause significant problems at work and put workers' productivity, health, and safety at risk. Therefore, it is crucial to inform the person about his or her risky lifestyle and forewarn him or her before a serious issue arises. This study offers a novel optimized deep neural network technique, ABC-ABO-CNN, to forecast worker stress. Individuals are significantly impacted by stress, especially in high-stress job contexts. Identifying and measuring employee stress levels is critical to maintaining productivity, health, and safety. Traditional methods like self-assessment questionnaires have limitations, leading to biased results. The suggested approach uses a Convolutional Neural Network (CNN) in conjunction with the African buffalo and Artificial Bee Colony optimization algorithms to overcome these drawbacks. Physiological information such as heart rate, BMI, pressure, glucose level, and skin reaction is used to detect stress. The next step is to categorize stress levels using sentiment analysis. When the suggested mechanism's performance is compared to that of traditional methods, it is shown to have greater accuracy, stress detection rate, and precision values.

Optimization of the free vibrations of doubly-curved sandwich panels using the artificial bee colony optimization algorithm

Optimization of the free vibrations of doubly-curved sandwich panels using the artificial bee colony optimization algorithm

USING ADDITIVE RATIO ASSESSMENT AND ARTIFICIAL BEE COLONY (ARAS-ABC) OPTIMIZATION ALGORITHM DURING DRILLING OF CARBON NANOMATERIAL/ GLASS FIBER-REINFORCED POLYMER LAMINATED COMPOSITES

In manufacturing industries, polymers are widely used due to their exceptional physiochemical and mechanical characteristics. It consists of high strength, low weight, corrosive resistivity, and ease of fabrication. Glass fiber is more cost-effective and easily available than other fibers such as carbon, aramid and kevlar. The most challenging issue for the manufacturer in the laminated polymer is the non-homogeneity and anisotropic behavior. This nature also hinders the machining performance of laminated polymer composites, which are entirely different from metals and their alloys. The supplements of nanomaterials enhanced the physiomechanical properties and the machining efficiency of fiber laminates. This work highlights the machining (drilling) aspect of glass fiber-reinforced polymer composites modified by multiwall carbon nanotube (MWCNT). The effect of drilling factors such as spindle speed ([Formula: see text]), feed rate ([Formula: see text]), and MWCNT weight percent (wt.%) on machining responses such as Thrust force (Th), Torque ([Formula: see text]), and Surface roughness (SR) has been investigated. The drilling operations were conducted using the 5 mm diameter TiAN (Sic coated) according to the response surface methodology (RSM) design. The process constraints were controlled by the hybrid module of additive ratio assessment (ARAS) and the Artificial Bee Colony (ABC) algorithm. The nature-inspired principles of the bee are used to optimize the objective function. The multiple responses were aggregated using the ARAS method, and its objective function is fed into the ABC algorithm. It was remarked that the hybrid ARAS-ABC is more capable than the traditional ARAS, with an overall improvement of 7.33% in assessment values. The scanning electron microscopy (SEM) test confirms the feasibility of the proposed hybrid (ARAS-ABC) module to achieve a favorable machining environment while drilling modified nanocomposites.

Performance Analysis of Marine-Predator-Algorithm-Based Optimum PI Controller with Unified Power Flow Controller for Loss Reduction in Wind–Solar Integrated System

Transmission line losses are a crucial and essential issue in stable power system operation. Numerous methodologies and techniques prevail for minimizing losses. Subsequently, Flexible Alternating Current Transmission Systems (FACTSs) efficiently reduce transmission losses, and the Unified Power Flow Controller (UPFC) is a reactive power compensation controller. The parameter strength of the proportional–integral (PI) controller was calibrated with the Marine Predator Algorithm (MPA), a recent metaheuristic algorithm. An MPA-based optimum PI controller with a UPFC evaluates the optimal location of the UPFC and PI controller parameters to accomplish the desired research objective. The power rating of the UPFC was determined depending on the voltage collapse rating and power loss and an evaluated performance analysis of the MPA–PI-controlled UPFC on a modified IEEE-30 bus transmission network in MATLAB Simulink code. The Newton–Raphson method was used to perform the load flow analysis. Hence, the proposed MPA–PI controller was examined in contrast to preferred heuristic algorithms, the Artificial Bee Colony (ABC) and Moth Flame Optimization algorithms (MFO); the results showed that the MPA–PI controller exhibited better performance with an improved voltage profile and surpasses active power losses with the optimal placement of the UPFC device under different loading conditions. The active power loss, considering a UPFC with the proposed algorithm, reduced from 0.0622 p.u to 0.0301 p.u; consequently, the voltage profile was improved in the respective buses, and the loss percentage reduction during a 100% base load was 68.39%, which was comparatively better than the ABC and MFO algorithms.

Forecasting Pollution Using Numerical Simulation Implementing Artificial Bee Colony Optimization

In this article, an optimization strategy is presented for the numerical solution of Burgers’ equations, which play an important role in estimating and forecasting pollution. The method involves the exponential B-spline basis function as the basis function in the differential quadrature method. Since exponential B-spline involves a parameter, the artificial bee colony optimization algorithm is implemented to find the unknown parameters that result in the minimum error. Among the metaheuristic optimization algorithms, the artificial bee colony (ABC) is one that has received the greatest attention from researchers and has been successfully implemented to solve various problems in engineering and sciences. The proposed work emphasizes the calculation of the parameter of exponential basis functions, a major factor that plays a role in the error calculation using the ABC optimization algorithm. The acquired findings are provided as tables, and the physical behaviour is showcased in the form of figures and tables. The results are in good conformity with the earlier studies.

A Quality-of-Service-Aware Service Composition Method in the Internet of Things Using a Multi-Objective Fuzzy-Based Hybrid Algorithm.

The Internet of Things (IoT) represents a cutting-edge technical domain, encompassing billions of intelligent objects capable of bridging the physical and virtual worlds across various locations. IoT services are responsible for delivering essential functionalities. In this dynamic and interconnected IoT landscape, providing high-quality services is paramount to enhancing user experiences and optimizing system efficiency. Service composition techniques come into play to address user requests in IoT applications, allowing various IoT services to collaborate seamlessly. Considering the resource limitations of IoT devices, they often leverage cloud infrastructures to overcome technological constraints, benefiting from unlimited resources and capabilities. Moreover, the emergence of fog computing has gained prominence, facilitating IoT application processing in edge networks closer to IoT sensors and effectively reducing delays inherent in cloud data centers. In this context, our study proposes a cloud-/fog-based service composition for IoT, introducing a novel fuzzy-based hybrid algorithm. This algorithm ingeniously combines Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) optimization algorithms, taking into account energy consumption and Quality of Service (QoS) factors during the service selection process. By leveraging this fuzzy-based hybrid algorithm, our approach aims to revolutionize service composition in IoT environments by empowering intelligent decision-making capabilities and ensuring optimal user satisfaction. Our experimental results demonstrate the effectiveness of the proposed strategy in successfully fulfilling service composition requests by identifying suitable services. When compared to recently introduced methods, our hybrid approach yields significant benefits. On average, it reduces energy consumption by 17.11%, enhances availability and reliability by 8.27% and 4.52%, respectively, and improves the average cost by 21.56%.

Nature‐inspired artificial bee colony‐based hyperparameter optimization of CNN for anomaly detection in induction motor

AbstractThe Induction Motor (IM) is one of the most frequently used prime movers in most industrial and transportation systems. The motor's stable and safe operation directly influences the secure and reliable operation of such prime movers. Developing an intelligent fault diagnosis system for such motors is very significant. This paper presents an intelligent fault diagnosis method based on the improved functionality of a Convolutional Neural Network (CNN) through its hyperparameter optimization using a nature‐inspired Artificial Bee Colony Optimization (ABCO) algorithm. The proposed diagnostic method introduces and analyses various possible mechanical and electrical faults in the IM. The validation of the proposed method is presented with three different modalities, including vibration, acoustic, and infrared thermography, with their comparative performance analysis. Vibration and acoustic‐based detection are done with time‐frequency scalograms using Constant Q Transform (CQT), which provides enhanced time resolution for lower and higher frequencies. The obtained result indicates that the infrared thermography‐based anomaly detection outperforms the vibration and acoustic‐based diagnosis with 100% classification accuracy. The results signify the potential to diagnose different mechanical and electrical faults in IM with substantial reliability and robustness.