Artificial Bee Colony Algorithm Research Articles

Advanced RUL Estimation for Lithium-Ion Batteries: Integrating Attention-Based LSTM with Mutual Learning-enhanced Artificial Bee Colony Optimization

Advanced RUL Estimation for Lithium-Ion Batteries: Integrating Attention-Based LSTM with Mutual Learning-enhanced Artificial Bee Colony Optimization

Enhanced Performance of Membrane Bioreactor in Sewage Treatment through Integrated Control Strategy

<p>Wastewater includes sewage water, which presents serious environmental problems that necessitate effective treatment techniques. Membrane Bioreactors (MBRs) have emerged as promising solutions, albeit plagued by membrane fouling and computational loading issues. To resolve these issues, this research article presents an innovative control strategy combining both artificial bee colony optimization (ABC) and recurrent neural network (RNN) to regulate the performance of MBR in sewage treatment. Initially, the influent wastewater data was collected and pre-processed using the regression imputation approach. RNN architecture was designed and trained using the pre-processed data to forecast the performance of the MBN system. Further, the ABC algorithm was applied to optimize the function of MBR by adjusting the control variables. The developed model was validated with the publically available wastewater treatment plan dataset and the effectiveness of the developed model was validated by performing intensive performance and comparative assessment. The performance evaluation demonstrates that the proposed methodology attained greater results of 98.59% effluent quality, 98.70% of nutrient removal efficiency, less computational time of 2.87s, and a low membrane fouling index of 1.23%. The comparative analysis illustrates that the presented approach achieved improved performances than the existing methodologies.</p>

Artificial bee colony-based nonrigid demons registration

The artificial bee colony (ABC) algorithm has gained popularity in recent years for its ability to solve optimization problems. The accuracy and resilience of ABC-based image processing techniques have demonstrated encouraging outcomes. The ABC method is an excellent solution for image processing issues since it has the ability to swiftly and effectively explore the search space. The current research intends to address image registration issues by refining the existing image registration strategy using ABC algorithm. The process of nonrigid demons registration is frequently employed in the processing of medical images. The combination of these two techniques is referred to as the ABC-based nonrigid demons registration method. The proposed method has shown superior performance in registration accuracy and efficiency compared to other existing methods. Applications in medical image analysis and computer-assisted diagnosis are highly promising for the ABC-based nonrigid demons registration. Particle swarm optimization (PSO) and frameworks based on genetic algorithms (GA) have been compared with the suggested framework. The observed results showed improved accuracy and faster convergence in ABC-based demons registration.

Research on the optimal scheduling strategy of electric vehicle charging and switching station based on an artificial bee colony algorithm

Research on the optimal scheduling strategy of electric vehicle charging and switching station based on an artificial bee colony algorithm

Research on multi-source separation method of partial discharge based on ABC-JADE

Abstract The presence of multiple PD power sources in the switchgear will produce mixed PD signals, which will bring difficulties to the subsequent signal identification work. To solve this problem, this paper uses the exponential signal model to perform hybrid separation and makes ABC (Artificial Bee Colony Algorithm)-JADE (Joint Approximative Diagonalization of Eigen matrices), i.e., the ABC algorithm is a part of the JADE algorithm, and the ABC algorithm introduces the foraging behavior of bees in the search space, which can help the algorithm better explore the solution space. The JADE algorithm is a differential evolution algorithm, which can adjust the algorithm parameters through an adaptive mechanism. In this paper, the similarity coefficient and amplitude relative error index are used to evaluate the signal separation performance, and the experimental results show that the proposed JADE-ABC algorithm can effectively separate mixed signals. The amplitude relative error and phase accuracy of the separated signal and the source signal are within a certain range, and have a certain degree of robustness.

Retraction notice: Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks

Retraction notice: Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks

Advanced UAV Material Transportation and Precision Delivery Utilizing the Whale-Swarm Hybrid Algorithm (WSHA) and APCR-YOLOv8 Model

This paper proposes an effective material delivery algorithm to address the challenges associated with Unmanned Aerial Vehicle (UAV) material transportation and delivery, which include complex route planning, low detection precision, and hardware limitations. This novel approach integrates the Whale-Swarm Hybrid Algorithm (WSHA) with the APCR-YOLOv8 model to enhance efficiency and accuracy. For path planning, the placement paths are transformed into a Generalized Traveling Salesman Problem (GTSP) to be able to compute solutions. The Whale Optimization Algorithm (WOA) is improved for balanced global and local searches, combined with an Artificial Bee Colony (ABC) Algorithm and adaptive weight adjustment to quicken convergence and reduce path costs. For precise placement, the YOLOv8 model is first enhanced by adding the SimAM attention mechanism to the C2f module in the detection head, focusing on target features. Secondly, GhoHGNetv2 using GhostConv is the backbone of YOLOv8 to ensure accuracy while reducing model Params and FLOPs. Finally, a Lightweight Shared Convolutional Detection Head (LSCDHead) further reduces Params and FLOPs through shared convolution. Experimental results show that WSHA reduces path costs by 9.69% and narrows the gap between the best and worst paths by about 34.39%, compared to the Improved Whale Optimization Algorithm (IWOA). APCR-YOLOv8 reduces Params and FLOPs by 44.33% and 34.57%, respectively, with mAP@0.5 increasing from 88.5 to 92.4 and FPS reaching 151.3. This approach can satisfy the requirements for real-time responsiveness while effectively preventing missed, false, and duplicate detections during the inspection of emergency airdrop stations. In conclusion, combining bionic optimization algorithms and image processing significantly enhances the efficiency and precision of material placement in emergency management.

Surrogate modeling for aerodynamic static instability of central-slotted box decks using machine learning approaches

Studies on aerodynamic controls of central-slotted box decks primarily focused on mitigating vortex-induced vibrations (VIV), as this type of deck typically performs well against flutter instability. However, as the span length increases, the critical wind speed of aerodynamic static instability ( U cr) might be lower than flutter critical wind speed. Thus, U cr will determine the overall aerodynamic performance of such bridges. Investigating this instability through wind tunnel testing methods and numerical simulation can be expensive and time-consuming. In this paper, surrogate models using machine learning approaches, specifically artificial neural network (ANN) and extreme gradient boosting (XGBoost), were developed and optimized for fast and reliable prediction for U cr based on wind tunnel tests and simulation data. The results demonstrated that the built surrogate models can predict U cr accurately. The parametric study results showed that the height ratio of wind fairing apex ( a/b), wind angle of attack ( α), and length of the main span ( L) have the most influence on the U cr compared with other parameters. Finally, based on the developed ANN surrogate model and the artificial bee colony (ABC) optimization algorithm, an optimized section was proposed to enhance the section’s performance against aerodynamic static instability.

Sediment load forecasting from a biomimetic optimization perspective: Firefly and Artificial Bee Colony algorithms empowered neural network modeling in Çoruh River

The service life of downstream dams, river hydraulics, waterworks construction, and reservoir management is significantly affected by the amount of sediment load (SL). This study combined models such as the artificial neural network (ANN) algorithm with the Firefly algorithm (FA) and Artificial Bee Colony (ABC) optimization techniques for the estimation of monthly SL values in the Çoruh River in Northeastern Turkey. The estimation of SL values was achieved using inputs of previous SL and streamflow values provided to the models. Various statistical metrics were used to evaluate the accuracy of the established hybrid and stand-alone models. The hybrid model is a novel approach for estimating sediment load based on various input variables. The results of the analysis determined that the ABC-ANN hybrid approach outperformed others in SL estimation. In this study, two combinations, M1 and M2, with different input variables, were used to assess the model's accuracy, and the best-performing model for monthly SL estimation was identified. Two scenarios, Q(t) and Q(t − 1), were coupled with the ABC-ANN algorithm, resulting in a highly effective hybrid approach with the best accuracy results (R2 = 0.90, RMSE = 1406.730, MAE = 769.545, MAPE = 5.861, MBE = − 251.090, Bias Factor = − 4.457, and KGE = 0.737) compared to other models. Furthermore, the utilization of FA and ABC optimization techniques facilitated the optimization of the ANN model parameters. The significant results demonstrated that the optimization and hybrid techniques provided the most effective outcomes in forecasting SL for both combination scenarios. As a result, the prediction outputs achieved higher accuracy than those of a stand-alone ANN model. The findings of this study can provide essential resources to various managers and policymakers for the management of water resources.

Prediction of Surface Subsidence in Mining Areas Based on Ascending-Descending Orbits Small Baseline Subset InSAR and Neural Network Optimization Models.

Surface subsidence hazards in mining areas are common geological disasters involving issues such as vegetation degradation and ground collapse during the mining process, which also raise safety concerns. To address the accuracy issues of traditional prediction models and study methods for predicting subsidence in open-pit mining areas, this study first employed 91 scenes of Sentinel-1A ascending and descending orbits images to monitor long-term deformations of a phosphate mine in Anning City, Yunnan Province, southwestern China. It obtained annual average subsidence rates and cumulative surface deformation values for the study area. Subsequently, a two-dimensional deformation decomposition was conducted using a time-series registration interpolation method to determine the distribution of vertical and east-west deformations. Finally, three prediction models were employed: Back Propagation Neural Network (BPNN), BPNN optimized by Genetic Algorithm (GA-BP), and BPNN optimized by Artificial Bee Colony Algorithm (ABC-BP). These models were used to forecast six selected time series points. The results indicate that the BPNN model had Mean Absolute Errors (MAE) and Root Mean Squared Errors (RMSE) within 7.6 mm, while the GA-BP model errors were within 3.5 mm, and the ABC-BP model errors were within 3.7 mm. Both optimized models demonstrated significantly improved accuracy and good predictive capabilities.

Multi-objective artificial bee colony algorithm for reducing carbon emission and maximizing profit in a circular supply chain network

Multi-objective artificial bee colony algorithm for reducing carbon emission and maximizing profit in a circular supply chain network

Artificial bee colony algorithm for operating room scheduling problem with dedicated/flexible resources and cooperative operations

Artificial bee colony algorithm for operating room scheduling problem with dedicated/flexible resources and cooperative operations

Bee-inspired insights: Unleashing the potential of artificial bee colony optimized hybrid neural networks for enhanced groundwater level time series prediction.

Analysis of the change in groundwater used as a drinking and irrigation water source is of critical importance in terms of monitoring aquifers, planning water resources, energy production, combating climate change, and agricultural production. Therefore, it is necessary to model groundwater level (GWL) fluctuations to monitor and predict groundwater storage. Artificial intelligence-based models in water resource management have become prevalent due to their proven success in hydrological studies. This study proposed a hybrid model that combines the artificial neural network (ANN) and the artificial bee colony optimization (ABC) algorithm, along with the ensemble empirical mode decomposition (EEMD) and the local mean decomposition (LMD) techniques, to model groundwater levels in Erzurum province, Türkiye. GWL estimation results were evaluated with mean square error (MSE), coefficient of determination (R2), and residual sum of squares (RSS) and visually with violin, scatter, and time series plot. The study results indicated that the EEMD-ABC-ANN hybrid model was superior to other models in estimating GWL, with R2 values ranging from 0.91 to 0.99 and MSE values ranging from 0.004 to 0.07. It has also been revealed that promising GWL predictions can be made with previous GWL data.

Optimizing sustainable reverse logistic networks: a case study of medical waste using the genetic artificial bee colony algorithm

Optimizing sustainable reverse logistic networks: a case study of medical waste using the genetic artificial bee colony algorithm

An artificial bee colony algorithm for the minimum edge-dilation K-center problem

An artificial bee colony algorithm for the minimum edge-dilation K-center problem

Application of artificial bee colony algorithm based on homogenization mapping and collaborative acquisition control in network communication security.

In order to optimize the spectrum allocation strategy of existing wireless communication networks and improve information transmission efficiency and data transmission security, this study uses the independent correlation characteristics of chaotic time series to simulate the collection and control strategy of bees, and proposes an artificial bee colony algorithm based on uniform mapping and collaborative collection control. Furthermore, it proposes an artificial bee colony algorithm based on uniform mapping and collaborative collection and control. The method begins by establishing a composite system of uniformly distributed Chebyshev maps. In the neighborhood intervals where the nectar sources are firmly connected and relatively independent, the algorithm then conducts a chaotic traversal search. The research results demonstrated the great performance of the suggested algorithm in each test function as well as the positive effects of the optimization search. The network throughput rate was over 300 kbps, the quantity of security service eavesdropping was below 0.1, and the spectrum utilization rate of the algorithm-based allocation method could be enhanced to 0.8 at the most. Overall, the performance of the proposed algorithm outperformed the comparison algorithm, with high optimization accuracy and a significant amount of optimization. This is favorable for the efficient use of spectrum resources and the secure transmission of communication data, and it encourages the development of spectrum allocation technology in wireless communication networks.

A novel data-driven IBA-ELM model for SOH/SOC estimation of lithium-ion batteries

Lithium-ion batteries (Libs) have become the best power source for electric vehicles due to their high energy density and long cycle life. The state estimation is directly related to the safe and efficient use of Libs. In our previous research, a Bat Algorithm-Extreme Learning Machine (BA-ELM) model was constructed and promising results were achieved for state of health (SOH) estimation. To increase the engineering application value of BA-ELM for SOH/state of charge (SOC) estimation, optimization mechanism of improving the global exploration and local exploitation capabilities of the model was studied. First, three improved methods including a conversion mechanism of Artificial Bee Colony algorithm employing bees and scout bees, a mechanism for adaptive adjustment of the search frequency of bats according to the Euclidean distance difference, and an adaptive inertia weight coefficient method were proposed to construct a novel Improved Bat Algorithm (IBA)-ELM model. Second, a battery life degradation test platform was built to obtain the degradation dataset for Libs. The variation rules of nine battery variables and cycles were revealed. Four high-quality SOH-related features were extracted. Thus, the SOH estimation performance of six models was evaluated. Third, a Libs charge–discharge dataset under driving conditions was collected and processed. Four models were trained based on 72,048 samples from four conditions. Further, SOC estimation tests were conducted on 35,071 samples from completely different conditions. The results show that the root mean square error (RMSE) and mean absolute error (MAE) of SOH estimation by IBA-ELM decreased by 21.93 % and 23.79 %, respectively, compared to those by BA-ELM. The RMSE and MAE of SOC estimation decreased by more than 10 %. Error, radar, and violin plots demonstrate that the IBA-ELM could accurately estimate SOH/SOC in larger scale datasets, simultaneously exhibiting better generalization performance.

Optimization of the Colony Size Parameter in the Ant Algorithm for Solving the Routing Problem in Communication Networks

With the increasing amount of information and complexity of communication networks, the issue of routing remains relevant. The routing problem is a classic problem of building a logical structure of a communication network. To solve this problem, many methods are used, such as: genetic algorithms, ant algorithm, swarm intelligence, artificial bee colony algorithm, firefly algorithm, bacterial food search optimization algorithm, etc. Despite the fact that the effectiveness of using the ant algorithm is widely known and described, however, there is no single approach to finding the optimal use case for the algorithm. There is a need to determine the optimal size of the colony when solving the routing problem. The article describes the results of the simulation process of modeling the behavior of ant colonies of different sizes to find the optimal route through the AnyLogic environment. It is determined that by means of simulation it is possible to reproduce the use of the ant algorithm to solve the routing problem in communication networks. To find the optimal ratio of the colony size to the complexity of the task, you can use the grid search methodology. The results clearly demonstrate that with increasing complexity of the task (the number of communication nodes), the same number of ants cope worse with the task. The problem associated with the need to accurately determine the optimal size of a colony is of great practical importance, because an increase in the size of a colony has generally decreasing utility. It was found that for many, an increase in the number of ants from 100 to 500 had a relatively greater effect than an increase in the size of the colony from 1000 to 10000. It is proved that by means of simulation it is possible to reproduce the use of the ant algorithm to solve the routing problem in communication networks.

Probing the Structural and Electronic Properties of the Anionic and Neutral Tellurium-Doped Boron Clusters TeBnq (n = 3-16, q = 0, -1).

In this study, we employ density functional theory along with the artificial bee colony algorithm for cluster global optimization to explore the low-lying structures of TeBnq (n = 3-16, q = 0, -1). The primary focus is on reporting the structural properties of these clusters. The results reveal a consistent doping pattern of the tellurium atom onto the in-plane edges of planar or quasi-planar boron clusters in the most energetically stable isomers. Additionally, we simulate the photoelectron spectra of the cluster anions. Through relative stability analysis, we identify three clusters with magic numbers -TeB7-, TeB10, and TeB12. The aromaticity of these clusters is elucidated using adaptive natural density partitioning (AdNDP) and magnetic properties analysis. Notably, TeB7- exhibits a perfect σ-π doubly aromatic structure, while TeB12 demonstrates strong island aromaticity. These findings significantly contribute to our understanding of the structural and electronic properties of these clusters.

Optimization of fractional-order PI-PID controllers for MIMO systems using artificial bee colony algorithm

Optimization of fractional-order PI-PID controllers for MIMO systems using artificial bee colony algorithm