Artificial Bee Colony Algorithm Research Articles

Optimized Watermarking Using Artificial Bee Colony Algorithm and 6-D Hyperchaotic Map Based Image Cryptographic Scheme

Optimized Watermarking Using Artificial Bee Colony Algorithm and 6-D Hyperchaotic Map Based Image Cryptographic Scheme

Design of Routing Algorithm for Communication of Power Wireless Sensor Networks Based on Improved Harmony Search

In this paper, a routing algorithm for the communication of power wireless sensor networks based on improved harmony search is proposed to reduce the communication routing delay and energy consumption of power wireless sensor networks. Firstly, according to the composition of communication routing delay and energy consumption of power wireless sensor networks, the delay model and energy consumption model are constructed respectively, and the objective function is constructed by assigning different weights. Then, artificial bee colony algorithm and Levy flight mechanism improved harmony search algorithm are adopted for solving the objective function. Experiment results reveal that the algorithm can meet different users’ different demands on delay and energy consumption. If the user needs low delay, set the low delay weight to 1 and the power consumption weight to 0, then the average delay is 7s, and the average power consumption is 3203J. When user demand is low energy consumption, set the delay weight to 0 and the energy consumption weight to 1, so that the average energy consumption is 2667J and the average delay is 15s, which can meet the needs of users.

Application of sparrow search swarm intelligence optimization algorithm in identifying the critical surface in slope-stability

The use of nature-inspired meta-heuristics to tackle complex optimization problems is steadily gaining popularity within a rapidly evolving world. Swarm intelligence (SI) optimization motivated by behavior of community-based organisms of flocks of birds, schools of fish, colonies of ants and bees performs the search through agents whose trajectories are primarily adjusted stochastically and sporadically deterministically, by golden rules drawn from Mother Nature. Each entity within the swarm is swayed by its individual ‘best’ and group’s ‘best’ position while moving randomly to converge to optimal through competition and cooperation. The sparrow search algorithm (SSA), developed by Xuea and Shen (Xue and Shen in Syst Sci Cont Eng 8:22–34, 2020) is a very recent SI approach that adopts the sparrow producer–scrounger model metaphorically for designing optimum searching strategies, inspired by the foraging, anti-predation behavior, and the overall group wisdom of sparrows. SSA has been experimented on some hard benchmark test functions to test its effectiveness and thereafter, applied in slope-stability problems in searching the critical failure surface. The objective function to be optimized is the factor of safety against failure given by Bishop's (Bishop in Geotechnique 5:7–17, 1955) simplified method. Results show SSA is a strong contender to bio-inspired methods like genetic algorithms, big-bang big-crunch, simulated annealing, and artificial bee colony algorithms. The study illustrates the flexibility, efficiency, and robustness of the methodology in function optimization.

Optimal trajectories for UAV three-dimensional path planning using a hybrid ABC-RRT* algorithm

Path planning is critical to the effective operation of unmanned aerial vehicles (UAVs) in complex environments. It is crucial to quickly determine the best path for UAV navigation. In this paper, a novel approach for unmanned aerial vehicle (UAV) path planning is presented by combining the robust artificial bee colony (ABC) algorithm with the flexible rapidly exploring random tree star (RRT*) algorithm. The main objective of this approach is to ensure effective obstacle avoidance. The proposed hybrid algorithm, ABC-RRT*, is compared with several current approaches, namely Improved ABC (IABC), ABC, Particle Swarm Optimization (PSO), ABC-PSO and RRT. ABC-RRT* combines the exploration capabilities of ABC with the route optimization capabilities of RRT* to efficiently determine the best trajectories for UAVs by balancing exploration and exploitation. ABC integration increases the effectiveness of exploration, while RRT* excels at identifying shorter paths and adapting to different conditions. ABC-RRT* has been shown to outperform other methods in terms of path length and flexibility when traversing complicated terrain with many obstacles. This was proven by extensive simulations in various situations. The results demonstrate the effectiveness, fast convergence and robustness of ABC-RRT* and make it a viable solution for obstacle avoidance of UAVs in practice. The fusion of ABC and RRT* for obstacle avoidance shows great potential for UAV route planning and offers remarkable advantages compared to conventional approaches.

Open shop scheduling with group and transportation operations by learning-driven hyper-heuristic algorithms

Open shop scheduling problems (OSSPs) are complex scheduling problems, which have been extensively studied in the literature. Group and transportation activities are two important aspects of OSSPs that still need attention. This work considers an OSSP with group and transportation operations to minimize maximum completion time by solving three key sub-problems: job assignment among groups, job sequence in groups and group sequence on machines. Firstly, an integer programming model is formulized to define the problem. Secondly, a learning-driven hyper-heuristic algorithm is developed by incorporating a Q-learning method and four meta-heuristics, i.e., genetic algorithm, artificial bee colony optimization, variable neighborhood search method and Jaya algorithm. The Q-learning method is devised to select the most promising meta-heuristic for performing at each iteration. Three neighborhood structures are designed by integrating critical machines and critical paths. Finally, the developed model is verified by an exact solver CPLEX, and the comparison results exhibit that CPLEX is effective for instances with ten jobs. For the instances with more than ten jobs, the developed algorithm wins CPLEX in terms of computation accuracy and efficiency, signifying its excellent performance in finding better solutions. Furthermore, four meta-heuristics mentioned above and three state-of-the-art meta-heuristics are employed for comparisons in solving a set of benchmark test instances. The results confirm that the formulated model and algorithm have stronger competitiveness in handling the considered problems.

Power quality improvement of grid‐connected solar power plant systems using a novel fractional order proportional integral derivative controller technique

AbstractRecently, there has been a push to integrate renewable energy system (RES) into grid‐connected load system in enhancing reliability and reducing losses. However, integrating these systems introduces power quality (PQ) issues, especially with non‐linear, critical, and imbalanced loads. Addressing this, a hybrid mantis search‐reptile search algorithm (HMS‐RSA) combined with a unified power quality conditioner (UPQC) to mitigate PQ problems related to current and voltages in RES systems. In other words, the UPQC, enhanced by fractional order proportional integral derivative controller parameters tuned using the proposed HMS‐RSA assists in enhancing the power quality. The approach has been validated by connecting a non‐linear load to the system, which typically creates PQ issues. The proposed method is implemented in MATLAB/Simulink and their performance is analysed in three scenarios, such as sag, swell, and disturbance, and the total harmonic distortion is evaluated to quantify improvements in PQ. Finally, the proposed method is compared with existing approaches, such as ant colony optimization (ACO), artificial bee colony optimization (ABC), and bacterial foraging optimization (BFO). The method also outperforms ACO, ABC, and BFO in terms of convergence speed and effectiveness in mitigating PQ issues.

Adaptive Position Control of Electrohydraulic Servo Systems with Parameter Uncertainty using Artificial Bee Colony Optimization Algorithm

In this paper, we present a robust adaptive backstepping-based controller for precise positioning of the spool valve in an Electro-Hydraulic Servo System (EHSS) under conditions of parameter fluctuations. Classical control strategies, such as PID and linear controllers, often struggle with the nonlinearities and parameter uncertainties inherent in EHSS, leading to poor tracking performance and instability. To overcome these limitations, we employ the Artificial Bee Colony (ABC) algorithm to optimize the controller parameters, minimizing both the tracking error and control signal. The proposed controller ensures uniform ultimate boundedness of the error and control signal by utilizing a Lyapunov-based stability criterion, which guarantees that errors do not exceed a predefined bound despite uncertainties and disturbances. Simulation results validate the robustness and effectiveness of the control scheme, even in the presence of parameter variations. Additionally, a comparative analysis with sliding mode control highlights the superior performance of the proposed method, particularly in providing smoother control signals and reducing chattering while ensuring stability.

Solving large-scale instances of the urban transit routing problem with a parallel artificial bee colony-hill climbing optimization algorithm

Swarm Intelligence simulates the collective behavior of decentralized and self-organized swarms. One of the main relevant methods is the Artificial Bee Colony (ABC) algorithm which simulates the foraging behavior of bee swarms in a colony to produce efficient solutions to various problems. The Urban Transit Routing Problem (UTRP) involves finding an efficient set of routes in a transit network to satisfy travel demand subject to operational and budget constraints. It is a complex, NP-Hard problem, in which otherwise correct solutions can be rejected because of impracticability. In this study, a hybrid algorithm consisting of a parallel ABC and Hill Climbing was used to find quality solutions to the UTRP. Thorough comparative results on Mandl’s well-known instance and Mumford’s large-scale instances demonstrate that the proposed algorithm outperforms existing techniques, achieving high levels of direct trip coverage in small computational times. Remarkably, when applied to the most extensive benchmark comprising over 6 million trips and 60 bus routes, the proposed algorithm demonstrates an impressive 11 % enhancement in direct coverage over the previously best-reported results, allowing the design of real-world bus networks in under 3 hours.

Research on the prediction of blasting fragmentation in open-pit coal mines based on KPCA-BAS-BP

The blasting block size of open-pit mines is influenced by many factors, and the influencing factors have a very complex nonlinear relationship. Traditional empirical formulas and a single neural network model cannot meet the requirements of modern blasting safety. To improve the prediction accuracy of blasting block size, the measured data of Beskuduk open-pit coal mine is used as training and testing samples. Seven factors including rock tensile strength, rock compressive strength, and blast hole spacing are selected as input variables of the prediction model. The average size of blasting fragmentation X50 is used as the output variable of the prediction model. The kernel principal component analysis (KPCA) is adopted to reduce the dimensionality of the input variables. The beetle antennae search algorithm (BAS) is selected to optimize the parameters of the initial weights and thresholds of the back propagation (BP) neural network. Finally, prediction model of blasting fragmentation in open-pit coal mine based on KPCA-BAS-BP is established. The results show that the average relative error of the model is 1.77%, and the root mean square error is 1.52%. Compared with the unoptimized BP neural network and the BP neural network optimized by the artificial bee colony algorithm (ABC) model, this model has higher prediction accuracy and is more suitable for predicting the blasting block size of open-pit coal mines, it provides a new method for predicting the fragmentation of blasting under the influence of multiple factors, filling the gap in related theoretical research, and has certain practical application value.

RNA-Seq analysis for breast cancer detection: a study on paired tissue samples using hybrid optimization and deep learning techniques

ProblemBreast cancer is a leading global health issue, contributing to high mortality rates among women. The challenge of early detection is exacerbated by the high dimensionality and complexity of gene expression data, which complicates the classification process.AimThis study aims to develop an advanced deep learning model that can accurately detect breast cancer using RNA-Seq gene expression data, while effectively addressing the challenges posed by the data’s high dimensionality and complexity.MethodsWe introduce a novel hybrid gene selection approach that combines the Harris Hawk Optimization (HHO) and Whale Optimization (WO) algorithms with deep learning to improve feature selection and classification accuracy. The model’s performance was compared to five conventional optimization algorithms integrated with deep learning: Genetic Algorithm (GA), Artificial Bee Colony (ABC), Cuckoo Search (CS), and Particle Swarm Optimization (PSO). RNA-Seq data was collected from 66 paired samples of normal and cancerous tissues from breast cancer patients at the Jawaharlal Nehru Cancer Hospital & Research Centre, Bhopal, India. Sequencing was performed by Biokart Genomics Lab, Bengaluru, India.ResultsThe proposed model achieved a mean classification accuracy of 99.0%, consistently outperforming the GA, ABC, CS, and PSO methods. The dataset comprised 55 female breast cancer patients, including both early and advanced stages, along with age-matched healthy controls.ConclusionOur findings demonstrate that the hybrid gene selection approach using HHO and WO, combined with deep learning, is a powerful and accurate tool for breast cancer detection. This approach shows promise for early detection and could facilitate personalized treatment strategies, ultimately improving patient outcomes.

IMOABC: An efficient multi-objective filter–wrapper hybrid approach for high-dimensional feature selection

With the development of data science, the challenge of high-dimensional data has become increasingly prevalent. High-dimensional data contains a significant amount of redundant information, which can adversely affect the performance and effectiveness of machine learning algorithms. Therefore, it is necessary to select the most relevant features from the raw data and perform feature selection on high-dimensional data. In this paper, a novel filter–wrapper feature selection method based on an improved multi-objective artificial bee colony algorithm (IMOABC) is proposed to address the feature selection problem in high-dimensional data. This method simultaneously considers three objectives: feature error rate, feature subset ratio, and distance, effectively improving the efficiency of obtaining the optimal feature subset on high-dimensional data. Additionally, a novel Fisher Score-based initialization strategy is introduced, significantly enhancing the quality of solutions. Furthermore, a new dynamic adaptive strategy is designed, effectively improving the algorithm’s convergence speed and enhancing its global search capability. Comparative experimental results on microarray cancer datasets demonstrate that the proposed method significantly improves classification accuracy and effectively reduces the size of the feature subset when compared to various traditional and state-of-the-art multi-objective feature selection algorithms. IMOABC improves the classification accuracy by 2.27% on average compared to various multi-objective feature selection methods, while reducing the number of selected features by 88.76% on average. Meanwhile, IMOABC shows an average improvement of 4.24% in classification accuracy across all datasets, with an average reduction of 76.73% in the number of selected features compared to various traditional methods.

Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network

Cyberattacks are increasingly becoming more complex, which makes intrusion detection extremely difficult. Several intrusion detection approaches have been developed in the literature and utilized to tackle computer security intrusions. Implementing machine learning and deep learning models for network intrusion detection has been a topic of active research in cybersecurity. In this study, artificial neural networks (ANNs), a type of machine learning algorithm, are employed to determine optimal network weight sets during the training phase. Conventional training algorithms, such as back-propagation, may encounter challenges in optimization due to being entrapped within local minima during the iterative optimization process; global search strategies can be slow at locating global minima, and they may suffer from a low detection rate. In the ANN training, the Artificial Bee Colony (ABC) algorithm enables the avoidance of local minimum solutions by conducting a high-performance search in the solution space but it needs some modifications. To address these challenges, this work suggests a Deep Autoencoder (DAE)-based, vectorized, and parallelized ABC algorithm for training feed-forward artificial neural networks, which is tested on the UNSW-NB15 and NF-UNSW-NB15-v2 datasets. Our experimental results demonstrate that the proposed DAE-based parallel ABC-ANN outperforms existing metaheuristics, showing notable improvements in network intrusion detection. The experimental results reveal a notable improvement in network intrusion detection through this proposed approach, exhibiting an increase in detection rate (DR) by 0.76 to 0.81 and a reduction in false alarm rate (FAR) by 0.016 to 0.005 compared to the ANN-BP algorithm on the UNSW-NB15 dataset. Furthermore, there is a reduction in FAR by 0.006 to 0.0003 compared to the ANN-BP algorithm on the NF-UNSW-NB15-v2 dataset. These findings underscore the effectiveness of our proposed approach in enhancing network security against network intrusions.

Research on surface integrity of titanium alloy in V-groove micro-textured ball-end milling cutter

In order to realize chip guiding control in titanium alloy machining, reduce the negative influence of unstable chip flow direction on the milling environment, and improve the performance and reliability of parts, this article adopts the V-groove micro-texture carbide tool as the research object and takes the laser preparation of the V-groove micro-texture as a breakthrough point, analyzes the influence of laser parameters on the aspect ratio of V-groove micro-texture, and optimizes the laser parameters. The titanium alloy milling test platform was built to analyze the influence of the micro-texture parameters of the V-groove on the surface integrity of the workpiece, reveal the action mechanism, and optimize the relevant parameters based on the artificial bee colony algorithm. The results show that laser power has the largest influence on the aspect ratio of the groove. The power determines the melt content in the groove and influences the width and depth of the micro-texture. The optimized laser combination parameters are: laser power 42.5 W, scanning speed 10 mm/s scanning times nine times, scanning frequency 30 kHz, and spot diameter 30 μm. The V-groove micro-texture on the tool surface can change the chip flow direction, reduce the contact area of the chip, accommodate the cutting particles, and improve heat conduction, reducing cutting resistance and friction, thereby optimizing cutting control distance, thereby improving the surface integrity of the workpiece becomes. The optimized V-groove micro-texture parameters are as follows: V-groove angle X = 46.2°, V-groove height H = 175 μm, radial spacing L = 259.4 μm, circumferential angle J = 2.1°, and distance from the edge K = 120 μm.

Prediction of permeability characteristics of mine slurries and sediments using finite-strain framework by optimisation algorithms

Determining the permeability characteristics of mine tailing slurries through laboratory finite-strain consolidation tests is costly due to the extensive testing required. An alternative approach involves back-predicting hydraulic conductivities from settling column test data. This study employed settlement and excess pore pressure data as inputs in optimising the permeability parameters using the finite-strain consolidation (FSC) framework. The proposed approach used the finite difference method to solve the governing equation for FSC in the forward analysis. Two novel variants of the Artificial Bee Colony (ABC) algorithm, namely the Modified Artificial Bee Colony (MABC) and the Hybrid Artificial Bee Colony (HABC) algorithms, were used to back-predict the hydraulic conductivity function parameters from the FSC test data. These models predicted the permeability characteristics using the temporal variation of the settlements for six slurries and excess pore pressure dissipation data for four slurries. The performance of the proposed algorithms along with Hybrid Particle Swarm Optimisation (HPSO) was compared with the conventional optimisation algorithms, viz., PSO, ABC, and Quantum PSO (QPSO). The HABC and HPSO exhibited remarkable stability, consistently converging to identical solution sets across multiple iterations, thereby outperforming other algorithms in this study. Despite its higher time complexity by HABC relative to the other evaluated methods, this complexity is warranted for enhanced robustness. The current study is helpful in brining practical and reliable methodologies for estimating the hydraulic conductivity function from field-scale conductivity (FSC) data, providing critical insights for the safe and efficient management of slurry wastes.

An innovative attention infused- BiRecurrenTwin network assisted hybrid segmentation technique for accurate heart disease prediction

Heart disease is a noteworthy global health concern, profoundly affecting public health and individual well-being. Early prediction and diagnosis are crucial for reducing its negative impact. This work focuses on developing an effectual heart disease detection employing advanced optimization approaches and data processing. The proposed system incorporates a hybrid segmentation algorithm, Hybrid K-means-Fuzzy-C-means (HKMFCM) clustering, which assigns membership degrees to data points, enabling soft clustering and accommodating data uncertainty. Additionally, the Artificial Bee Colony (ABC) Optimization method is applied for feature selection, mimicking the foraging behavior of honeybees to identify the most discriminative attributes from the dataset. This optimization algorithm iteratively explores the feature space to select features that enhance the model's predictive accuracy. Furthermore, a novel classification architecture, termed the Attention-infused BiRecurrenTwin Network, is introduced to accurately predict heart disease based on segmented and extracted patient data profiles. This classifier leverages both Bidirectional Gated Recurrent Unit (BiGRU) and Bidirectional Long Short-Term Memory (BiLSTM) networks, with their bidirectional nature capturing both past and future contexts, thus increasing the classifier's capability to detect subtle temporal patterns in patient data. In addition, the proposed system addresses traditional approaches limitations through its advanced components like HKMFCM (effectively manages data uncertainty by enabling soft clustering), ABC Optimization technique (enabling an iterative, global search for the most discriminative attributes) Attention-infused BiRecurrenTwin Network (surpasses conventional classifiers by capturing both past and future temporal patterns in patient data). The simulation outcomes demonstrate that the developed system attains improved performance, with accuracy, ROC, F-measure, precision, recall, sensitivity, and specificity values of 96.09%, 97%, 95%, 96.6%, 94.3%, 95.14%, and 97.05%, respectively which is higher than the baseline models with an average of 7.75 % increase in accuracy. These results indicate that the developed predictive models show promise in accurately classifying individuals based on their extracted features, thereby facilitating the early recognition of heart disease.

Ship Lock Chamber Arrangement in Xiangjiang River Based on Artificial Bee Colony and Adaptive Genetic Algorithm

Abstract To plan the ship lock chamber arrangement in Xiangjiang river, an artificial bee colony-adaptive genetic algorithm was proposed for the scheduling of the lock chamber, considering that the performance of the basic genetic algorithm relies on the quality of the initial population, the selection of genetic operators, crossover, and mutation operations. Firstly, the artificial bee colony algorithm was used to initialize the population to enhance population diversity. The utilization rate of the lock chamber and the waiting time for the lock were used as evaluation indicators for the fitness function. Then, adaptive strategies were designed to adjust the crossover and mutation operators to improve the convergence speed of the algorithm. Experimental results showed that, in a Xiangjiang river ship lock chamber with a size of 280 m×34 m, the average number of ships selected by the proposed artificial bee colony-adaptive genetic algorithm was 2 more than that of manual scheduling and 1.5 more than that of the genetic algorithm. The utilization rate of the lock chamber scheduling was increased by 13.01% compared to manual scheduling and by 5.4% compared to the genetic algorithm. The average waiting time for the lock was reduced by 1.3 hours compared to manual scheduling and by 0.8 hours compared to the genetic algorithm. It effectively improves the efficiency of ship traffic and optimizes the utilization of the lock chamber, making it practical for real-world applications.

An Optimization Approach for Enhancing Energy Efficiency, Reducing CO2 Emission, and Improving Lubrication Reliability in Roller Bearings using ABC Algorithm

Friction and energy waste pose significant challenges in various industrial processes. Lubrication plays a crucial role in reducing friction and optimizing energy consumption. This study focuses on analyzing, simulating and calculation the oil film thickness, friction levels, energy losses, and CO2 emissions. The objective is to optimize lubrication conditions to enhance performance, improve energy consumption, and maximize lubrication efficiency for rolling bearings in a centrifugal fan. The simulation utilizes ANSYS CFX software, MATLAB programming. The optimal oil viscosity grade is determined based on two objectives by using artificial bee colony algorithm (ABC): minimizing energy consumption (thus reducing CO2 emission) and achieving the optimal oil film thickness and viscosity ratio. The findings reveal that, under the current lubrication conditions and normal fan operation, energy losses due to oil friction amount to 36.3 MWh per year, with CO2 emissions resulting from power losses reaching 18,750 kilograms per year. By transitioning to the optimized oil grade, energy savings of 1.08 MWh per year and a corresponding reduction of 557 kilograms in CO2 emissions per year can be achieved.

Theoretical study on intramolecular hydrogen bonds of flavonoid cocrystals.

This study investigates the role of intramolecular hydrogen bonds in the formation of cocrystals involving flavonoid molecules, focusing on three active pharmaceutical ingredients (APIs): chrysin (CHR), isoliquiritigenin (ISO), and kaempferol (KAE). These APIs form cocrystals with different cocrystal formers (CCFs) through intramolecular hydrogen bonding. We found that disruption of these intramolecular hydrogen bonds leads to decreased stability compared to molecules with intact bonds. The extrema of molecular electrostatic potential surfaces (MEPS) show that flavonoid molecules with disrupted intramolecular hydrogen bonds have stronger hydrogen bond donors and acceptors than those with intact bonds. Using the artificial bee colony algorithm, dimeric structures of these flavonoid molecules were explored, representing early-stage structures in cocrystal formation, including API-API, API-CCF, and CCF-CCF dimers. It was observed that the number and strength of dimeric interactions significantly increased, and the types of interactions changed when intramolecular hydrogen bonds were disrupted. These findings suggest that disrupting intramolecular hydrogen bonds generally hinders the formation of cocrystals. This theoretical study provides deeper insight into the role of intramolecular hydrogen bonds in the cocrystal formation of flavonoids.

Optimizing Cyber Threat Detection in IoT: A Study of Artificial Bee Colony (ABC)-Based Hyperparameter Tuning for Machine Learning

In the rapidly evolving landscape of the Internet of Things (IoT), cybersecurity remains a critical challenge due to the diverse and complex nature of network traffic and the increasing sophistication of cyber threats. This study investigates the application of the Artificial Bee Colony (ABC) algorithm for hyperparameter optimization (HPO) in machine learning classifiers, specifically focusing on Decision Trees, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN) for IoT network traffic analysis and malware detection. Initially, the basic machine learning models demonstrated accuracies ranging from 69.68% to 99.07%, reflecting their limitations in fully adapting to the varied IoT environments. Through the employment of the ABC algorithm for HPO, significant improvements were achieved, with optimized classifiers reaching up to 100% accuracy, precision, recall, and F1-scores in both training and testing stages. These results highlight the profound impact of HPO in refining model decision boundaries, reducing overfitting, and enhancing generalization capabilities, thereby contributing to the development of more robust and adaptive security frameworks for IoT environments. This study further demonstrates the ABC algorithm’s generalizability across different IoT networks and threats, positioning it as a valuable tool for advancing cybersecurity in increasingly complex IoT ecosystems.

Energy-aware scheduling optimization in hybrid flow shops using artificial bee colony algorithm

Energy-aware scheduling optimization in hybrid flow shops using artificial bee colony algorithm