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Hybrid flow shop scheduling problem (HFSP) with real-life constraints has been extensively considered; however, HFSP with batch processing machines (BPM) at a middle stage is seldom investigated. In this study, HFSP with BPM at a middle stage in hot & cold casting process is considered and an adaptive artificial bee colony (AABC) is proposed to minimise makespan. To produce high quality solutions, an adaptive search process with employed bee phase and adaptive search step is implemented. Adaptive search step, which may be onlooker bee phase or cooperation or empty, is decided by evolution quality and an adaptive threshold. Cooperation is performed between the improved solutions of one employed bee swarm and the unimproved solutions of another swarm. Six search operators are constructed and search operator is adaptively adjusted. A new scout phase is also given. A lower bound is provided and proved. Extensive experiments are conducted. The computational results validate that new strategies such as cooperation are effective and efficient and AABC can obtain better results than methods from existing literature on the considered problem. [ABSTRACT FROM AUTHOR]

This paper proposes a novel artificial bee colony with predation effect (ABCPE) algorithm for tuning a proportional integral derivative (PID) controller. The mathematical model of ABCPE algorithm to introduce predator effect in the foraging behavior of artificial bees colony algorithm has been formulated. The proposed algorithm has been tested on tuning problems of different process models. The simulation results reveal that the closed-loop responses are relatively fast and non-oscillatory as compared to the frequency response analysis method for reference tracking. Further, the results obtained using ABCPE are also compared with other evolutionary algorithms. The exhaustive analysis shows that the ABCPE-based solution approach leads to a set of tuning parameters having smaller overshoot, less setting time, and rise time compared to other solution approaches. The stability analysis using Nichols plot reveals that the phase margin of proposed algorithm is higher as compared to other tuning methods. Finally, the convergence behavior and robustness analysis reveals the effectiveness of the proposed approach to solve engineering design problems. [ABSTRACT FROM AUTHOR]

Magnetic levitation systems are complex and nonlinear, requiring sophisticated control methods to maintain the stability and position of the levitated object. This research presents an optimized fractional order PID (FOPID) control approach for position control of a freely-suspended ferromagnetic object. The dynamic system model is mathematically modeled in MATLAB using first-principle modeling and the grey box method. The FOPID controller has five degrees of freedom (DOFs) that allow for fine-tuning of the control gains and fractional orders, enabling the system to handle the nonlinearity inherent in the magnetic levitation system. The DOFs of FOPID and integer order PID controllers are optimized using the Artificial Bee Colony (ABC) algorithm and results are compared with state-of-the-art optimization methods. The results showed that the FOPID controller can effectively control the magnetic levitation system with constraints and outperforms other methods by up to 92.14% in terms of settling time with negligible steady-state error. [ABSTRACT FROM AUTHOR]

Researchers face a formidable task in devising effective control strategies for highly nonlinear systems. Addressing these challenges, we adopt the MIT rule in our study and employ the Model Reference Adaptive Controller (MRAC) approach to optimize solutions for nonlinear processes. The adaptive controller design, based on MRAC, aims to stabilize a process in the face of large parameter variations and external disturbances affecting the closed-loop system. We apply this technique to analyze a set of closed-loop systems, including Non-Reheat Turbine, Brushless DC Motor (BLDC) and Continuous Stirred Tank Reactor (CSTR) related to process industries. The controller parameter tuning technique, based on Artificial Bee Colony (ABC), is applied to achieve optimal controller performance. To demonstrate the effectiveness of the proposed controller, we compare the results with the conventional Proportional–Integral–Derivative (PID) and hybridized control techniques. The simulation is performed in the time domain. The time response of Non-Reheat Turbine, BLDC, and CSTR from the ABC-MRAC-MIT controller has a rise time (0.3967 s, 0.4415 s, 0.0850 s), settling time (1.8160 s,2.772 s, 0.4452 s), maximum overshoot (6.30% 3.40% and 6.9279%) respectively. Simulation results reveal the superior time-response characteristics of the proposed ABC-MRAC-MIT technique compared to other existing methods, enhancing the performance of process models and validating the novelty of this work. [ABSTRACT FROM AUTHOR]

With the widespread implementation of artificial intelligence techniques and self-driving systems in smart cars, providing excellent coverage of wireless sensor networks is critical for stable and effective tasks. Coverage control is an essential task for the design of wireless sensor networks. However, considering the influence of network resources and coverage features, several normal optimization methods are hard to carry out, yet heuristic iterative algorithms could generate an estimated ideal feasible solution for this issue. We present an artificial bee colony algorithm based on random dual strategies, called RDABC. Specifically, RDABC modifies the optimization direction through alternating between dual search techniques with the goal to find further excellent feasible solution. At the same time, through incorporating cross-mutation strategy to improve variety, and increase the algorithm's optimization efficiency. According to simulation experiments, RDABC outperforms four well-known algorithms in terms of coverage optimization. As a whole, RDABC optimizes the location and deployment of wireless sensors, enhances the overall performance and stability of intelligent transportation systems, and simplifies vehicle monitoring and traffic sign tasks. [ABSTRACT FROM AUTHOR]

Reliable forecasting of short-term traffic flow is an essential component of modern intelligent transport systems. However, existing methods fail to deal with the non-linear nature of short-term traffic flow, often making the forecasting unreliable. Herein, we propose a reliable short-term traffic flow forecasting method, termed hybrid extreme learning, that effectively learns the non-linear representation of traffic flow, boosting forecasting reliability. This new algorithm probes the non-linear nature of short-term traffic data by exploiting the artificial bee colony that selects the best-implied layer deviation and input weight matrix to enhance the multi-structural information perception capability. It speeds up the forecasting time by calculating the output weight matrix, which guarantees the real usage of the forecasting method, boosting the time reliability. We extensively evaluate the proposed hybrid extreme learning method on well-known short-term traffic flow forecasting datasets. The experimental results show that our method outperforms existing methods by a large margin in both forecasting accuracy and time, effectively demonstrating the reliability improvement of the proposed method. This reliable method may open the avenue of deep learning techniques in short-term traffic flow forecasting in real scenarios. [ABSTRACT FROM AUTHOR]

Global constrained optimization problems are very complex for engineering applications. To solve complicated and constrained optimization problems with fast convergence and accurate computations, a new quantum artificial bee colony algorithm using Sin chaos and Cauchy factor (SCQABC) is proposed. The algorithm introduces a quantum bit to initialize the population, which is then updated by the quantum rotation gate to enhance the convergence of the artificial bee colony algorithm (ABC). Sin chaos is introduced to process the individual positions, which improves the randomness and ergodicity of initializing individuals and results in a more diverse initial population. To overcome the upper limits of visiting target individual positions, Cauchy factor is used to mutate individuals for solving falling into local optimum problems. To evaluate the performance of the SCQABC, 20 classical benchmark functions and CEC-2017 are used. The practical engineering problems are also used to verify the practicability of SCQABC algorithm. Moreover, the experimental results will be compared with other well-known and progressive algorithms. According to the results, the SCQABC improves by 64.93% compared with the ABC and also has corresponding improvement compared with other algorithms. Its successful application to the robot gripper problem highlights its effectiveness in solving constrained optimization problems. [ABSTRACT FROM AUTHOR]

This study addresses the growing threat of network attacks by exploring their types and analyzing the challenges associated with their precise detection. To mitigate these threats, we propose a novel cyber security approach that integrates Genetic Algorithm (GA) and neural network architecture. The GA is employed for the selection and optimization of attributes that represent DDoS and malware attack features. These optimized features are then fed into a neural network for training and classification. The effectiveness of the proposed approach was evaluated through precision, recall, and F-measure analyses, demonstrating superior detection capabilities for DDoS and malware attacks compared to existing methods. Furthermore, we introduce a hybrid approach that combines Swarm Intelligence (SI) and nature-inspired techniques. The GA is utilized to select features and reduce the dataset size, followed by the application of Discrete Wavelet Transform (DWT) with Artificial Bee Colony (ABC) to further filter irrelevant features. The results show that this hybrid approach significantly enhances the accuracy and efficiency of network attack detection in wide area networks. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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Unrelated parallel machine scheduling problem (UPMSP) is a typical scheduling one and UPMSP with various real-life constraints such as additional resources has been widely studied; however, UPMSP with additional resources, maintenance, and energy-related objectives is seldom investigated. The Artificial Bee Colony (ABC) algorithm has been successfully applied to various production scheduling problems and demonstrates potential search advantages in solving UPMSP with additional resources, among other factors. In this study, an energy-efficient UPMSP with additional resources and maintenance is considered. A dynamical artificial bee colony (DABC) algorithm is presented to minimize makespan and total energy consumption simultaneously. Three heuristics are applied to produce the initial population. Employed bee swarm and onlooker bee swarm are constructed. Computing resources are shifted from the dominated solutions to non-dominated solutions in each swarm when the given condition is met. Dynamical employed bee phase is implemented by computing resource shifting and solution migration. Computing resource shifting and feedback are used to construct dynamical onlooker bee phase. Computational experiments are conducted on 300 instances from the literature and three comparative algorithms and ABC are compared after parameter settings of all algorithms are given. The computational results demonstrate that the new strategies of DABC are effective and that DABC has promising advantages in solving the considered UPMSP. [ABSTRACT FROM AUTHOR]

Abstract In the domain of passive brain-computer interface applications, the identification of emotions is both essential and formidable. Significant research has recently been undertaken on emotion identification with electroencephalogram (EEG) data. The aim of this project is to develop a system that can analyse an individual’s EEG and differentiate among positive, neutral, and negative emotional states. The suggested methodology use Independent Component Analysis (ICA) to remove artefacts from Electromyogram (EMG) and Electrooculogram (EOG) in EEG channel recordings. Filtering techniques are employed to improve the quality of EEG data by segmenting it into alpha, beta, gamma, and theta frequency bands. Feature extraction is performed with a hybrid meta-heuristic optimisation technique, such as ABC-GWO. The Hybrid Artificial Bee Colony and Grey Wolf Optimiser are employed to extract optimised features from the selected dataset. Finally, comprehensive evaluations are conducted utilising DEAP and SEED, two publically accessible datasets. The CNN model attains an accuracy of approximately 97% on the SEED dataset and 98% on the DEAP dataset. The hybrid CNN-ABC-GWO model achieves an accuracy of approximately 99% on both datasets, with ABC-GWO employed for hyperparameter tuning and classification. The proposed model demonstrates an accuracy of around 99% on the SEED dataset and 100% on the DEAP dataset. The experimental findings are contrasted utilising a singular technique, a widely employed hybrid learning method, or the cutting-edge method; the proposed method enhances recognition performance.

Magnetic levitation systems are complex and nonlinear, requiring sophisticated control methods to maintain the stability and position of the levitated object. This research presents an optimized fractional order PID (FOPID) control approach for position control of a freely-suspended ferromagnetic object. The dynamic system model is mathematically modeled in MATLAB using first-principle modeling and the grey box method. The FOPID controller has five degrees of freedom (DOFs) that allow for fine-tuning of the control gains and fractional orders, enabling the system to handle the nonlinearity inherent in the magnetic levitation system. The DOFs of FOPID and integer order PID controllers are optimized using the Artificial Bee Colony (ABC) algorithm and results are compared with state-of-the-art optimization methods. The results showed that the FOPID controller can effectively control the magnetic levitation system with constraints and outperforms other methods by up to 92.14% in terms of settling time with negligible steady-state error.

This study focuses on the crucial need for effective control strategies design for linear electromechanical actuators (EMA), which are essential components in industrial automation. While the PI controller is commonly used due to its simplicity and versatility in various control scenarios, its effectiveness is limited by its complex tuning process, which requires significant time and effort to achieve the optimal performance. To address this issue, the paper focuses on employing metaheuristic approaches that are Spiral Dynamic Algorithm (SDA) and Artificial Bee Colony (ABC) to fine tune the PI parameters for controlling the position of EMA. The simulation results, implemented in MATLAB Simulink, show that PI-SDA and PI-ABC produce better performances with minimal steady-state error, reduced overshoot, faster settling time and rise time. Hardware-in-the-loop (HIL) testing proves the effectiveness of the controller's performance in real-world scenarios. PI-SDA achieved a steady-state error of 0.0623, zero overshoot, faster rise time of 1.6956 s, and faster settling time of 7.2166 s. As for validation, PI-ABC showed similar results in HIL environment with a steady-state error of -0.0314, an overshoot of 5.0007%, a rise time of 1.3805 s, and a settling time of 9.1002 s. These results highlight the effectiveness of metaheuristic methods in real-world implementation and outperform the traditional heuristic approaches.

The Internet of Things (IoT) is a new information technology sector in which each device may receive and distribute data across a network. Industrial IoT (IIoT) and related areas, such as Industrial Wireless Networks (IWNs), big data, and cloud computing, have made significant strides recently. Using IIoT requires a reliable and effective data collection system, such as a spanning tree. Many previous spanning tree algorithms ignore failure and mobility. In such cases, the spanning tree is broken, making data delivery to the base station difficult. This study proposes an algorithm to construct an optimal spanning tree by combining an artificial bee colony, genetic operators, and density correlation degree to make suitable trees. The trees' fitness is measured using hop count distances of the devices from the base station, residual energy of the devices, and their mobility probabilities in this technique. The simulation outcomes highlight the enhanced data collection reliability achieved by the suggested algorithm when compared to established methods like the Reliable Spanning Tree (RST) construction algorithm in IIoT and the Hop Count Distance (HCD) based construction algorithm. This proposed algorithm shows improved reliability across diverse node numbers, considering key parameters including reliability, energy consumption, displacement probability, and distance. [ABSTRACT FROM AUTHOR]

Myocarditis poses a significant health threat, often leading to severe outcomes such as heart failure or death. While endomyocardial biopsy (EMB) is a reliable diagnostic method, its invasive nature limits its use to severe cases. Cardiac magnetic resonance (CMR) offers a less invasive alternative by utilizing enhanced signal contrast to detect and monitor myocarditis, effectively highlighting affected myocardial areas. This study introduces an advanced model designed to overcome challenges like initial weight sensitivity and class imbalance, which are often overlooked in existing research. The model employs multiple convolutional neural networks (CNNs) to extract feature vectors from images for classification. To address class imbalance, a proximal policy optimization (PPO)-based algorithm is utilized, significantly improving the training process by preventing abrupt policy shifts and stabilizing them. This mechanism rewards precise classification of underrepresented classes more heavily than overrepresented ones, promoting balanced learning. Additionally, the model incorporates a novel pre-training method, the mutual learning-artificial Bee Colony (ML-ABC) technique, which dynamically adjusts initial weight settings through mutual learning components. This approach enhances the model's initial ability to recognize complex patterns in myocardial images, improving classification accuracy. The model's efficacy was validated using the Z-Alizadeh Sani myocarditis dataset, achieving an impressive F-measure of 83%. These results highlight the model's superiority over existing methods and confirm its potential for clinical use, marking a significant advancement in the early detection and treatment of myocarditis. [ABSTRACT FROM AUTHOR]

This article presents a classification of Acoustic Emission (AE) signals from AlSi10Mg specimens produced via Selective Laser Melting (SLM). Tensile tests characterized the mechanical properties of specimens printed in different orientations (X, Y, Z, 45°). Initially, a study quantified damage modes based on the stress-strain curve and cumulative AE energy. AE signals for each specimen (X, Y, 45°, Z), across deformation stages (elastic and plastic), and damage modes were analyzed using continuous wavelet transform to extract time-frequency features. A novel convolutional neural network, based on artificial bee colonies and fuzzy C-means, was developed for scalogram classification. Data augmentation with Gaussian white noise enhanced the approach. Cross-validation ensured robustness against overfitting and suboptimal local maxima. Evaluation metrics, including the confusion matrix, precision-recall curve, and F1 score, demonstrated the algorithm's high accuracy of 92.6%, precision-recall curve of 92.5%, and F1 score of 92.5% for AE signals based on printing direction (X, Y, 45°, Z). The study highlighted the potential for improving AE signal classification related to elastic and plastic deformation stages with 100% accuracy. For damage modes, the algorithm achieved a confusion matrix accuracy of 90.6%, a precision-recall curve of 90.4%, and an F1 score of 90.5%. This approach demonstrates high accuracy in classifying AE signals across different printing orientations, deformation stages, and damage modes of AlSi10Mg specimens manufactured through SLM. [ABSTRACT FROM AUTHOR]

The autonomous task success of an unmanned aerial vehiclel (UAV) or its military specialization called the unmanned combat aerial vehicle (UCAV) has a direct relationship with the planned path. However, planning a path for a UAV or UCAV system requires solving a challenging problem optimally by considering the different objectives about the enemy threats protecting the battlefield, fuel consumption or battery usage and kinematic constraints on the turning maneuvers. Because of the increasing demands to the UAV systems and game-changing roles played by them, developing new and versatile path planning algorithms become more critical and urgent. In this study, a greedy algorithm named as the Back-and-Forth (BaF) was designed and introduced for solving the path planning problem. The BaF algorithm gets its name from the main strategy where a heuristic approach is responsible to generate two predecessor paths, one of which is calculated from the start point to the target point, while the other is calculated in the reverse direction, and combines the generated paths for utilizing their advantageous line segments when obtaining more safe, short and maneuverable path candidates. The performance of the BaF was investigated over three battlefield scenarios and twelve test cases belonging to them. Moreover, the BaF was integrated into the workflow of a well-known meta-heuristic, artificial bee colony (ABC) algorithm, and detailed experiments were also carried out for evaluating the possible contribution of the BaF on the path planning capabilities of another technique. The results of the experiments showed that the BaF algorithm is able to plan at least promising or generally better paths with the exact consistency than other tested meta-heuristic techniques and runs nine or more times faster as validated through the comparison between the BaF and ABC algorithms. The results of the experiments further proved that the integration of the BaF boosts the performance of the ABC and helps it to outperform all of fifteen competitors for nine of twelve test cases. [ABSTRACT FROM AUTHOR]

Delivery apps are experiencing significant growth, requiring efficient algorithms to coordinate transportation and generate profits. One problem that considers the goals of delivery apps is the multi-vehicle profitable pickup and delivery problem (MVPPDP). In this paper, we propose eight new metaheuristics to improve the initial solutions for the MVPPDP based on the well-known swarm intelligence algorithm, Artificial Bee Colony (ABC): K-means-GRASP-ABC(C)S1, K-means-GRASP-ABC(C)S2, Modified K-means-GRASP-ABC(C)S1, Modified K-means-GRASP-ABC(C)S2, ACO-GRASP-ABC(C)S1, ACO-GRASP-ABC(C)S2, ABC(S1), and ABC(S2). All methods achieved superior performance in most instances in terms of processing time. For example, for 250 customers, the average times of the algorithms was 75.9, 72.86, 79.17, 73.85, 76.60, 66.29, 177.07, and 196.09, which were faster than those of the state-of-the-art methods that took 300 s. Moreover, all proposed algorithms performed well on small-size instances in terms of profit by achieving thirteen new best solutions and five equal solutions to the best-known solutions. However, the algorithms slightly lag behind in medium- and large-sized instances due to the greedy randomised strategy and GRASP that have been used in the scout bee phase. Moreover, our algorithms prioritise minimal solutions and iterations for rapid processing time in daily m-commerce apps, while reducing iteration counts and population sizes reduces the likelihood of obtaining good solution quality. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

With the continuous development of computer vision, the application of image recognition technology is becoming increasingly widespread. An edge detection image recognition model based on improved artificial bee colony algorithm has been proposed. Firstly, the identification process of artificial bee colonies is designed. To solve the algorithm easily falling into local optima, a GA with a global search strategy is further improved, achieving an improvement in model operation speed and coherence. Moreover, the target detection and localization methods are selected. The Canny operator and line fitting method are ultimately determined for image search and localization. To further verify the reliability of the improved artificial bee colony algorithm, simulation experiments are conducted on the MATLAB platform. The experimental results show that under 0.1 noise, the improved artificial bee colony algorithm has better recognition accuracy, compared to the particle swarm algorithm. The calculation time is reduced by 7.35s. In summary, the improved artificial bee colony algorithm has the best recognition accuracy and noise resistance performance. [ABSTRACT FROM AUTHOR]

This study proposes an improved method of the Artificial Bee Colony (ABC) algorithm for the distribution network in scenarios where distributed generation sources and Soft Open Points (SOPs) are connected to optimize power control. Improvement is achieved by integrating the ABC algorithm with the Grenade Explosion Method and Cauchy to accelerate the ABC algorithm's speed. The objective function is considered to reduce power losses over a day. The proposed method was tested on the IEEE-33 bus test system under various scenarios: Case 1 with 3 DGs installed, Case 2 with 3 DGs and 1 SOP simultaneously installed in the distribution network, and Case 3 having the same configuration as Case 2 but operating for 24 hours. In addition to reducing power losses, the voltage at the nodes in the distribution grid was also improved, maintained above 0.95 pu and close to 1 pu. Case 3 showed that integrating a Wind Turbine (WT), two Photovoltaic (PV) generators, and one SOP during operation resulted in the lowest energy losses, smaller than a system with only one WT and two PVs, and significantly lower than the baseline system without any DGs and SOPs. Therefore, employing SOPs in a distribution network with integrated DGs can offer significant benefits in reducing energy losses. [ABSTRACT FROM AUTHOR]

The issue of imbalanced datasets, i.e., uneven sample distribution among different classes causes training biases and degrades learning algorithm performance. In past, several solutions for data imbalance handling have been proposed but most of them focus on removing the majority class instances, leading to loss of important information. An alternate strategy to mitigate this issue that has been investigated in literature is minority class samples generation. However, generation of quality synthetic samples for minority class remains an open problem. In this study, a fusion of grey wolf optimizer (GWO) with artificial bee colony (ABC) is proposed to generate good representative samples of the minority class. The combination is analysed because GWO has good exploitation abilities, while ABC is good at exploration. The effectiveness of the proposed method is tested on 20 real-world benchmark datasets and on one real-life application, i.e., scam video classification on YouTube using standard assessment indicators. The performance of the proposed method is compared against 18 state-of-the-art data imbalance handling methods using three classification algorithms, i.e., support vector machine (SVM), k-nearest neighbours (KNN) and decision tree (DT). Our experimental results show an improvement in G-mean score on 18 out of 20 datasets with a maximum improvement of 8% for SVM, and on 17 out of 20 datasets with a maximum improvement of 10.7% for KNN and 6.3% for DT respectively. An improvement in AUC score is also seen on 17 out of 20 datasets for SVM and DT with a maximum improvement of 4.5% and 6% respectively, and on 16 out of 20 datasets for KNN with a maximum improvement of 7.7%. These results show that the proposed method is robust. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the wake of unparalleled expansion in digital communication platforms, the imperative to bolster security and privacy measures has escalated. Within this landscape, image steganalysis emerges as a pivotal domain committed to detecting concealed information embedded in image files. This academic article unveils a novel image steganalysis model, melding dilated convolutional methodologies with a state-of-the-art mutual learning-based artificial bee colony (ML-ABC) approach and reinforcement learning (RL). The architecture operates a consortium of convolutional neural networks, collaboratively deriving features. After derivation, these features are combined to simplify the subsequent classification task. A reinforcement learning-focused (RL-focused) algorithm is employed to address the challenges posed by uneven datasets. The learning path is conceived as a series of linked decision points, with each instance representing a unique state. The network acts as an agent, earning rewards or suffering consequences according to its ability to distinguish between less frequent and more frequent classes. To commence the initial weight training, a methodology grounded in ML-ABC is implemented. This tactic adeptly adjusts the optimal food source for solution candidates, intertwining elements of mutual learning tied to the initial weights. The efficacy of the model is rigorously evaluated utilizing the BossBase 1.01 and BOWS datasets. Thorough experimentation is conducted on the selected dataset, with the objective of identifying optimal parameter values, including the reward mechanism. Subsequent results prominently highlight the superiority of our proposed solution compared to alternative methods explored within this research. [ABSTRACT FROM AUTHOR]

Service composition is widely employed in cloud manufacturing. Due to the abundance of similar cloud manufacturing services, the search space for optimizing service composition tends to be expansive. Existing optimization models primarily focus on QoS (quality of service) while often neglecting QoC (quality of collaboration). Furthermore, there remains scope for improving the quality and stability of service composition optimization. Therefore, this paper proposes a two-phase method for optimizing service composition in cloud manufacturing. In the first phase, we introduce a service cluster-oriented service response framework, efficiently generating the candidate response service set to reduce solution search space. In the second phase, we construct an optimization model that integrates QoS and QoC. Subsequently, we devise an artificial bee colony (ABC) algorithm incorporating a multi-search strategy island model to optimize cloud manufacturing service composition. Experimental results demonstrate that the introduction of service clusters enhances search efficiency, with the proposed method outperforming compared ABC algorithms and other swarm intelligence algorithms in optimization quality and stability. [ABSTRACT FROM AUTHOR]

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.

The objective of solving feature-based localization problems is to estimate the path of the robot referring to a given map. Thus, it is not surprising that robust estimators such as Smooth Variable Structure Filter (SVSF) are often used to handle this problem. Basically, its use is highly dependent on an accurate system model and known statistical noise. Where neither of these are available by definition. Therefore, the conventional way is not recommended and the use of an adaptive filter approach can be involved. Based on this and although only partially, Innovation Adaptive Estimation (IAE) has been considered to have a positive influence on improving the performance of the estimator. But not infrequently the solutions offered by this approach also lead to divergences due to unmapped dynamic conditions. Moreover, in this proposal, IAE is enhanced by applying Artificial Bee Colony-Tuned Fuzzy Logic. The hope is that there is quality control for the process noise covariance Q and R measurements by updating them based on the output of this ABC-Tuned FLC.

Optimization of scheduling problems that involve single or multiple machines with a multimodal objective function and linear or non-linear constraints are generally NP-Hard. The Artificial Bee Colony (ABC) algorithm is a metaheuristic proposed about eighteen years ago and has gained popularity in the swarm intelligence-based evolutionary computation approaches. Various scheduling requirements in diverse domains viz. Electric Vehicle charging, Operating Room, Nurse, Automatic Guided Vehicle (AGV), production, manufacturing, hydrothermal, Printed Circuit Board (PCB), and call centre scheduling are studied. The NP-Hard nature of these scheduling problems render exact methods incompetent for large problem instances thus requiring meta-heuristics such as ABC to produce feasible solutions in a reasonable computation time. The adaptation of ABC in these domains and the outcomes and performance of ABC are highlighted. Based on the case studies, it is found that the requirements of scheduling problems that would benefit from ABC are those that have a planning horizon requiring a near-optimal solution in a reasonably fast time when run on minimal computing capacity. Future works that would merit the application and enhancement of existing ABC techniques are proposed. The case studies presented in this paper motivate R&D Engineers and young researchers to apply ABC techniques of optimization and scheduling in different research topics for achieving energy and cost efficiency. [ABSTRACT FROM AUTHOR]

To address the issues of slow and premature convergence of artificial bee colony (ABC) for handling complex multimodal optimization problems, this paper proposes a multi-role steered variable dimensionality perturbation ABC called MrABC-vdp. MrABC-vdp assigns the nectar sources four distinct roles based on their quality at each iteration. To strike the global exploration and local exploitation equilibrium of the solution space, the bee colony's search behavior is tightly controlled and corrected based on the synergistic relationship established between various preference search equations under multi-role nectar source guidance and the employed bee, onlooker bee, and scout bee phases. To achieve the dynamic trade-off of the global exploration speed and local convergence accuracy of the bee colony across the iteration, a variable dimensionality perturbation is imposed on the candidate nectar sources by a simulated binary crossover operator with time-varying crossover probability. Experimental results on the CEC 2017 multimodal benchmarks show that the proposed MrABC-vdp has a significant performance advantage and strong robustness versus nine advanced homogenous and heterogenous swarm-inspired intelligent algorithms. [ABSTRACT FROM AUTHOR]