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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.

Online social networks, especially Twitter (X), have become focal points for illicit activities, providing unique criminal investigation opportunities. This paper introduces an innovative methodology that uses social media sentiment analysis to predict criminal activities. One major challenge in sentiment analysis is the uneven distribution of sentiment classes, where traditional models often fail to accurately classify instances of the minority class due to the overwhelming presence of majority class data. To tackle this issue, we propose a model that combines a reinforcement learning (RL) algorithm with a scope loss function. The RL approach uses a reward mechanism that assigns a more significant value to correctly predicting minority class instances over majority class ones. The scope loss function ensures an optimal balance between utilizing known data and exploring new data, thus maintaining a delicate equilibrium between accuracy and generalizability. Our model employs a series of convolutional neural networks (CNNs) to extract significant features from textual content, which are then utilized for sentiment classification. We also incorporate an advanced artificial bee colony (ABC) optimization technique to refine the model's hyperparameters. The effectiveness of our approach was empirically tested using two distinct datasets: one consisting of crime incident reports from the Chicago Police Department covering the period from September 2019 to July 2024 and another comprising tweets containing crime-related terms related to Chicago. The predictive capabilities of our proposed model were benchmarked against existing models, demonstrating superior performance with accuracies of 96.411% and 94.088%, respectively. This breakthrough highlights the potential of integrating sentiment analysis with reinforcement learning to significantly enhance the predictive accuracy of crime-related activities in online social networks, offering valuable insights for law enforcement and criminal investigation applications.

K-means clustering is a simple and efficient, fast in convergence and easy to implement statistical analysis method. However, the traditional [K-means] clustering algorithm is sensitive to the selection of initial clustering centers and easy to fall into a local optimum, and at the same time, most unsupervised feature selection algorithms are easy to ignore the relationship between features. To solve the above issues, this paper proposes a feature selection algorithm combining artificial bee colony with [K-means] clustering. Firstly, to make the similarity of samples in the same cluster high and the similarity of the samples in different clusters low, a new fitness function is constructed based on the clustering degree within the cluster and the dispersion degree between the clusters, which can better reflect the characteristics of each sample, and then a new probability expression of the honey source being selected is constructed. Secondly, the weight which decreases gradually with the increase of the number of iterations is designed, and the honey source location update expression that makes the search range of the bee colony dynamically indent is proposed. Thirdly, to make up for the limitation of the traditional Euclidean distance which only considers the cumulative difference between vectors when calculating the distance, a weighted Euclidean distance expression which simultaneously considers both the different influence degrees of the samples and the similarity of the samples is constructed. Finally, the standard deviation and distance correlation coefficient are introduced to define feature discrimination and feature representativeness, and the product of them is used to measure the importance of features. Experimental results show that the proposed algorithm accelerates the convergence speed of artificial bee colony algorithm and improves the clustering effect of [K-means] algorithm, and also effectively improves the classification effect of feature selection.

Linear Quadratic Regulator is one of the most common ways to control a linear system. Despite Linear Quadratic Regulator’s (LQR) strong performance and solid resilience, developing these controllers have been challenging, largely because there is no reliable way to choose the Q and R weighing matrices. In this regard a deterministic method is used for choosing them in this paper, providing the designers a precise control over performance variables. An Artificial Bee Colony (ABC) optimisation is also used to find the sub-optimal gain matrices along with an analytical approach based on neural networks. A comparative study of the three approaches is performed using MATLAB simulations. These three approaches are applied on an inverted pendulum–cart system due to its complexity and dexterity. The results show that all the three methods show comparable performances with the proposed analytical method being slightly better in terms of transient characteristics.

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.

This paper presents the results of a simulation model regarding the productivity and safety working space for construction workers through the floors of a building using swarm intelligence (SI), a field of artificial intelligence (AI), and specifically using artificial bee colony (ABC) optimization. After designing the algorithm used to build the simulation model, the simulation was used in an actual building project by comparing the travel times of workers conventionally transporting material with another group working on routes optimized by the algorithm. Thus, the proposed algorithm provides routes combining shorter travel times and correct distances between workers when transporting materials in a construction site, handling the interference between crews. After validating the algorithm on-site, no statistically significant differences were found between the travel times of workers and the times delivered by the algorithm. Additionally, the travel times using the routes obtained through the algorithm were significantly lower than those made by workers who moved freely without a predefined route. In summary, the algorithm proposed may help construction practitioners maintain safe movements that respond to hazard contexts imposed by any restriction that demands a safety distance.

In the published version of the paper, there is an error in Fig. 7. Figure 7 should be the results of different construction methods when "the standard deviation of shadow fading is 3 dB", but in the published version it is the result of different construction methods when "the standard deviation of shadow fading is 0 dB". The original version of Fig. 7 in the paper should be replaced with the corrected version of Fig. 7 as follows. Link to original paper: https://www.radioeng.cz/fulltexts/2022/22_03_0422_0430.pdf

Abstract This paper investigates the enhancement of voltage stability margin using Unified Power Flow Control (UPFC) device tuned with Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Bacterial Foraging Optimization (BFO) and Cuckoo Search techniques (CS) on Nigerian 330 kV 56-bus practical network. In order to have a voltage stable power system, it is good to keep voltages within the acceptable limits. This is achieved using continuation power flow embedded in Power System Analysis Toolbox (PSAT). The optimal location and tuning of the UPFC device is determined using line stability index and the met-heuristics techniques. The effects of UPFC and the proposed optimizing techniques on voltage stability margin are examined. The results so obtained for tuning PSO, ABC, BFO and Cuckoo Search with UPFC device are compared to know the technique that yields the best loading parameter for Nigerian 56- bus power system for voltage stability margin enhancement. It is found that UPFC tuned with Cuckoo Search outperforms the other techniques in terms of the increased loading parameter of the Nigerian power system with margin improvement of 86.6%, 90% and 88.6% for Adiabor (Bus 45), Jalingo (Bus 34) and Jos (Bus 13). The tuned UPFC device has proved beyond reasonable doubt that it can improve voltage stability margin of the entire Nigerian 330 kV Network as envisaged from results before and after optimization.

A swarm-based metaheuristic algorithm, like artificial bee colony (ABC), embraces four key elements of collective intelligence: positive feedback, negative feedback, multiple interactions, and fluctuation. Fluctuation refers to population diversity which can be measured using dimension-wise diversity. This paper performed component-wise analysis of ABC algorithm using diversity measurement. The analysis revealed scout bees component as counterproductive and onlooker bees component with poor global search ability. Subsequently, an ABC algorithm without scout bees component and modified onlooker bees component is proposed in this paper. The effectiveness and efficiency of the proposed ScoutlessABC is validated on test suite of a dozen of benchmark functions. To further evaluate the performance, ScoutlessABC is employed on the parameter training problem of fuzzy neural network for solving eight classification problems. The experimental results show that ScoutlessABC maintains strong convergence ability than the original ABC algorithm. Overall, this study has two major contributions: (a) an effective component-wise analysis approach using diversity measurement and (b) a simplified and modified ABC variant with enhanced search efficiency.

Abstract In this paper, we consider a strong global search algorithm which exhibits strong exploration ability in unmanned aerial vehicle (UAV)-aided networks. UAVs in wireless communication have aroused great interest recently due to its low cost and flexibility in providing wireless connectivity in areas without infrastructure coverage. Artificial bee colony algorithm is a powerful approach for such a scene. However, due to its one-dimensional and greedy search strategy, it still suffers slow convergence speed. In the traditional version, three types of bees, including employed bees, onlooker bees, and scouts, are employed and they cooperate with each other to find the best food source position. Though different roles, these three types of bees play, there is no difference of division within the internal of each type of bees. Considering this phenomenon, this paper proposes a modified artificial bee colony algorithm with intellective search and special division (ABCIS) to enhance its performance, where different employed bees and different onlooker bees use different search strategies to search for food sources. Besides, the greedy selection method is also abandoned and the food sources’ positions are updated at each iteration. Under this circumstance, the whole population’s experience is fully utilized to guide bee’s search. Finally, to testify the proposed algorithms’ competitiveness, a series of benchmarks are adopted, and the experimental results demonstrate its superior performance among other state-of-the-art algorithm in UAV-aided networks.

Technology advancement in Wireless sensor network (WSNs) has attracted much attention in smart computing and potential usage across numerous applications fields. WSN composed of tiny and self-configured sensor nodes with battery operated. Sensors are restricted to limited energy and resources. Unbalanced nodes energy results in more energy consumption and affects network lifetime. Achieving energy efficiency remains a challenging issue in designing WSN routing. Approaches for minimising node energy usage through clustering techniques have offered optimal solutions. However, the existing clustering scheme fails to balance nodes energy without considering energy parameters, low energy node being chosen as cluster head, node density, and scalability. Existing scheme limits to global exploration possibilities and restricted to only specific search regions. In this paper, we propose an energy optimization routing using improved artificial bee colony (EOR-iABC) for cluster based WSN. The proposed EOR-iABC routing scheme intends to achieve energy optimization and prolong network's lifetime. The EOR-iABC adapts distinctive search policy using improved artificial bee colony algorithm for selection of energy efficient cluster heads (CH) in periodic time intervals through integration of crossover and mutation. The delay convergence is eliminated by associating employee and onlooker-bee stages, which helps to reform local search strategies. An optimal path from CH to base station (BS) is discovered through energy-efficient fitness node to improve network data collection efficiency. Grenade explosion method (GEM) and Cauchy operator are employed to extend search policies dynamically from one region to another for large-scale WSN. Simulation results show EOR-iABC outperforms in terms of energy efficiency by 27% compared to OCABC scheme and by 16% compared to IABCOCT scheme.

Abstract The recycling and remanufacturing of end-of-life products are significant for environmental protection and resource conservation. Disassembly is an essential process of remanufacturing end-of-life products. Effective disassembly plans help improve disassembly efficiency and reduce disassembly costs. This paper studies a disassembly planning problem with operation attributes, in which an integrated decision of the disassembly sequence, disassembly directions, and disassembly tools are made. Besides, a mathematical model is formulated with the objective of minimizing the penalty cost caused by the changing of operation attributes. Then, a neighborhood modularization-based artificial bee colony algorithm is developed, which contains a modular optimized design. Finally, two case studies with different scales and complexities are used to verify the performance of the proposed approach, and experimental results show that the proposed algorithm outperforms the two existing methods within an acceptable computational time.

Modern power systems require advanced monitoring and control, a capability made possible by Phasor Measurement Units (PMUs) aided by synchrophasor technology. Because PMUs have significant costs, it is necessary to optimally place them in an electrical power network. This paper proposes the Optimal PMU Placement (OPP) on the Kenya Power Transmission Network (Nairobi Region 30-bus system) using the existing methods; The Depth-First method, the Mixed Integer Linear Programming (MILP) using intlinprog solver, and the Artificial Bee Colony (ABC) algorithm. The algorithms are first implemented on the IEEE-14 and 30 bus test systems for verification before implementing the Kenya Power Transmission Network (Nairobi Region 30-bus system). Finally, the results for the three methods are compared. A key consideration is the System Observability Redundancy Index (SORI) under normal baseload conditions, with and without the inclusion of Zero Injection Buses (ZIBs). A higher value of SORI increases the measurement redundancy of the PMUs installed at a given bus. This paper further proposes the modelling of ZIB with adjacent buses by considering their Observability Index (OI). The case studies are modelled in Power System Analysis Toolbox (PSAT), and the simulations are carried out in MATLAB. From the simulation results, the ABC algorithm gives the optimal solution with the highest SORI compared to the Depth-First method and MILP, with the exclusion of the ZIB. The Nairobi region 30-bus system require 12 PMUs located at buses; 2, 5, 8, 9, 11, 13, 14, 16, 21, 24, 27 and 29 for complete power system observability with a SORI of 43.

Cloud computing is increasing rapidly as a successful paradigm presenting on-demand infrastructure, platform, and software services to clients. Load balancing is one of the important issues in cloud computing to distribute the dynamic workload equally among all the nodes to avoid the status that some nodes are overloaded while others are underloaded. Many algorithms have been suggested to perform this task. Recently, worldview is turning into a new paradigm for optimization search by applying the osmosis theory from chemistry science to form osmotic computing. Osmotic computing is aimed to achieve balance in highly distributed environments. The main goal of this paper is to propose a hybrid metaheuristics technique which combines the osmotic behavior with bio-inspired load balancing algorithms. The osmotic behavior enables the automatic deployment of virtual machines (VMs) that are migrated through cloud infrastructures. Since the hybrid artificial bee colony and ant colony optimization proved its efficiency in the dynamic environment in cloud computing, the paper then exploits the advantages of these bio-inspired algorithms to form an osmotic hybrid artificial bee and ant colony (OH_BAC) optimization load balancing algorithm. It overcomes the drawbacks of the existing bio-inspired algorithms in achieving load balancing between physical machines. The simulation results show that OH_BAC decreases energy consumption, the number of VMs migrations and the number of shutdown hosts compared to existing algorithms. In addition, it enhances the quality of services (QoSs) which is measured by service level agreement violation (SLAV) and performance degradation due to migrations (PDMs).

Spectrum map (SM) is an important tool to reflect the spectrum usage in the electromagnetic environment. To address the problems of low precision and poor efficiency in the SM construction, this paper develops a novel SM construction approach based on the artificial bee colony enabled sensor layout optimization and an adaptive Kriging model based on spatial autocorrelation. Considering the significant autocorrelation between sensor attributes caused by the exponentially decaying shadow fading of signal propagation, the sensor estimation groups are established, and the estimation results are obtained by the Kriging model. The simulation results show that the proposed SM construction scheme can not only effectively reduce the overhead of sensor resources but also obtain a high SM construction accuracy. Extensive simulation results show that the proposed method can reduce the RMSE of SM construction by 37.56%, 25.32% and 12.89% respectively compared with Random-OK when the standard deviation of shadow fading is 1 dB, 3 dB and 6 dB.

The process of selecting software requirements aims to identify the optimal set of requirements that enhances the value of a software release while keeping costs within the budget. It is referred to as the next release problem (NRP) and is classified as a non-deterministic polynomial (NP) hard problem. Additionally, the addressed requirements are complicated by interconnections and other constraints. In the current paper, the NRP is defined as a multi-objective optimization problem with two conflicting objectives, the satisfaction of customers and cost of development, and three constraints to address two real-world instances of the NRP. A hybrid algorithm combining the multi-objective artificial bee colony and differential evolution named (HABC-DE) is proposed in this work. The proposed approach involves management from the original artificial bee colony (ABC) with operators of the differential evolution (DE) algorithm to balance the optimization process's exploitation and exploration stages. The results demonstrated that the suggested algorithm was capable of efficiently generating high-quality non-dominated solutions with 163.48 ± 4.9295 for mean and standard deviation values which can help decision-makers choose the right set of requirements for a new software release production.

Cities based on oil and gas energy resources are crucial to energy production and economic development, but they also face various disasters and security risks. To ensure the safety and well-being of urban residents during disaster events, the planning of urban shelters is crucial. In this paper, comprehensively considering multiple factors such as disaster risk, population distribution, and convenient transportation, the artificial bee colony algorithm is used to optimize the site selection and capacity planning of shelters. By comprehensively evaluating the disaster resistance capacity of urban refuges, the response speed of residents and other related indicators, the planning algorithm of refuges is continuously optimized to better meet the needs of oil and gas energy resource-based cities. The results of the study showed that the average overall disaster resilience of AI-based urban shelters reached 0.64. When the distance to the shelter was 4 km, the average response speed of residents reached 10.22 min, and other indicators also improved. The research shows that the oil and gas energy urban refuge planning algorithm based on the artificial intelligence elastic city model provides an innovative approach for urban planners and disaster managers. Further research and practice will help promote the application of this algorithm in real cities, improving the resilience and disaster resistance of cities and the safety and security level of residents.

The patient admission scheduling (PAS) problem is a well-known combinatorial optimization problem that entails the allocation of patients to some restricted bedspace for a certain period while adhering to a set of established constraints such as medical preferences and prerequisites. The existing algorithmic techniques utilized in solving the formulation of the PAS could not efficiently navigate the deep solution space due to the highly constrained nature of the formulation. Therefore, to address the drawbacks exhibited by the existing search methodologies when applied to the PAS problem, this paper proposed a solution template that hybridized the artificial bee colony (ABC) with hill climbing heuristic (HCH) named the Hill-ABC algorithm. Hill climbing is integrated within the operator of employed bees of the ABC algorithm to exploit the PAS’s rugged solution space and strike the right balance between diversification and intensification during search activities. The design of the proposed Hill-ABC approach is in two phases: The first phase is aimed at generating initial feasible solutions using a room selection-based method (RSM). Similarly, the second phase employed three neighbourhood structures to improve the quality of previously generated initial solutions. The proposed hybrid Hill-ABC algorithm is validated using the PAS standard datasets. The experiment analysis shows that the proposed method achieved better-quality solutions than most of the existing state-of-the-art algorithms.

The usage of renewable energy sources as an alternative energy source is faced with a major challenge introduced by the intermittency nature of the source. We have two options i) adapt the demand to the source, or ii) adapt the source to the demand. If the process demand is flexible and can be adapted to the energy source, it will enjoy cheap energy. Alternatively, non-flexible processes will require extra costs to be paid to adapt the source to the process energy requirements. This paper presents a demand response model to adapt the demand of industrial processes to the available energy source. The model is solved analytically using mixed integer linear programming. Then, three metaheuristic search algorithms were used. The results of the solving algorithms are compared with each other using the Wilcoxons' rank-sum test. Then the model is tested for two examples of flexible and rigid processes showing the differences.

The integro-differential equation with the Cauchy kernel is used in many different technical problems, such as in circuit analysis or gas infrared radiation studies. Therefore, it is important to be able to solve this type of equation, even in an approximate way. This article compares two approaches for solving this type of equation. One of the considered methods is based on the application of the differential Taylor series, while the second approach uses selected heuristic algorithms inspired by the behavior of animals. Due to the problem domain, which is symmetric, and taking into account the form of the function appearing in this equation, we can use this symmetry in some cases. The paper also presents numerical examples illustrating how each method works and comparing the discussed approaches.

Feature selection is a basic task for pattern recognition and classification. It enhances the performance of the classification algorithms with the help of removing the redundant features. Thanks to eliminating irrelevant features, the computational time is decreased. Thus, intensive works have been carried out in this area. This paper proposes a new standard error-based artificial bee colony (SEABC) algorithm for the feature selection problem, which is developed by integrating standard error-based new solution search mechanisms into the original artificial bee colony algorithm. The SEABC algorithm is used for feature selection. Shannon entropy function is used to serve as the objective function of the SEABC algorithm. Thirteen datasets are used from UCI machine learning datasets. Features are selected according to Shannon conditional entropy values and then a threshold process is implemented to find their best relevant subset. Support Vector Machines (SVMs) and k-Nearest Neighbor (KNN) are used as the optimal classifiers. The proposed SEABC algorithm is compared with genetic algorithm (GA), particle swarm optimization (PSO), ABC, improved ABC (I-ABC), Gbest-guided ABC (GABC), and PS-ABC algorithms. In general, it is observed that the SEABC algorithm achieves better classification results than other well-known algorithms.

Energy efficiency is considered as the most potential issue in Wireless Sensor Networks (WSNs), since they incorporate limited sized batteries that could not be recharged or replaced. The energy possessed by the sensor nodes needs to be optimally utilized in order to extend the lifetime expectancy with guaranteed QoS in the network. In this paper, a Hybrid Artificial Bee Colony and Monarchy Butterfly Optimization Algorithm (HABC-MBOA)-based Cluster Head Selection Scheme is proposed for the predominant selection of cluster heads under clustering process. This proposed HABC-MBOA replaces the employee bee phase of ABC with mutated butterfly adjusting operator of MBOA for preventing earlier trapping of solutions into a local optimal point and delayed convergence by maintaining the tradeoff between exploitation and exploration. This proposed HABC-MBOA plays an anchor role in eliminating inadequacy of ABC algorithm towards global search potential. This proposed HABC-MBOA also eliminates the possibility of cluster heads being overloaded with maximum number of sensor nodes, that results in rapid death of the sensor nodes during the deployment of impotent cluster head selection process. The simulation results confirmed that the number of alive nodes in the network is determined to be 18.92% superior to the benchmarked cluster head selection approaches.

Demodulation methods play a critical role in achieving high-performance interferometric fiber-optic temperature sensors. However, the conventional passive 3 × 3 coupler demodulation method overlooks certain issues, such as the non-1:1:1 splitting ratio of the coupler, resulting in a non-ideal phase difference in the three output interference signals. These problems significantly impact the measurement results of interferometric temperature sensors. In this paper, we propose a novel arc-tangent method based on a 3 × 3 coupler and a demodulation algorithm combining long short-term memory (LSTM) with an artificial bee colony (ABC). The arc-tangent method is employed to enhance the input phase signal of the ABC-LSTM network model and establish a nonlinear mapping between the phase signal and temperature, effectively preventing the influence of the spectral ratio and phase difference of the 3 × 3 coupler on temperature demodulation. The proposed ABC-LSTM method achieves high-resolution measurements with an interval of 0.10 °C, and the absolute error is below 0.0040 °C within the temperature range of 25.00–25.50 °C. To demonstrate the stability and adaptability of the proposed method under long-term constant temperature conditions, we conducted measurements for approximately three hours in a controlled temperature environment set at 25.00 °C. Experimental results indicate that the maximum error of LSTM-ABC method remains around 0.0040 °C, outperforming the conventional algorithm (0.0095 °C). Furthermore, when comparing the average error values of the conventional passive 3 × 3 coupler method (0.0023 °C), LSTM model (0.0019 °C), and ABC-LSTM model (0.0014 °C), it is evident that the demodulation results of the ABC-LSTM method exhibit the highest level of stability. Therefore, the ABC-LSTM method enhances the accuracy and reliability of interferometric fiber-optic temperature-sensing systems.

Facial expression recognition in the world is challenging due to various unconstrained conditions. Although existing facial expression classifiers have been almost perfect for analyzing constrained frontal faces, they fail to perform well on partially occluded faces common in the wild. This paper deals with Facial expression detection and recognition through the Viola-jones algorithm and HCNN using the LSTM method to avoid those challenges in recent work. For face detection, basically, utilize the face detection Viola-Jones algorithm and it recognizes the occluded face and it helps to perform the feature selection through the whale optimization algorithm, once after compression and further, it minimizes the feature vector given into the HCNN and LSTM model for efficiently identifying the facial expression. In existing work, the feature extraction stage exact finding of the corner becomes a very difficult task, to solve this issue need to use a corner detection algorithm to enhance the feature extraction (corner points) from the face image. One of the main drawbacks of WOA is that it is not good at exploring the search space. To overcome those issues, the work introduced an improved framework for facial image recognition. In which first edges are detected using the canny edge detection operator. Then improved linear contrast stretching is used for image enhancement. Then in feature extraction, the author proposes Hybrid SIFT with Double d-LBP (Dd-LBP) to obtain the features that are illumination and pose independent. For face detection, utilize the face detection Viola-Jones algorithm and it recognizes the occluded face and significant features are selected by using self-learning chicken swarm optimization, it minimizes the feature vector is given into the Hybrid HCNN and LSTM model for efficiently identifying the facial expression. Experimental results demonstrate the efficiency of the proposed work in terms of accuracy, precision, recall and f-measure.

Artificial Bee Colony (ABC) algorithm is a meta-heuristic algorithm, is inspired by the bee’s food search behaviour based on swarm intelligence. Successful applications were performed on many optimization problems using this algorithm rising in popularity over the past few years. The update mechanism of the ABC algorithm, despite the fact that its exploration is good, faces the problem of convergence performance. For solving convergence problem of ABC Algorithm An Artificial Bee Colony Algorithm Based Fitness Weighted Search (ABCFWS) algorithm proposed in this paper. In this approach, an intelligent search space is proposed instead of the random search space of the ABC algorithm. In this method, the fitness values of the food source and the selected neighbour food source are taken as weights and a more balanced search space was found in the direction of the food source with better fitness value. The proposed method has been applied to 28 unconstrained numerical optimization test problems with different characteristics and the results were compared with the ABC algorithm variations. The results show that the proposed method is successful and competitive.

The extraction of the optimal mode of the bearing signal in the drive system of a corn harvester is a challenging task. In addition, the accuracy and robustness of the fault diagnosis model are low. Therefore, this paper proposes a fault diagnosis method that uses the optimal mode component as the input feature. The vibration signal is first decomposed by variational mode decomposition (VMD) based on the optimal parameters searched by the artificial bee colony (ABC). Moreover, the key components are screened using an evaluation function that is a fusion of the arrangement entropy, the signal-to-noise ratio, and the power spectral density weighting. The Stockwell transform is then used to convert the filtered modal components into time–frequency images. Finally, the MBConv quantity and activation function of the EfficientNet network are optimized, and the time–frequency pictures are imported into the optimized network model for fault diagnosis. The comparative experiments show that the proposed method accurately extracts the optimal modal component and has a fault classification accuracy greater than 98%.

The N-puzzle problem is one of the most classical problems in mathematics. Since the number of states in the N-puzzle is equal to the factorial of the number of tiles, traditional algorithms can only provide solutions for small-scale ones, such as 8-puzzle. Various uninformed and informed search algorithms have been applied to solve the N-puzzle, and their performances have been evaluated. Apart from traditional methods, artificial intelligence algorithms are also used for solutions. This paper introduces a new approach based on a meta-heuristic algorithm with a solving of the 15-puzzle problem. Generally, only Manhattan distance is used as the heuristic function, while in this study, a linear conflict function is used to increase the effectiveness of the heuristic function. Besides, the puzzle was divided into subsets named pattern database, and solutions were obtained for the subsets separately with the artificial bee colony (ABC) algorithm. The proposed approach reveals that the ABC algorithm is very successful in solving the 15-puzzle problem.

In order to solve the design problem of electric vehicle charging station distribution, based on the consideration of user and investor costs, this paper establishes a mixed integer model for charging station site selection based on game theory ideas. Among them, the user cost is determined by two indicators, namely, the cost of time for users to reach the charging station and the cost of time for users to wait in line, while the cost of the charging station is determined by the construction cost and the daily operation and maintenance cost. In the established model, the hierarchical analysis is used to minimize the combined cost of users and charging stations as the objective. In addition, an improved artificial bee colony algorithm is designed to solve the model. The improved algorithm adds a neighborhood search method and a feasible decoding scheme to the honey bee harvesting and tracking process, thus solving the problems of low search accuracy, poor convergence, and inability to directly calculate the mixed integer model of the original algorithm. Simulation results show that the improved artificial bee colony algorithm can effectively solve the mixed integer model and has higher search accuracy and convergence speed compared with the traditional method. By applying the algorithm to solve the siting model, the location and number of charging stations can be clearly planned, thus improving charging efficiency and reliability.

A hybrid artificial bee colony (HABC) solved the multiobjective low-carbon flexible job-shop scheduling problem (MLFJSP) was proposed in the paper. HABC algorithm uses a two-layer coding method to establish the initial population as the nectar source for the employed bees. In the optimization process, the employed bee phase and the onlooker bee phase adopt improved crossover mutation strategies and adaptive neighborhood search strategies to generate new nectar sources, and the greedy method is used to retain better solutions. The scout bee update mechanism prevents the algorithm from falling into a local optimum and enhances the convergence of the algorithm. In order to prevent the loss of the optimal solution, the optimization results of each phase are saved in the Pareto archive (PA). Finally, two sets of international standard instances are used to carry out simulation experiments. The simulation results demonstrated that HABC algorithm is an effective algorithm to solve the multiobjective low-carbon flexible job shop scheduling problem.

It is very difficult and expensive to replace sensor node battery in wireless sensor network in many critical conditions such as bridge supervising, resource exploration in hostile locations, and wildlife safety, etc. The natural choice in such situations is to maximize network lifetime. One such approach is to divide the sensing area of wireless sensor network into clusters to achieve high energy efficiency and to prolong network lifetime. In this paper, an Artificial Bee Colony Inspired Clustering Solution (ABCICS) is introduced. The proposed protocol selects the head of the cluster with optimal fitness function. The fitness function comprises the residual energy of node, node degree, node centrality, and distance from base station to node. When cluster-head with high energy node transmits the data to the base station, it further minimizes the energy consumption of the sensor network. The presented protocol is compared with LEACH, HSA-PSO, and MHACO-UC. Simulation experiments show the effectiveness of our approach to enhance the network lifetime.

In this paper, we consider the Homicidal Chauffeur (HC) problem as an interesting and practical differential game. At first, we introduce a bilevel optimal control problem (BOCP) and prove that a saddle point solution for this game exists if and only if this BOCP has an optimal solution in which the optimal value of the objective function is equal to $1$. Then, BOCP is discretized and converted to a nonlinear bilevel programming problem. Finally, an Artificial Bee Colony (ABC) algorithm is used for solving this problem, in which the lower-level problem will be considered as a constraint and solved by an NLP-solver. Finally, to demonstrate the effectiveness of the presented method, various cases of HC problem are solved and the simulation results are reported.

An islanded microgrid (IMG) having renewable energy sources (RES), dispatchable sources and battery energy storage (BES) is an effective approach to supply the electricity in remote areas. The reliable operation of IMG is quite complex and challenging task, due to uncertain behaviour of RES, loads and energy bid. In this paper, an IMG is simulated to maximise the revenue considering a dynamic optimisation objective subjected to various constraints of system components for two different cases, i.e. considering the stationary and the dynamic energy bid. Artificial bee colony (ABC) and its variants based on global best (GABC), and global and personal best with asynchronous scaling factors (GPSABC) are implemented and the obtained results are also compared with the existing optimisation techniques. Results of GPSABC improve the revenues and offers better accuracy and stability parameters. Further, the effect on revenue results with different ramp rates of diesel generators is described through GPSABC.

Artificial bee colony algorithm is an effective algorithm for parameter optimization, but the traditional artificial bee colony algorithm is liable to fall into local extreme points at a later stage. In this paper, we propose an improved artificial bee colony optimization algorithm, which solves the problems of premature convergence and falling into the local extreme value in the classification of hyperspectral images. First we use an improved chaotic sequence with higher randomness to initialize and update nectar sources to expand the distribution of nectar sources. Secondly, the optimized adaptive step size is introduced into the neighborhood search to speed up the algorithm convergence and improve the search efficiency. Then we build an improved artificial bee colony algorithm support vector machine optimization model to mine the optimal values of penalty factor C and kernel function parameter σ. Next, the model was used to perform classification experiments on two hyperspectral images (University of Pavia, Indian Pine) with different attributes, and compared with the traditional bee colony algorithm, genetic algorithm, and particle swarm algorithm. Experimental results on HSI datasets demonstrate the superiority of the proposed method over several well-known methods in both classification accuracy and convergence speed.

In recent years, system maintenance has become a hot topic that has attracted significant research interests from both academia and industry, and has found applications in many areas. A modern system usually consists of many components, among which there may be different dependencies. The maintenance strategy that ignores the dependencies among components cannot meet the actual engineering requirements and may even affect the usability of the entire system. Therefore, in this paper, we study the economic dependence of equipment cost and duration sharing when components are replaced at the same time. By characterizing the relationship between the effective service age and reliability of components using the Weibull distribution, we establish a selective maintenance model of multi-state complex system with economic dependence. The discrete artificial bee algorithm is used as the global optimization method to solve the dual- objective optimization model and obtain the Pareto solution set of the maintenance level of each component. A case study of the Shooman network system is performed to demonstrate the usefulness of the model and the importance of considering the economic dependencies between components in maintenance decisions.

Lifetime is one of the most critical indexes of the Wireless Sensor Network (WSN). In this paper, we propose a clustering protocol based on the meta-heuristic approach (CPMA). CPMA takes the network lifetime as the primary consideration and consists of two parts. The first part focuses on the online cluster head selection and network communication coordination. The selection is based on the Harmony Search (HS) Algorithm, which aims to reduce the total energy dissipation and smooth the energy distribution throughout the network. Currently, most clustering protocols cannot automatically tune the corresponding protocol parameters according to the diversity of different WSNs. To solve such issue, the second part of CPMA uses the Artificial Bee Colony (ABC) algorithm to optimize its crucial parameters. The optimization is offline and will be executed only once before the network is working. We make a detailed comparison of CPMA with classical clustering protocols. The results show that CPMA can better prolong the network lifetime and improve network throughput under almost all the network conditions. Furthermore, our simulation also exhibits that CPMA has good adaptability and performs well under different network lifetime definitions. All the results prove that CPMA has the advantages of being suitable and efficient for a wide number of WSN applications.

Cloud computing is a modern technology for dealing with large-scale data. The Cloud has been used to process the selection and placement of replications on a large scale. Most previous studies concerning replication used mathematical models, and few studies focused on artificial intelligence (AI). The Artificial Bee Colony (ABC) is a member of the family of swarm intelligence based algorithms. It simulates bee direction to the final route and has been proven to be effective for optimization. In this paper, we present the different costs and shortest route sides in the Cloud with regard to replication and its placement between data centers (DCs) through Multi-Objective Optimization (MOO) and evaluate the cost distance by using the knapsack problem. ABC has been used to solve shortest route and lower cost problems to identify the best selection for replication placement, according to the distance or shortest routes and lower costs that the knapsack approach has used to solve these problems. Multi-objective optimization with the artificial bee colony (MOABC) algorithm can be used to achieve highest efficiency and lowest costs in the proposed system. MOABC can find an optimal solution for the best placement of data replicas according to the minimum distance and the number of data transmissions, affording low cost with the knapsack approach and availability of data replication.Low cost and fast access are characteristics that guide the shortest route in the CloudSim implementation as well. The experimental results show that the proposed MOABC is more efficient and effective for the best placement of replications than compared algorithms.

Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) are traditionally optimised independently. However recently, integrated ASP and ALB optimisation has become more relevant to obtain better quality solution and to reduce time to market. Despite many optimisation algorithms that were proposed to optimise this problem, the existing researches on this problem were limited to Evolutionary Algorithm (EA), Ant Colony Optimisation (ACO), and Particle Swarm Optimisation (PSO). This paper proposed a modified Artificial Bee Colony algorithm (MABC) to optimise the integrated ASP and ALB problem. The proposed algorithm adopts beewolves predatory concept from Grey Wolf Optimiser to improve the exploitation ability in Artificial Bee Colony (ABC) algorithm. The proposed MABC was tested with a set of benchmark problems. The results indicated that the MABC outperformed the comparison algorithms in 91% of the benchmark problems. Furthermore, a statistical test reported that the MABC had significant performances in 80% of the cases.

Abstract Unmanned aerial vehicles (UAVs) in wireless communication have received significant interests recently due to its low cost and flexibility in providing wireless connectivity in areas without infrastructure coverage. In this paper, we optimize the location of UAVs to maximize their coverage areas. We propose a modified artificial bee colony (MABC) incorporating bee number reallocation and a new search equation. In the proposed algorithm, a greater number of bees in the population are assigned to execute local searches near food sources to enhance solution accuracy, and the bees are guided by elite vectors to enable the algorithm to rapidly converge to a potentially globally optimal position. The deployment of UAVs is thus obtained. The experimental results show that the proposed algorithm achieves great improvements over the traditional algorithm.

Voltage collapse tends to occur due to the voltage instability created during large faults. As a last resort, under-voltage load shedding (UVLS) is performed after all the available power operation and control mechanisms have been exhausted. Load shedding techniques have advanced from the conventional and adaptive methods that are less optimal compared to computational intelligence-based techniques. Recent works have identified hybrid algorithms to give more optimal solutions for UVLS problems with multi-objective functions. In this paper, a novel hybrid ABC-PSO algorithm, adapted from a software estimation project, is used to perform UVLS on a modified IEEE 14-bus system. Eight overload conditions are imposed on the system ranging from 105% to 140% loading, where FVSI ranking is used in identifying weak buses. The load shedding is then performed following decentralized relay settings of 3.5 seconds, 5 seconds and 8 seconds, which gives an overall 99.32% recovery of voltage profiles. The proposed hybrid ABC-PSO algorithm is able to shed optimal amounts of load, giving an 89.56% post-contingency load, compared to GA's 77.04%, ABC-ANN at 84.03% and PSO-ANN at 80.96%. This study has been simulated on MATLAB software, using the Power System Analysis Toolbox (PSAT) graphical user and command-line interfaces.

This paper presents an adaptive Fractional Order PID (FOPID) controller for improving the performance of a Brushless DC (BLDC) motor using Artificial Bee Colony (ABC) algorithm. BLDC motor is desired to operate at various speed and load conditions with enhanced performance and robust speed control. In practice, the effect of longer settling time, fluctuation of steady-state error, power fluctuation and nonlinearity characteristics of the BLDC motor drive result in poor controllability. To overcome the problems, an optimized FOPID controller using the ABC algorithm in a self-tuned regulator structure is proposed to minimize the given objective function to satisfy the inequality constraints. It is also interesting to note that the usage of Hall Effect sensors has many limitations due to the failure of its components, poor reliability, need special mechanical arrangements for mounting and electrical noise aspects. In order to avoid such issues, a Kalman Filter is designed for estimating the speed of the motor. The simulation is carried out for the proposed ABC tuned FOPID controller and the results are compared with conventional genetic algorithm and modified genetic algorithm tuned FOPID controllers. The results indicate that the proposed ABC tuned controller is superior in terms of time-domain characteristics, control effort, and specified performance indices. Further to show the usefulness of the proposed method, an experimental model is developed and validated for the selected operating conditions with the required comparison.

With the tremendous growth of information in the web, the classic query processing approaches are unable to respond to queries in real time. The aim of this paper is to develop an innovative tool using swarm intelligence to address information retrieval in the context of response time and solution quality through cope with the complexity induced by that huge volume of information. In this paper, we will show that our proposed approach that use of Artificial Bee Colony (ABC) algorithm called MABC can be another alternative to palliate the complexity issue in terms of response time while it produces a solution quality is relatively convergent or even better. Experimental tests have been conducted on two well-known CACM and NPL collections. Both are different in size, CACM is small while NPL is relatively large. Numerical results exhibit the superiority and the benefit gained from using the MABC approach instead of the classic approaches.

Clustering using fuzzy C-means (FCM) is a soft segmentation method that has been extensively investigated and successfully implemented in image segmentation. FCM is useful in various aspects, such as the segmentation of grayscale images. However, FCM has some limitations in terms of its selection of the initial cluster center. It can be easily trapped into local optima and is sensitive to noise, which is considered the most challenging issue in the FCM clustering algorithm. This paper proposes an approach to solve FCM problems in two phases. Firstly, to improve the balance between the exploration and exploitation of improved global best-guided artificial bee colony algorithm (IABC). This is achieved using a new search probability model called PIABC that improves the exploration process by choosing the best source of food which directly affects the exploitation process in IABC. Secondly, the fuzzy clustering algorithm based on PIABC, abbreviated as PIABC-FCM, uses the balancing of PIABC to avoid getting stuck into local optima while searching for the best solution having a set of cluster center locations of FCM. The proposed method was evaluated using grayscale images. The performance of the proposed approach shows promising outcomes when compared with other related works.

With the rapid development of internet technology, the amount of collected or generated data has increased exponentially. The sheer volume, complexity, and unbalanced nature of this data pose a challenge to the scientific community to extract meaningful information from this data within a reasonable time. In this paper, we implemented a scalable design of an artificial bee colony for big data classification using Apache Spark. In addition, a new fitness function is proposed to handle unbalanced data. Two experiments were performed using the real unbalanced datasets to assess the performance and scalability of our proposed algorithm. The performance results reveal that our proposed fitness function can efficiently deal with unbalanced datasets and statistically outperforms the existing fitness function in terms of G-mean and F1-Score. In additon, the scalability results demonstrate that our proposed Spark-based design obtained outstanding speedup and scaleup results that are very close to optimal. In addition, our Spark-based design scales efficiently with increasing data size.

In most wireless sensor networks (WSN), multi-hop routing algorithm is used to transmit the data collected by sensors to user. Multi-hop forwarding leads to energy hole problem and high transmission overhead in large scale WSN. In order to address these problems, this paper proposes multiple mobile sink based data collection algorithm, which introduces energy balanced clustering and Artificial Bee Colony based data collection. The cluster head election is based on the residual energy of the node. In this study, we focused on a large-scale and intensive WSN which allows a certain amount of data latency by investigating mobile Sink balance from three aspects: data collection maximization, mobile path length minimization, and network reliability optimization. Simulation results show that, in comparison with other algorithms such Random walk and Ant Colony Optimization, the proposed algorithm can effectively reduce data transmission, save energy, improve network data collection efficiency and reliability, and extend the network lifetime.

Abstract In industrial Internet, many intelligent applications are implemented based on data collection and distribution. Data collection and data distribution in the wireless sensor networks are very important, where the node topology can be described by the spanning tree for obtaining an efficient transmission. Classical algorithms in graph theory such as the Kruskal algorithm or Prim algorithm can only find the minimum spanning tree (MST) in industrial wireless sensor networks. Swarm intelligence algorithm can obtain multiple solutions in one calculation. Multiple solutions are very helpful for improving the reliability of industrial wireless sensor networks. In this paper, we combine quantum computing with artificial bee colony and design a spanning tree construction algorithm for industrial wireless sensor networks. Quantum computations are introduced into the onlooker bees search. Food source replacement strategy is improved. Finally, the algorithm is simulated and evaluated. The results show that the new proposed algorithm can obtain more alternative solutions and has a better performance in search efficiency.

According to underground construction development and its high cost process, an accurate assessment and prevention of probable risks are of significant importance. Different methods have been developed to assess underground constructions. In this paper, the aim is to develop a new soft computing model to evaluate tunnel support systems. Firstly, a numerical analysis was performed using the explicit finite difference model by FLAC2D software to excavate a sequence model and support system installation. The design loads including the axial force, moment, and shear force were calculated for some important points of the support system including the crown, the middle of the bottom and the side walls. In order to analyse the stability of the support system, the section points were evaluated into 3 clusters by the artificial bee colony as a meta-heuristic algorithm and a k-means algorithm using Matlab software. The results of clustering were compared by the safety factor of the support system. The results indicated that the section points that are in cluster 1 have a lower safety factor than clusters 3 and 2, respectively. It concluded that the artificial bee colony can be reliably used in the initial assessment of tunnel support systems based on the axial force, moment, and shear force.

The Internet of Things (IoT) provides a beautiful and intelligent landscape for humanity's future. The connections of various sensors and devices in the IoT result in a large consumption of energy. Therefore, research on energy saving and energy efficient methods is imperative. For wireless sensors in an IoT network, sustainable operation in an energy efficient manner is essential due to the limited battery capacity of sensors. This paper attempts to study an IoT network containing wireless sensors and base stations. Wireless power transfer techniques for supplying battery charging are becoming increasingly mature. For wireless sensors, a charging vehicle is responsible for the electrical power supply. To save electrical energy, data transfer of the discussed IoT network scenario is expressed as a minimization problem. A three-stage method is proposed to handle the optimization problem. A restart artificial bee colony (RABC) method is proposed to solve the subproblems of the data transfer model. It is proved that the RABC method asymptotically converges to the optimal solution of the problem. Numerical simulations show that energy consumption in the studied network scenario can be minimized using the proposed method with a good, robust property.

This research work presents a modified sine-cosine optimized maximum power point tracking (MPPT) algorithm for grid integration. The developed algorithm provides the maximum power extraction from a photovoltaic (PV) panel and simplified implementation with a benefit of high convergence velocity. Moreover, the performance and ability of the modified sine-cosine optimized (MSCO) algorithm is equated with recent particle swarm optimization and artificial bee colony algorithms for comparative observation. Practical responses is analyzed under steady state, dynamic, and partial shading conditions by using dSPACE real controlling board laboratory scale hardware implementation. The MSCO-based MPPT algorithm always shows fast convergence rate, easy implementation, less computational burden and the accuracy to track the optimal PV power under varying weather conditions. The experimental results provided in this paper clearly show the validation of the proposed algorithm.

The artificial bee colony (ABC) algorithm is well known and widely used optimization method based on swarm intelligence, and it is inspired by the behavior of honeybees searching for a high amount of nectar from the flower. However, this algorithm has not been exploited sufficiently. This research paper proposes a novel method to analyze the exploration and exploitation of ABC. In ABC, the scout bee searches for a source of random food for exploitation. Along with random search, the scout bee is guided by a modified genetic algorithm approach to locate a food source with a high nectar value. The proposed algorithm is applied for the design of a nonlinear controller for a continuously stirred tank reactor (CSTR). The statistical analysis of the results confirms that the proposed modified hybrid artificial bee colony (HMABC) achieves consistently better performance than the traditional ABC algorithm. The results are compared with conventional ABC and nonlinear PID (NLPID) to show the superiority of the proposed algorithm. The performance of the HMABC algorithm-based controller is competitive with other state-of-the-art meta-heuristic algorithm-based controllers in the literature.

In the present paper, a tool is proposed to optimally schedule the charging requests of a fleet of carsharing Electric Vehicles (EVs) in an urban area, to enable their participation in the Ancillary Service Market. The centralized scheduler minimizes the imbalance of an EV fleet with respect to the power commitment declared in the Day-Ahead Market, providing also tertiary reserve and power balance control to the grid. The regulation is carried out by optimizing the initial charging time of each vehicle, according to a deadline set by the carsharing operator. To this purpose, a nature-inspired optimization is adopted, implementing innovative hybridizations of the Artificial Bee Colony algorithm. The e-mobility usage is simulated through a topology-aware stochastic model based on carsharing usage in Milan (Italy) and the Ancillary Services requests are modeled by real data from the Italian electricity market. The numerical simulations performed confirmed the effectiveness of the approach in identifying a suitable schedule for the charging requests of a large EV fleet (up to 3200 units), with acceptable computational effort. The benefits on the economic sustainability of the E-carsharing fleet given by the participation in the electricity market are also confirmed by an extensive sensitivity analysis.