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In this paper, an efficient face recognition method using AGA and ANFIS-ABC has been proposed. At first stage, the face images gathered from the database are preprocessed. At Second stage, an interest point which is used to improve the detection rate consequently. The parameters used in the interest point determination are optimized using the Adaptive Genetic Algorithm. Finally using ANFIS, face images are classified by using extracted features. During the training process, the parameters of ANFIS are optimized using Artificial Bee Colony Algorithm (ABC) in order to improve the accuracy. The performance of the proposed ANFIS-ABC technique is evaluated using an ORL database with 400 images of 40 individuals, YALE-B database with 165 images of 15 individuals and finally with real time video the detection rate and false alarm rate is compared with proposed and existing methods to prove the system efficiency.

Artificial bee colony (ABC) algorithm is an efficient biological-inspired optimization method, which mimics the foraging behavior of honey bees to solve the complex and nonlinear optimization problems. However, in some cases, it suffers from inefficient exploration, low exploitation and slow convergence rate. These shortcomings cause the problem of stagnation at local optimum which is dangerous in determining the true solution (optima) of the problem. Therefore, in the present paper, an attempt has been made toward the removal of the drawbacks from the classical ABC by proposing a novel hybrid method called SCABC algorithm. The SCABC algorithm hybridizes the ABC with sine cosine algorithm (SCA) to upgrade the level of exploitation and exploration in the classical ABC algorithm. The SCA is a recently introduced algorithm, which uses the trigonometric functions sine and cosine to perform the search. The validation of the SCABC algorithm is performed on a well-known benchmark set of 23 optimization problems. The various analysis metrics such as statistical, convergence and performance index analysis verify the better search ability of the SCABC as compared to classical ABC, SCA. The comparison with some other optimization algorithms demonstrates a comparatively better state of exploitation and exploration in the SCABC algorithm. Moreover, the SCABC is also employed on multilevel thresholding problems. The various performance measures demonstrate the efficacy of the SCABC algorithm in determining the optimal thresholds of gray images.

The indirect and accurate determination of blast-induced rock movement has important significance in the reduction of ore loss and dilution and in the protection of environment. The present paper aims to predict blast-induced rock movement resulting from the Husab Uranium Mine, Namibia, the Coeur Rochester Mine, USA, and the Phoenix Mine, USA, and three new hybrid models using a genetic algorithm (GA), an artificial bee colony algorithm (ABC), a cuckoo search algorithm (CS) and support vector regression (SVR), namely the GA-SVR, ABC-SVR and CS-SVR models, are proposed. Eight typical blasting parameters rock type, number of free faces, first centerline distance, hole diameter, power factor, spacing, subdrill and initial depth of monitoring were chosen as the input variables to establish the intelligent model, and horizontal blast-induced rock movement (MH) was the output variable after conducting the available analyses of the database. Three performance metrics, including the correlation coefficient (R2), mean square error and variance account for, were used to assess the predictive performances of the aforementioned models. Based on the obtained results, the performance metrics show that the GA-SVR, ABC-SVR and CS-SVR model can provide satisfactory performance in estimating blast-induced rock movement, and GA-SVR model can achieve better results than the GWO-SVR, CS-SVR and ANN models when considering both predictive performance and calculation speed.Article HighlightsThree new hybrid predictive models are proposed (GA-SVR, ABC-SVR and CS-SVR).An more convenient, easily operable and higher accuracy predictive method for blast-induced rock movement determination is presented.The GA-SVR model can provide a higher performance capacity when considering both the predictive performance and the calculation speed.

In this paper, we propose efficient methods for the reconstruction, compression, compressive sensing (CS) and encryption of 1D signals. The proposed reconstruction method is based on the use of Charlier moments (CMs) and the Artificial Bee Colony (ABC) algorithm. The latter is used for optimizing the local parameter of Charlier polynomials during the computation of CMs. In addition, new methods are presented for 1D signal compression and CS using CMs and ABC algorithm that guarantees a high quality of the decompressed/reconstructed signal. Moreover, we suggest a new signal encryption/decryption scheme relying on fractional-order Charlier moments and ABC algorithm, which is used for providing a high quality of the decrypted signal and for improving the security of the proposed scheme. The results of the conducted simulations and comparisons clearly show the efficiency of the proposed 1D-signal analysis methods.

In today's context, due to the extensive construction projects, there is a surging demand for building stones. Within quarry-based processing facilities, a pivotal aspect influencing the production of these building stones pertains to evaluating the performance of band saw machines, particularly concerning the cutting of these stones. In this context, Maximum Electric Current (MEC) emerges as a critical variable. To identify this crucial factor, it necessitates a comprehensive grasp of the inherent properties of the stone since it profoundly influences costs, equipment depreciation, and production rates. Estimating the MEC poses numerous challenges and complications due to the uncertainty inherent in geological and geotechnical parameters at each point. Conventional and traditional methods, such as numerical, experimental, analytical, and regression methods, have limitations as they often overlook the uncertainty in rock parameters, leading to the construction of simplistic and non-linear models with simplified assumptions in analytical methods that may lack high accuracy. Consequently, this article employs intelligent methods to overcome these challenges and achieve an optimal solution with high accuracy. Using intelligent methods, it becomes possible to create complex and non-linear models efficiently, minimizing both time and cost. Consequently, this study addresses these challenges by employing two optimization algorithms: Artificial Bee Colony (ABC) and Sine Cosine (SC) Algorithms to estimate MEC specifically in quarry operations. In pursuit of this objective, 120 test samples drawn from 12 distinct types of carbonate rocks obtained from a marble factory in the Mahalat region of Iran were utilized. The considered input parameters encompassed Young's modulus, Mohs hardness, uniaxial compressive strength ( UCS ), production rate and F-Schimazek abrasion factors. The dataset was partitioned, allocating 80% (70 data points) for model development and reserving 20% (18 data points) for model validation. The analysis of modeling outcomes involved three statistical criteria: squared correlation coefficient, mean square error, and root mean square error. The results revealed that the developed model demonstrates a high level of accuracy and minimal error, closely approximating real values. Hence, it can serve as a valuable tool for engineers engaged in the field of rock engineering. In a final step, to assess sensitivity and evaluate the model's output, the @RISK software was employed. The analyses unveiled that among the input parameters within the quarry context, UCS exerts the most substantial influence on the model's output. Even slight variations in UCS can lead to significant alterations in MEC within quarry operations.

To solve the problem of inserting new job into flexible job-shops, this paper proposes a dynamic self-learning artificial bee colony (DSLABC) optimization algorithm to solve dynamic flexible job-shop scheduling problem (DFJSP). Through the reasonable arrangement of the processing sequence of the jobs and the corresponding relationship between the operations and the machines, the makespan can be shortened, the economic benefit of the job-shop and the utilization rate of the processing machine can be improved. Firstly, the Q-learning algorithm and the traditional artificial bee colony (ABC) algorithm are combined to form the self-learning artificial bee colony (SLABC) algorithm. Using the learning characteristics of the Q-learning algorithm, the update dimension of each iteration of the ABC algorithm can be dynamically adjusted, which improves the convergence accuracy of the ABC algorithm. Secondly, the specific method of dynamic scheduling is determined, and the DSLABC algorithm is proposed. When a new job is inserted, the new job and the operations that have not started processing will be rescheduled. Finally, through solving the Brandimarte instances, it is proved that the convergence accuracy of the SLABC algorithm is higher than that of other optimization algorithms, and the effectiveness of the DSLABC algorithm is demonstrated by solving a specific example with a new job inserted.

The mechanical characteristics of rock are greatly influenced by hydrochemical corrosion. The chemical corrosion impact and deformation properties of the meso-pore structure of rock under the action of different hydrochemical solutions for the stability evaluation of rock mass engineering are of high theoretical relevance and applied value. Based on actual data, a support vector machine (SVM) rock constitutive model based on artificial bee colony algorithm (ABC) optimization is constructed in this article. The impact of porosity (chemical deterioration), confining pressure, and other aspects is thoroughly examined. It is used to mimic the triaxial mechanical behavior of rock under various hydration conditions, with high nonlinear prediction ability. Simultaneously, the statistical damage constitutive model and the ABC-SVM constitutive model are used to forecast the sample’s stress–strain curve and compare it to the experimental data. The two models’ correlation coefficients (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE) are computed and examined. The correlation coefficient between the ABC-SVM constitutive model calculation results and the experimental results is found to be larger (R2 = 0.998), and the error is smaller (RMSE = 0.7730, MAPE = 1.51), indicating that it has better prediction performance on the conventional triaxial constitutive relationship of rock. It is a highly promising new way of describing the rock’s constitutive connection.

K-Means Clustering is a popular technique in data analysis and data mining. To remedy the defects of relying on the initialization and converging towards the local minimum in the K-Means Clustering (KMC) algorithm, a chaotic adaptive artificial bee colony algorithm (CAABC) clustering algorithm is presented to optimally partition objects into K clusters in this study. This algorithm adopts the max–min distance product method for initialization. In addition, a new fitness function is adapted to the KMC algorithm. This paper also reports that the iteration abides by the adaptive search strategy, and Fuch chaotic disturbance is added to avoid converging on local optimum. The step length decreases linearly during the iteration. In order to overcome the shortcomings of the classic ABC algorithm, the simulated annealing criterion is introduced to the CAABC. Finally, the confluent algorithm is compared with other stochastic heuristic algorithms on the 20 standard test functions and 11 datasets. The results demonstrate that improvements in CAABA-K-means have an advantage on speed and accuracy of convergence over some conventional algorithms for solving clustering problems.

The objective of this article is twofold. On the one hand, we introduce a cognitively inspired hybridization metaheuristic that combines the strengths of two existing metaheuristics: the artificial bee colony (ABC) algorithm and the dragonfly algorithm (DA). The aim of this hybridization is to reduce the problems of slow convergence and trapping into local optima, by striking a good balance between global and local search components of the constituent algorithms. On the other hand, we use the proposed metaheuristic to train a multi-layer perceptron (MLP) as an alternative to existing traditional- and metaheuristic-based learning algorithms; this is for the purpose of improving overall accuracy by optimizing the set of MLP weights and biases. The proposed hybrid ABC/DA (HAD) algorithm comprises three main components: the static and dynamic swarming behavior phase in DA and two global search phases in ABC. The first one performs global search (DA phase), the second one performs local search (onlooker phase), and the third component implements global search (modified scout bee phase). The resultant metaheuristic optimizer is employed to train an MLP to reach a set of weights and biases that can yield high performance compared to traditional learning algorithms or even other metaheuristic optimizers. The proposed algorithm was first evaluated using 33 benchmark functions to test its performance in numerical optimization problems. Later, using HAD for training MLPs was evaluated against six standard classification datasets. In both cases, the performance of HAD was compared with the performance of several new and old metaheuristic methods from swarm intelligence and evolutionary computing. Experimental results show that HAD algorithm is clearly superior to the standard ABC and DA algorithms, as well as to other well-known algorithms, in terms of achieving the best optimal value, convergence speed, avoiding local minima and accuracy of trained MLPs. The proposed algorithm is a promising metaheuristic technique for general numerical optimization and for training MLPs. Specific applications and use cases are yet to be explored fully but they are supported by the encouraging results in this study.

The purpose of this study is to improve the P-I controllers of the voltage-source converters (VSC)-based multiterminal high voltage direct-current (MT-HVDC) networks. Since the VSCs are the non-linear elements of the MT-HVDC stations, the classical optimization methods, which approximately implement the linear model to optimize the P-I controllers of the VSCs, do not generate optimal results. Therefore, this paper presents a novel technique to optimize the VSC-based MT-HVDC grids’ P-I controllers by embedding the artificial bee colony (ABC) algorithm. The voltage-droop control method is employed at on-shore grids to ensure the active and reactive power balance within MT-HVDC networks. During an evaluation, achieved via a detailed four-terminal MT-HVDC model designed in PSCAD/EMTDC, the improved results obtained under different dynamic situations such as unbalance wind power generation, change in load demand at the on-shore side grids, and eventual VSC disconnection, respectively.