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Complex systems are large scale and involve numerous uncertainties, which means that such systems tend to be expensive to operate. Further, it is difficult to analyze systems of this kind in a real environment, and for this reason agent-based modeling and simulation techniques are used instead. Based on estimation methods, modeling and simulation techniques establish an output set against the existing input set. However, as the data set in a given complex systems becomes very large, it becomes impossible to use estimation methods to create the output set desired. Therefore, a new mechanism is needed to optimize data sets in this context. In this paper, the adaptive modified artificial bee colony algorithm is shown to be successful in optimizing the numerical test function and complex system parameter data sets. Moreover, the results show that this algorithm can be successfully adapted to a given problem. Specifically, this algorithm can be more successful in optimizing problem solving than either the artificial bee colony algorithm or the modified artificial bee colony algorithm. The adaptive modified artificial bee colony algorithm performs a search in response to feedback received from the simulation in run-time. Because of its adaptability, the adaptive modified artificial bee colony algorithm is of great importance for its ability to find solutions to multiple kinds of problems across numerous fields. [ABSTRACT FROM AUTHOR]

In this study, apple images taken with near-infrared (NIR) cameras were classified as bruised and healthy objects using iterative thresholding approaches based on artificial bee colony (ABC) and particle swarm optimization (PSO) algorithms supported by a convolutional neural network (CNN) deep learning model. The proposed model includes the following stages: image acquisition, image preprocessing, the segmentation of anatomical regions (stemcalyx regions) to be discarded, the detection of bruised areas on the apple images, and their classification. For this aim, by using the image acquisition platform with a NIR camera, a total of 1200 images at 6 different angles were taken from 200 apples, of which 100 were bruised and 100 healthy. In order to increase the success of detection and classification, adaptive histogram equalization (AHE), edge detection, and morphological operations were applied to the images in the preprocessing stage, respectively. First, in order to segment and discard the stem-calyx anatomical regions of the images, the CNN model was trained by using the preprocessed images. Second, the threshold value was determined by means of the ABC/PSO-based iterative thresholding approach on the images whose stem-calyx regions were discarded, and then the bruised areas on the images with no stem-calyx anatomical regions were detected by using the determined threshold value. Finally, the apple images were classified as bruised and healthy objects by using this threshold value. In order to illustrate the classification success of our approaches, the same classification experiments were reimplemented by directly using the CNN model alone on the preprocessed images with no ABC and PSO approaches. Experimental results showed that the hybrid model proposed in this paper was more successful than the CNN model in which ABCand PSO-based iterative threshold approaches were not used. [ABSTRACT FROM AUTHOR]

Multilabel classification is a supervised learning problem wherein each individual instance is associated with multiple labels. Ensemble methods are effective in managing multilabel classification problems by creating a set of accurate, diverse classifiers and then combining their outputs to produce classifications. This paper presents a novel stacking-based ensemble algorithm, ABC-based stacking, for multilabel classification. The artificial bee colony algorithm, along with a single-layer artificial neural network, is used to find suitable meta-level classifier configurations. The optimization goal of the meta-level classifier is to maximize the average accuracy of classification of all the instances involved. We run an experiment on 10 benchmark datasets of varying domains and compare the proposed approach to five other ensemble algorithms to demonstrate the feasibility and effectiveness of ABC-based stacking. [ABSTRACT FROM AUTHOR]

Collaborative filtering is one of the widely adopted approaches in recommender systems used for e-commerce applications, stating that users having similar tastes will have similar preferences in the future. The literature presents a number of similarity metrics such as the extended Jaccard coefficient to quantify these preference similarities. This paper aims to improve prediction accuracy by optimizing the similarity values computed using these metrics by adopting two biologically inspired approaches, namely artificial bee colony and genetic algorithms, with a bottom-up approach, suggesting that any improvement on a single-user basis will reflect on the overall prediction accuracy. Detailed statistical analysis was performed using the t-test, analysis of variance, and McNemar's test to see whether there were performance differences. The results show that statistically significant differences exist with high confidence levels. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) technology enables physical devices to communicate with each other for preparing, gathering, and sharing hazard warnings or critical information without human intervention. With respect to emergency applications of IoT technology, an essential issue is to provide an efficient and robust scheme for data gathering. The proposed solution in the existing approaches is to construct a spanning tree over the IoT devices and collect data using the tree. The shortcoming of these algorithms is that they do not take into account the probability of device mobility or failure. In such cases, the spanning tree is split, and it becomes impossible to deliver critical data to the base station on time. In this paper, we propose a reliable spanning tree construction algorithm, which is called reliable spanning tree construction in IoT (RST-IoT). Our algorithm utilizes the artificial bee colony algorithm to generate proper trees. In this method, hop count distances of the devices from the base station, residual energies of the devices, and their mobility probabilities are considered to measure the appropriateness of the trees. Moreover, the proposed algorithm generates a number of trees instead of a single one. These trees are arranged according to their preferences and used for data gathering in succession. Each tree is employed for data gathering upon splitting the preceding one. The simulation results show that RST-IoT improves the reliability of data gathering in emergency applications compared to the previous approaches. [ABSTRACT FROM AUTHOR]

As the usage and development of wireless sensor networks increases, problems related to these networks are being discovered. Dynamic deployment is one of the main issues that directly affect the performance of wireless sensor networks. In this paper, an artificial bee colony algorithm is applied to the dynamic deployment of mobile sensor networks to gain better performance by trying to increase the coverage area of the network. The good performance of the algorithm shows that it can be utilized in the dynamic deployment of wireless sensor networks. [ABSTRACT FROM AUTHOR]

Distributed generation (DG) is widely used to minimize total power losses in distribution networks. However, one of the problems of DG in a grid system is reverse current ow (RCF), which is when the DG output becomes greater than the connected load. Therefore, this paper proposes a multiobjective artificial bee colony (MOABC) algorithm to determine the optimal network reconfiguration for reducing total RCF in DG. The proposed algorithm is tested on 33-bus radial distribution systems in two different scenarios, i.e. base case and with 50% load. The proposed technique can reduce reverse current by up to 93%; however, the total power loss in the system will increase by 7%. Therefore, a suitable weight value is needed in MOABC for balancing the effect of RCF and the power loss value. [ABSTRACT FROM AUTHOR]

In this paper a simple and efficient heuristic search method based on the artificial bee colony (ABC) algorithm is presented and used for the optimal power ow (OPF) problem in power systems with static VAR compensator (SVC) devices. The total generation cost of a power system with SVC devices (which improve the voltage stability at load buses) is optimally minimized with the use of ABC. The ABC, which is based on the foraging behavior of honey bees searching for the best food source, is a recently proposed optimization algorithm. The performance of the presented ABC algorithm was tested and verified on the IEEE 11-bus and IEEE 30-bus power systems by comparing it with several other optimization methods. Furthermore, ABC is used not only for optimizing the total generation cost and active power loss, but also for improving the voltage stability of the 22-bus power system in Turkey. Our results illustrate that ABC can successfully be used to solve nonlinear problems related to power systems. [ABSTRACT FROM AUTHOR]

In this paper, the researcher proposes a new evolutionary optimization algorithm that depends on genetic operators such as crossover and mutation, referred to as the bull optimization algorithm (BOA). This new optimization algorithm is called the BOA because the best individual is used to produce offspring individuals. The selection algorithm used in the genetic algorithm (GA) is removed from the proposed algorithm. Instead of the selection algorithm, individuals initially produced attempt to achieve better individuals. In the proposed method, crossover operation is always performed by using the best individual. The mutation process is carried out by using individual positions. In other words, individuals are converged to the best individuals by using crossover operation, which aims to get the individual that is the better than the best individual in the mutation stage. The proposed algorithm is tested using 50 large continuous benchmark test functions with different characteristics. The results obtained from the proposed algorithm are compared with those of the GA, particle swarm optimization (PSO), differential evolution (DE), and the artificial bee colony (ABC) algorithm. The BOA, ABC, DE, PSO, and GA provided either optimum results or better results than other optimization algorithms in 42, 38, 34, 25s and 17 benchmark functions, respectively. According to the test results, the proposed BOA provided better results than the optimization algorithms that are most commonly used in solving continuous optimization problems. [ABSTRACT FROM AUTHOR]

In this paper, an auto-tuned fuzzy load frequency controller (FLFC)-based artificial bee colony (ABC) algorithm is developed to quench the deviations in the frequency and tie-line power due to load disturbances in an interconnected power system. Optimal tuning of membership functions (MFs) and fuzzy control rules is very important to improve the design performance and achieve a satisfactory level of robustness for a particular operation. In this work, to reduce the fuzzy system design effort and take large parametric uncertainties into account, a new systematic and simultaneous tuning method is developed for designing MFs and fuzzy rules. For this, the designing problem is restructured as an optimization problem and the ABC algorithm is employed to solve it. This newly developed method provides some advantages such as a flexible controller with a simple structure and easy algorithm. For the purpose of the proposed method's evaluation, the designed controller is applied to a 2-area power system with considerations regarding governor saturation and the results are compared to the one obtained by a classic proportional-integral controller. Simulation results show better operation and improved system parameters, such as the settling time and step response rise time, using the proposed approach, in the presence of system parameter variations. [ABSTRACT FROM AUTHOR]

The artificial bee colony (ABC) algorithm, which was inspired by the foraging and dance behaviors of real honey bee colonies, was first introduced for solving numerical optimization problems. When the solution space of the optimization problem is binary-structured, the basic ABC algorithm should be modified for solving this class of problems. In this study, we propose XOR-based modification for the solution-updating equation of the ABC algorithm in order to solve binary optimization problems. The proposed method, named binary ABC (binABC), is examined on an uncapacitated facility location problem, which is a pure binary optimization problem, and the results obtained by the binABC are compared with results obtained by binary particle swarm optimization (BPSO), the discrete ABC (DisABC) algorithm, and improved BPSO (IBPSO). The experimental results show that binABC is an alternative tool for solving binary optimization problems and is a competitive algorithm when compared with BPSO, DisABC, and IBPSO in terms of solution quality, robustness, and simplicity. [ABSTRACT FROM AUTHOR]

This paper presents a solution for the emission-controlled economic dispatch (ECED) problem of medium- sized power systems via an artificial bee colony algorithm. The ECED problem, which accounts for the minimization of both the fuel cost and the emission, is a multiple objective function problem. The objective is to minimize the total fuel cost of the generation and environmental pollution caused by fossil-based thermal generating units and to also maintain an acceptable system performance in terms of the limits on the generator's real and reactive power outputs, bus voltages, shunt capacitors/reactors, and power flow of transmission lines. The proposed algorithm is validated on an IEEE 30-bus system with 6 generating units. The results of the proposed technique are compared with that of the particle swarm optimization technique. The proposed approach is also tested on the Algerian 59-bus network and compared with global optimization methods (fuzzy genetic algorithm and ant colony optimization). The results show that the approach proposed can converge to a near solution and obtain a competitive solution in a critical situation and within a reasonable time. [ABSTRACT FROM AUTHOR]

Infinite impulse response (IIR) system identification problem is defined as an IIR filter modeling to represent an unknown system. During a modeling task, unknown system parameters are estimated by metaheuristic algorithms through the IIR filter. This work deals with the self-adaptive search-equation-based artificial bee colony (SSEABC) algorithm that is adapted to optimal IIR filter design. SSEABC algorithm is a recent and improved variant of artificial bee colony (ABC) algorithm in which appropriate search equation is determined with a self-adaptive strategy. Moreover, the success of the SSEABC algorithm enhanced with a competitive local search selection strategy was proved on benchmark functions in our previous studies. The SSEABC algorithm is utilized in filter modelings which have different cases. In order to demonstrate the performance of the SSEABC algorithm on IIR filter design, we have also used canonical ABC, modified ABC (MABC), best neighbor-guided ABC, and an ABC with an adaptive population size (APABC) algorithms as well as other algorithms in the literature for comparison. The obtained results and the analysis on performance evolution of compared algorithms on several filter design cases indicate that SSEABC outperforms all considered ABC variants and other algorithms in the literature. [ABSTRACT FROM AUTHOR]