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The NAO robot integrates sensors, vision systems, and control systems. Its monocular vision system is adopted to locate the target object in the three-dimensional space of robots. Firstly, a positioning model based on monocular vision is established according to the principle of small hole perspective. Then, the position coordinates of the target center are obtained in the image coordinate system. In the model mentioned above, the relationship between position coordinates and image coordinates is established at a certain space height. According to this relationship, the two-dimensional coordinates in the image are converted into the three-dimensional coordinates in the robot coordinate system. After getting the target location, we establish the navigation map and find the optimal path under the unknown environment. Based on the simultaneous localization and the mapping (SLAM) theory, the sonar sensor of the NAO robot is used to detect the distance between the robot and the obstacles or between the robot and the end landmark. Moreover, the sonar sensor and the camera are used to distinguish the obstacle and the landmark. After the navigation map is built, the bi-directional parallel search strategy and the simulated annealing algorithm are introduced to improve the traditional artificial bee colony algorithm, and the improved artificial bee colony algorithm is proposed to find an optimal path in the navigation map. Finally, the experimental results show that based on the built environment map, the robot can find an optimal path from the origin to the landmark on the premise of avoiding obstacles.

In 2022, the World Health Organization declared an outbreak of monkeypox, a viral zoonotic disease. With time, the number of infections with this disease began to increase in most countries. A human can contract monkeypox by direct contact with an infected human, or even by contact with animals. In this paper, a diagnostic model for early detection of monkeypox infection based on artificial intelligence methods is proposed. The proposed method is based on training the artificial neural network (ANN) with the adaptive artificial bee colony algorithm for the classification problem. In the study, the ABC algorithm was preferred instead of classical training algorithms for ANN because of its effectiveness in numerical optimization problem solutions. The ABC algorithm consists of food and limit parameters and three procedures: employed, onlooker and scout bee. In the algorithm standard, artificial onlooker bees are produced as much as the number of artificially employed bees and an equal number of limit values are assigned for all food sources. In the advanced adaptive design, different numbers of artificial onlooker bees are used in each cycle, and the limit numbers are updated. For effective exploitation, onlooker bees tend toward more successful solutions than the average fitness value of the solutions, and limit numbers are updated according to the fitness values of the solutions for efficient exploration. The performance of the proposed method was investigated on CEC 2019 test suites as examples of numerical optimization problems. Then, the system was trained and tested on a dataset representing the clinical symptoms of monkeypox infection. The dataset consists of 240 suspected cases, 120 of which are infected and 120 typical cases. The proposed model's results were compared with those of ten other machine learning models trained on the same dataset. The deep learning model achieved the best result with an accuracy of 75%. It was followed by the random forest model with an accuracy of 71.1%, while the proposed model came third with an accuracy of 71%.

Reactive power compensation is one of the practical tools that can be used to improve power systems and reduce costs. These benefits are achieved when the compensators are installed in a suitable place with optimal capacity. This study solves the issues of optimal supply and the purchase of reactive power in the IEEE 30-bus power system, especially when considering voltage stability and reducing total generation and operational costs, including generation costs, reserves, and the installation of reactive power control devices. The modified version of the artificial bee colony (MABC) algorithm is proposed to solve optimization problems and its results are compared with the artificial bee colony (ABC) algorithm, the particle swarm optimization (PSO) algorithm and the genetic algorithm (GA). The simulation results showed that the minimum losses in the power system requires further costs for reactive power compensation. Also, optimization results proved that the proposed MABC algorithm has a lower active power loss, reactive power costs, a better voltage profile and greater stability than the other three algorithms.

This paper focuses on power system scheduling problems, aiming to enhance energy utilization efficiency through multi-energy complementarity. To support the “dual-carbon” strategic goals, this paper proposes a coordinated dispatch model for hydro–thermal–wind–solar–pumped storage integrated energy systems, aiming to enhance energy utilization efficiency and system flexibility while reducing carbon emissions. To address issues such as premature convergence and low computational efficiency in traditional optimization algorithms for multi-energy complementary dispatch, an improved Artificial Bee Colony algorithm named Super-quality Variation Burst Artificial Bee Colony (SVBABC) is developed, which incorporates elite solution guidance and an explosion variation mechanism. Simulation results based on a regional practical power system demonstrate that compared to classical methods (e.g., Artificial Bee Colony, Fireworks Algorithm, and Ant Lion Optimizer), SVBABC exhibits significant advantages in global optimization capability and convergence stability. This study provides an innovative solution for efficient dispatch of multi-energy complementary systems. Through synergistic regulation of pumped storage and thermal power, the accommodation capability of renewable energy is effectively enhanced, thereby providing critical technical support for the development of new power systems.

To develop sustainable groundwater management strategies, generally coupled simulation-optimization (SO) models are used. In this study, a new SO model is developed by coupling moving least squares (MLS)-based meshless local Petrov-Galerkin (MLPG) method and modified artificial bee colony (MABC) algorithm. The MLPG simulation model utilizes the advantages of meshless methods over the grid-based techniques such as finite difference (FDM) and finite element method (FEM). For optimization, the basic artificial bee colony algorithm is modified to balance the exploration and exploitation capacity of the model more effectively. The performance of the developed MLPG-MABC model is investigated by applying it to hypothetical and field problems with three different management scenarios. The model results are compared with other available SO model solutions for its accuracy. Further, sensitivity analyses of various model parameters are carried out to check the robustness of the SO model. The proposed model gave quite promising results, showing the applicability of the present approach.

Product manufacturing quality is influenced by various factors of the production process, so the key to improve product manufacturing quality is improving the combination of the relevant parameters and processing methods in the manufacturing process. Aiming at this issue, a manufacturing quality improvement method using gene recombination and editing mechanism is proposed. In this method, an optimization model is established and described by formulas, in which three optimization objectives including production quality, costs, and time are involved. In the model, the quality indicator is measured by a comprehensive evaluation approach of product gene. To address the model, an improved genetic algorithm (GA) and artificial bee colony algorithm (ABC) with hybrid encoding scheme (H-IGA-IABC) is designed by considering the different types of gene elements. Fifteen comparison experiments with different scales are performed to test the model and H-IGA-IABC. According to the data obtained by different components and algorithms, the search ability, speed of convergence of H-IGA-IABC are better than that of other components and algorithms, especially in solving large-scale problems. Compared with the solution before optimization, the quality evaluation results and other indicators of the solutions after optimization are significantly better. Therefore, the proposed method is effective and performs well.

A growing number of educational studies apply sensors to improve student learning in real classroom settings. However, how can sensors be integrated into classrooms to help instructors find out students’ reading concentration rates and thus better increase learning effectiveness? The aim of the current study was to develop a reading concentration monitoring system for use with e-books in an intelligent classroom and to help instructors find out the students’ reading concentration rates. The proposed system uses three types of sensor technologies, namely a webcam, heartbeat sensor, and blood oxygen sensor to detect the learning behaviors of students by capturing various physiological signals. An artificial bee colony (ABC) optimization approach is applied to the data gathered from these sensors to help instructors understand their students’ reading concentration rates in a classroom learning environment. The results show that the use of the ABC algorithm in the proposed system can effectively obtain near-optimal solutions. The system has a user-friendly graphical interface, making it easy for instructors to clearly understand the reading status of their students.

In this paper, a new method based on the modified artificial bee colony (MABC) algorithm to determine the main characteristic parameters of the Schottky barrier diode such as barrier height, ideality factor and series resistance. For this model, the Ni/ n -GaAs/In Schottky barrier diode was produced and annealed at different temperature in a laboratory. The performance of the modified ABC method was compared to that of the basic artificial bee colony (ABC), particle swarm optimization (PSO), differential evolution (DE), genetic algorithm (GA) and simulated annealing (SA). From the results, it is concluded that the modified ABC algorithm is more flexible and effective for the parameter determination than the other algorithms.

In this paper, the algorithmic concepts of the Cuckoo-search (CK), Particle swarm optimization (PSO), Differential evolution (DE) and Artificial bee colony (ABC) algorithms have been analyzed. The numerical optimization problem solving successes of the mentioned algorithms have also been compared statistically by testing over 50 different benchmark functions. Empirical results reveal that the problem solving success of the CK algorithm is very close to the DE algorithm. The run-time complexity and the required function-evaluation number for acquiring global minimizer by the DE algorithm is generally smaller than the comparison algorithms. The performances of the CK and PSO algorithms are statistically closer to the performance of the DE algorithm than the ABC algorithm. The CK and DE algorithms supply more robust and precise results than the PSO and ABC algorithms.