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Efficient pilotage planning is essential, particularly due to the increasing demand for skilled pilots amid frequent vessel traffic. Addressing pilot shortages and ensuring navigational safety, this study presents an innovative pilot-ASV scheduling strategy. This approach utilizes autonomous surface vehicles (ASVs) to assist or replace junior pilots in specific tasks, thereby alleviating pilot resource constraints and upholding safety standards. We develop a comprehensive mathematical model that accommodates pilot work time windows, various pilot levels, and ASV battery limitations. An improved artificial bee colony algorithm is proposed to solve this model effectively, integrating breadth-first and depth-first search strategies to enhance solution quality and efficiency uniquely. Extensive numerical experiments corroborate the model's effectiveness, showing that our integrated optimization approach decreases vessel waiting times by an average of 9.18% compared to traditional methods without ASV integration. The findings underscore the potential of pilot-ASV scheduling to significantly improve both the efficiency and safety of vessel pilotages. [ABSTRACT FROM AUTHOR]

In the article, we propose a new method to generate representative volume element (RVE) of random spatial distributions with cylindrical fibers and spherical particles based on the Improved Artificial Bee Colony (IABC) algorithm. In this employed bee phase of IABC, the proposed algorithm adopts a new search strategy, which improves the computational efficiency. To analyze the differences between the fiber distribution generated by this algorithm and the actual fiber distribution, we conducted statistical analysis on the distribution of the generated RVE. The statistical analysis results of the nearest neighbor distance (NND), Second-order intensity function, and pair correlation function were in good agreement with the experiment and complete spatial randomness (CSR) patterns. In addition, the predicted elastic properties were compared with experimental measurements and theoretical prediction methods. The results demonstrate the accuracy and efficiency of the proposed algorithm. This algorithm can quickly generate the RVE of cylindrical fibers and spherical particles with volume fractions of 80% and 62%, and the calculation time for generating the RVE of cylindrical fibers is about 17s. [ABSTRACT FROM AUTHOR]

The ability to estimate wind speed accurately is crucial for optimizing the utilization of wind energy in power systems. The randomness of wind speed and the performance of prediction models are the two issues that have the greatest impact on wind speed prediction. This paper starts from the two aspects of digging wind speed characteristics and optimizing model performance. A new wind speed combined prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved artificial bee colony algorithm (IABC) to optimize least squares support vector machine (LSSVM) performance. In order to reduce the randomness of the wind speed sequence and increase its predictability, CEEMDAN is first used to break down the wind speed sequence. Secondly, an LSSVM prediction model optimized by an improved artificial bee colony algorithm is established for each subsequence obtained by decomposition; finally, the wind speed forecast value is created by superimposing the results of each subsequence’s prediction. Case studies demonstrate that the combined prediction model put out in this study outperforms other models in terms of prediction performance.

The images obtained from remote sensing consist of background complexities and similarities among the objects that act as challenge during the classification of land scenes. Land scenes are utilized in various fields such as agriculture, urbanization, and disaster management, to detect the condition of land surfaces and help to identify the suitability of the land surfaces for planting crops, and building construction. The existing methods help in the classification of land scenes through the images obtained from remote sensing technology, but the background complexities and presence of similar objects act as a barricade against providing better results. To overcome these issues, an improved artificial bee colony optimization algorithm with convolutional neural network (IABC-CNN) model is proposed to achieve better results in classifying the land scenes. The images are collected from aerial image dataset (AID), Northwestern Polytechnical University-Remote Sensing Image Scene 45 (NWPU-RESIS45), and University of California Merced (UCM) datasets. IABC effectively selects the best features from the extracted features using visual geometry group-16 (VGG-16). The selected features from the IABC are provided for the classification process using multiclass-support vector machine (MSVM). Results obtained from the proposed IABC-CNN achieves a better classification accuracy of 96.40% with an error rate 3.6%. [ABSTRACT FROM AUTHOR]

The maximum power point tracking (MPPT) controller can monitor the voltage and current of photovoltaic (PV) array in real-time to acquire the maximum power output for battery charging in PV system. In order to improve the accuracy and rapidity of tracking process, a bionic two-stage MPPT control strategy composed of fast positioning stage and precise determination stage is proposed in this paper for optimizing the duty cycle d of DC–DC converter. Firstly, an improved artificial bee colony algorithm with simplified probability selection mechanism and novel employed bee phase is presented to balances exploration and exploitation for excellent rapidity in positioning rough search region around global peak. Then, the simultaneous heat transfer search (SHTS) algorithm is adopted for accurately acquiring the global maximum power point in obtained search region. On the one hand, the SHTS can reduce the subjectivity in artificial setting of the parameters to ensure the universality and accuracy of search process. On the other hand, the parallel search in SHTS can decrease the optimization time effectively. Finally, extensive simulation results illustrate that this two-stage MPPT strategy shows excellent performance in precisely identifying the GMPP of PV system with phenomenal rapidity among multiple peaks. Moreover, it outperforms all the counterparts in tracking speed and accuracy under partial shading conditions. • A two-stage MPPT controller is designed considering tracking speed and accuracy. • An improved artificial bee colony algorithm is adopted in the early stage of MPPT. • The simultaneous heat transfer search algorithm is used in the later stage of MPPT. • The proposed MPPT scheme is applied to PV system under various shading conditions. [ABSTRACT FROM AUTHOR]

• A mode division strategy is improved by using an unsupervised learning method. • An acceleration factor is introduced to the artificial bee colony algorithm. • K-means clustering is improved by combining artificial bee colony algorithm. • The fuel consumption is decreased by 4.78% with the improved strategy. Based on the optimization results of dynamic programming, this paper proposes an optimal rule design method for a series-parallel plug-in hybrid electric vehicle mode division strategy combining K-means clustering and improved artificial bee colony. The improved artificial bee colony algorithm uses the average of the sum of the squares of the distances method to initialize the colony to overcome the randomness of initialization and best utilize the useful information, constructs a fitness function adapted to the clustering analysis, and introduces an acceleration factor into the search equation to dynamically adjusted search scope. Combining the improved artificial bee colony algorithm with the K-means clustering algorithm, the clustering performance is improved and the optimal rule-based mode division strategy is obtained. The simulation results show that the new strategy reduces fuel consumption by 4.78% and electricity consumption by 1.81% comparing with the original strategy. [ABSTRACT FROM AUTHOR]

In this paper, the improved artificial bee colony (IABC) and improved particle swarm optimisation (IPSO) have been used for the optimal design of stepped spillway with the minimisation of construction cost as the energy dissipater structure for a dam project. Generally, the construction cost of this structure is the most expensive part of the project. However, traditional designing methods of stepped spillway such as the Vittal and Porey (VP) approach cannot find optimal dimensions of the stepped spillway with minimum cost. Therefore, the optimisation methods such as meta‐heuristic algorithms have been used. As a case study, Tehri dam's stepped spillway in India has been considered here and the optimal dimensions of this spillway have been obtained using IPSO, IABC algorithms and the results have been compared with VP approach and other available results. Comparison of the results shows that when the three‐stepped spillway is considered, the results of IABC and IPSO algorithms are improved by 17.72% in comparison with VP results. In addition, when the four‐stepped spillway is considered, the results of IABC, IPSO algorithms are, respectively, improved by 16.47% and 16.53% in comparison with the VP results. [ABSTRACT FROM AUTHOR]

Maximizing regenerative energy utilization is an important way to reduce substation energy consumption in subway systems. Timetable optimization and energy storage systems are two main ways to improve improve regenerative energy utilization, but they were studied separately in the past. To further improve energy conservation while maintaining a low cost, this paper presents a strategy to improve regenerative energy utilization by an integration of them, which determines the capacity of each Wayside Energy Storage System (WESS) and correspondingly optimizes the timetable at the same time. We first propose a dual-objective optimization problem to simultaneously minimize substation energy consumption and the total cost of WESS. Then, a mathematical model is formulated with the decision variables as the configuration of WESS and timetable. Afterwards, we design an ϵ -constraint method to transform the dual-objective optimization problem into several single-objective optimization problems, and accordingly design an improved artificial bee colony algorithm to solve them sequentially. Finally, numerical examples based on the actual data from a subway system in China are conducted to show the effectiveness of the proposed method. Experimental results indicate that substation energy consumption is effectively reduced by using WESS together with a correspondingly optimized timetable. Note that substation energy consumption becomes lower when the total size of WESS is larger, and timetable optimization further reduces it. A set of Pareto optimal solutions is obtained for the experimental subway line—based on which, decision makers can make a sensible trade-off between energy conservation and WESS investment accordingly to their preferences.

The Ensemble Empirical Mode Decomposition (EEMD) algorithm has been used in bearing fault diagnosis. In order to overcome the blindness in the selection of white noise amplitude coefficient e in EEMD, an improved artificial bee colony algorithm (IABC) is proposed to obtain it adaptively, which providing a new idea for the selection of EEMD parameters. In the improved algorithm, chaos initialization is introduced in the artificial bee colony (ABC) algorithm to insure the diversity of the population and the ergodicity of the population search process. On the other hand, the collecting bees are divided into two parts in the improved algorithm, one part collects the optimal information of the region according to the original algorithm, the other does Levy flight around the current global best solution to improve its global search capabilities. Four standard test functions are used to show the superiority of the proposed method. The application of the IABC and EEMD algorithm in bearing fault diagnosis proves its effectiveness.

The Improved Artificial Bee Colony (IABC) algorithm is a variant of the well-known Artificial Bee Colony (ABC) algorithm. In IABC, a new initialization approach and a new search mechanism were added to the ABC for avoiding local optimums and a better convergence speed. New parameters were added for the new search mechanism. Specified values of these newly added parameters have a direct impact on the performance of the IABC algorithm. For better performance of the algorithm, parameter values should be subjected to change from problem to problem and also need to be updated during the run of the algorithm. In this paper, two novel parameter control methods and related algorithms have been developed in order to increase the performance of the IABC algorithm for large scale optimization problems. One of them is an adaptive parameter control which updates parameter values according to the feedback coming from the search process during the run of the algorithm. In the second method, the management of the parameter values is left to the algorithm itself, which is called self-adaptive parameter control. The adaptive IABC algorithms were examined and compared to other ABC variants and state-of-the-art algorithms on a benchmark functions suite. Through the analysis of the results of the experiments, the adaptive IABC algorithms outperformed almost all ABC variants and gave competitive results with state-of-the-art algorithms from the literature. [ABSTRACT FROM AUTHOR]

One of data grouping process method is k-harmonic clustering method (KHM) which has a relatively short and simple process. However, it has a weakness at cluster center point. Randomly formed cluster center point causes difficulty to converge solutions. One way to solve the problem at the cluster center point requires a method which has a global solution for KHM. The method is Improved artificial bee colony (IABC), improvement of artificial bee colony (ABC) method based on behavior patterns of honey bee colony in food searching process. Advantage of the IABC method is able to have more optimum global solution. This research proposes a new method of clustering using improved artificial bee colony and K-Harmonic means (IABC-KHM) to optimize the center point in clusters that lead to global solution. In this study, the IABC is functioned for finding the most optimum cluster center point for the data clustering process using KHM. Furthermore, the performance test of the IABC-KHM clustering method is compared with ABC and ABC-KHM methods on three different datasets. The result of mean value of best function of IABC-KHM method of Iris dataset is 152,87, Contraceptive Method Choice dataset is 918,54, and Wine dataset is 31,01. Moreover, the result of the average value of the best F-Measure method IABC-KHM Iris dataset is 0.90, the Contraceptive Method Choice dataset is 0.41, the Wine dataset is 0.95. To conclude, IABC-KHM method has successfully optimized the position of cluster center point that directs the cluster result which has global solution.

Maximizing regenerative energy utilization is an important way to reduce substation energy consumption in subway systems. Timetable optimization and energy storage systems are two main ways to improve improve regenerative energy utilization, but they were studied separately in the past. To further improve energy conservation while maintaining a low cost, this paper presents a strategy to improve regenerative energy utilization by an integration of them, which determines the capacity of each Wayside Energy Storage System (WESS) and correspondingly optimizes the timetable at the same time. We first propose a dual-objective optimization problem to simultaneously minimize substation energy consumption and the total cost of WESS. Then, a mathematical model is formulated with the decision variables as the configuration of WESS and timetable. Afterwards, we design an ϵ -constraint method to transform the dual-objective optimization problem into several single-objective optimization problems, and accordingly design an improved artificial bee colony algorithm to solve them sequentially. Finally, numerical examples based on the actual data from a subway system in China are conducted to show the effectiveness of the proposed method. Experimental results indicate that substation energy consumption is effectively reduced by using WESS together with a correspondingly optimized timetable. Note that substation energy consumption becomes lower when the total size of WESS is larger, and timetable optimization further reduces it. A set of Pareto optimal solutions is obtained for the experimental subway line—based on which, decision makers can make a sensible trade-off between energy conservation and WESS investment accordingly to their preferences. [ABSTRACT FROM AUTHOR]

The Ensemble Empirical Mode Decomposition (EEMD) algorithm has been used in bearing fault diagnosis. In order to overcome the blindness in the selection of white noise amplitude coefficient e in EEMD, an improved artificial bee colony algorithm (IABC) is proposed to obtain it adaptively, which providing a new idea for the selection of EEMD parameters. In the improved algorithm, chaos initialization is introduced in the artificial bee colony (ABC) algorithm to insure the diversity of the population and the ergodicity of the population search process. On the other hand, the collecting bees are divided into two parts in the improved algorithm, one part collects the optimal information of the region according to the original algorithm, the other does Levy flight around the current global best solution to improve its global search capabilities. Four standard test functions are used to show the superiority of the proposed method. The application of the IABC and EEMD algorithm in bearing fault diagnosis proves its effectiveness. [ABSTRACT FROM AUTHOR]