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Abstract: As the industrial systems are growing complex these-days and data related to the system performance are recorded/collected from various resources under various practical constraints. If the collected data are used as such in the analysis, then they have high range of uncertainties occurred in the analysis and hence performance of the system cannot be done up to desired levels. Thus the main objective of the present work is to remove the uncertainties in the data up to a desired degree of accuracy by utilizing the uncertain, vague and limited data. For analysis of this, an artificial bee colony based Lambda–Tau (ABCBLT) methodology has been used in which expression of the reliability parameters are computed by using Lambda–Tau methodology and their membership functions are formulated by solving a nonlinear optimization problem with artificial bee colony (ABC) algorithm. A time varying failure rate has been used in the analysis instead of constant failure rate. A new RAM-Index has been proposed for ranking the systems’ components based on its performance. The technique has been demonstrated through a case study of press unit of a paper industry, situated in Northern part of India, producing 200tons of paper per day. The results computed by the proposed approach are compared with the Lambda–Tau methodology and concluded that they have a reduced region of prediction in comparison of existing technique region, i.e. uncertainties involved in the analysis are reduced. Thus, it may be a more useful analysis tool to assess the current system conditions and involved uncertainties. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

Optimization of scheduling problems that involve single or multiple machines with a multimodal objective function and linear or non-linear constraints are generally NP-Hard. The Artificial Bee Colony (ABC) algorithm is a metaheuristic proposed about eighteen years ago and has gained popularity in the swarm intelligence-based evolutionary computation approaches. Various scheduling requirements in diverse domains viz. Electric Vehicle charging, Operating Room, Nurse, Automatic Guided Vehicle (AGV), production, manufacturing, hydrothermal, Printed Circuit Board (PCB), and call centre scheduling are studied. The NP-Hard nature of these scheduling problems render exact methods incompetent for large problem instances thus requiring meta-heuristics such as ABC to produce feasible solutions in a reasonable computation time. The adaptation of ABC in these domains and the outcomes and performance of ABC are highlighted. Based on the case studies, it is found that the requirements of scheduling problems that would benefit from ABC are those that have a planning horizon requiring a near-optimal solution in a reasonably fast time when run on minimal computing capacity. Future works that would merit the application and enhancement of existing ABC techniques are proposed. The case studies presented in this paper motivate R&D Engineers and young researchers to apply ABC techniques of optimization and scheduling in different research topics for achieving energy and cost efficiency. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Unmanned aerial vehicle edge networks (UENs) can reduce the cache load of the core network and improve system performance to provide users with efficient content services. However, the time-varying characteristics of content popularity in UENs lead to a low accuracy of popularity prediction, and the capacity limitations of wireless channel conditions lead to a lower cache hit rate than the rates of traditional fiber-optic-based cache strategies. Therefore, this paper proposes the discrete artificial bee colony cache strategy of UENs (DABCCSU). First, the information–dynamics–dissemination model of UENs (IDDMU) is established to deduce the coupling relationship between the channel capacity and the service probability in IDDMU. The influence of the service probability change on the content dissemination process is discussed, and the content popularity in UENs is predicted by the state iteration matrix. Then, the discrete artificial bee colony cache (DABCC) optimization algorithm is proposed. The action function of the artificial bee colony is designed as a random action based on the historical cache strategy. The discrete cache strategy is used as an optimization variable, and the popularity prediction result obtained by IDDMU is used to maximize the cache hit rate. DABCC provides the optimal cache strategy for the UENs, and effectively improves the cache hit rate. The simulation result shows that the accuracy of DABCCSU in content popularity prediction is more than 90%, which achieves a good prediction effect. In terms of cache performance, the average cache hit rate of DABCCSU is 91.62%, which is better than the 51.09% of the Least Recently Used (LRU) strategy, 89.27% of the Greedy Algorithm (GA) and 54.26% of Binary Particle Swarm Optimization (BPSO). In addition, the cache hit rate of DABCCSU under different cache capacities is better than that of LRU, GA, and BPSO, showing a relatively stable performance. It shows that DABCCSU can achieve excellent content popularity prediction, and it can also maximize the cache hit rate under limited communication resources and cache resources to provide UENs with the optimal content cache strategy, and provides users with high-quality content services. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

In the Internet of Things (IoT), the users have complex needs, and the Web Service Composition (WSC) was introduced to address these needs. The WSC's main objective is to search for the optimal combination of web services in response to the user needs and the level of Quality of Services (QoS) constraints. The challenge of this problem is the huge number of web services that achieve similar functionality with different levels of QoS constraints. In this paper, we introduce an extension of our previous works on the Artificial Bee Colony (ABC) and Bat Algorithm (BA). A new hybrid algorithm was proposed between the ABC and BA to achieve a better tradeoff between local exploitation and global search. The bat agent is used to improve the solution of exhausted bees after a threshold (limits), and also an Elitist Strategy (ES) is added to BA to increase the convergence rate. The performance and convergence behavior of the proposed hybrid algorithm was tested using extensive comparative experiments with current state-of-the-art nature-inspired algorithms on 12 benchmark datasets using three evaluation criteria (average fitness values, best fitness values, and execution time) that were measured for 30 different runs. These datasets are created from real-world datasets and artificially to form different scale sizes of WSC datasets. The results show that the proposed algorithm enhances the search performance and convergence rate on finding the near-optimal web services combination compared to competitors. TheWilcoxon signed-rank significant test is usedwhere the proposed algorithm results significantly differ fromother algorithms on 100% of datasets. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper proposes a cloud computing-based approach to efficiently process the massive data produced in intelligent machine tool diagnosis flow. By collecting and extracting the vibration, power and other useful system signals during the machining operation of machine tools, the cutting process samples and cutting gap samples of machine tools can be accurately segmented, in order to construct a set of signal samples that can effectively and completely characterize the level of tool wear. We propose a visual detection method that relies on local threshold segmentation to predict tool wear status. The machine tool image is divided into several small blocks, and each image block is segmented to obtain the segmentation threshold, which is defined as the local threshold of each block. Then, the detection method scans the whole image based on the maximum local threshold among all blocks. Considering the complicated flow of visual detection and the high volume of machine tool diagnosis data, we further propose a big data processing approach which is implemented on a cloud computing architecture. By modeling the workflow of the proposed visual detection method as a directed acyclic graph, we develop a scheduling model that aims at minimizing the execution time of massive tool diagnosis data processing with available cloud computing resources. A effective metaheuristic based on search strategy of artificial bee colony is developed to solve the formulation scheduling problem. Experimental results on a cloud-based system demonstrate that, the visual detection method enhances the accuracy of tool wear detection, and the cloud-based approach significantly reduces the execution time of tool diagnosis flow by means of distributed computing. [ABSTRACT FROM AUTHOR]

As the amount of data input increases, fog devices on IoT edge networks become increasingly inefficient. However, a well-designed fog computing resource-scheduling strategy can help to reduce excessive time delays and energy consumption. Therefore, in this paper, we propose an efficient fog computing resource-scheduling strategy. First, we used particle swarm optimization (PSO) to determine the optimal load balance among fog nodes and to obtain the optimal computation time and energy consumption in a single fog cluster. Second, we designed a particle swarm genetic joint optimization artificial bee colony algorithm (PGABC) to optimize the task scheduling among fog clusters based on the time and energy consumption obtained from load balancing. In addition, PGABC was used to optimize the task-scheduling model, which further reduced the delay and energy consumption of fog computing. The experimental results show that the time delay that was calculated using the proposed PGABC algorithm in the given model was reduced by 1.04%, 15.9%, and 28.5%, compared to GABC, ABC, and PSO, respectively, and the energy consumption was reduced by 3.9%, 6.6%, and 12.6%, respectively. The proposed resource-scheduling strategy reduced the delay by approximately 31.25%, 27.8%, 27.8%, and 25.4%, and the energy consumption by approximately 9.7%, 33.3%, 32%, and 29.6%, compared to SJF–PSO, PGABC-R, HSF.ABC&PSO, and MFO, respectively. [ABSTRACT FROM AUTHOR]

Selective disassembly is a systematic method to remove target components or high-valuable components from an EOL product for reuse, recycling and remanufacturing as quick and feasible as possible, which plays a key role for the effective application of circular economy. However, in practice, the process of selective disassembly is usually characterized by various unpredictable factors of EOL products. It is very difficult to identify a feasible disassembly sequence for getting the target components before taking actions due to the uncertainty. In this paper, a data-driven method of selective disassembly planning for EOL products under uncertainty is proposed, in which disassemblability is regarded as the degree of difficulty in removing components under uncertainty. Taxonomy of uncertainty metrics that represents uncertain characteristics of components and disassembly transitions of selective disassembly is established. Random and fuzzy assessment data of uncertainty is converted into qualitative values and aggregated to fit a prediction model based on the trapezium cloud model. The turning time of disassemblability is predicted for a given set of certainty degree. Further, the disassemblability values are applied to determine the best selective disassembly sequence in order to get target component with tradeoff between minimum number of disassembly operations and maximum feasibility. The effectiveness of the proposed method is illustrated by a numerical example. Moreover, by comparing to selective disassembly planning without considering uncertainty, the proposed method turns selective disassembly of EOL products more realistic than 11% and provide insights on how to design product to facilitate disassembly operations. [ABSTRACT FROM AUTHOR]

Artificial bee colony algorithm is a strong global search algorithm which exhibits excellent exploration ability. The conventional ABC algorithm adopts employed bees, onlooker bees and scouts to cooperate with each other. However, its one dimension and greedy search strategy causes slow convergence speed. To enhance its performance, in this paper, we abandon the greedy selection method and propose an artificial bee colony algorithm with special division and intellective search (ABCIS). For the purpose of higher food source research efficiency, different search strategies are adopted with different employed bees and onlooker bees. Experimental results on a series of benchmarks algorithms demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

The Artificial Bee Colony (ABC) algorithm is considered as one of the swarm intelligence optimization algorithms. It has been extensively used for the applications of static type. Many practical and real-world applications, nevertheless, are of dynamic type. Thus, it is needed to employ some optimization algorithms that could solve this group of the problems that are of dynamic type. Dynamic optimization problems in which change(s) may occur through the time are tougher to face than static optimization problems. In this paper, an approach based on the ABC algorithm enriched with explicit memory and population clustering scheme, for solving dynamic optimization problems is proposed. The proposed algorithm uses the explicit memory to store the aging best solutions and employs clustering for preserving diversity in the population. Using the aging best solutions and keeping the diversity in population of the candidate solutions in the environment help speed-up the convergence of the algorithm. The proposed approach has been tested on Moving Peaks Benchmark. The Moving Peaks Benchmark is a suitable function for testing optimization algorithms and it is considered as one of the best representative of dynamic environments. The experimental study on the Moving Peaks Benchmark shows that the proposed approach is superior to several other well-known and state-of-the-art algorithms in dynamic environments. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Aspect terms are opinion targets for people to express and understand opinions in reviews. Aspect terms extraction is an essential subtask in aspect-level sentiment analysis. To extract aspect terms from a sentence, existing methods mainly focus on context features generated by pre-trained models. However, these models either neglect the crucial implicit linguistic features, e.g., post-of-tag, head, and head dependency, or fail to explore sufficient valuable features for aspect term extraction, which lead to the deficiency in aspect term extraction task. To address the challenges, in this paper, we propose a novel and effective framework for aspect term extraction by integrating both contextual and linguistic features with the artificial bee colony-based feature selection method. Firstly, a novel variant of artificial bee colony is designed to identify the most valuable linguistic features to reduce the high sparsity and dimensionality of the raw dataset. Next, the selected features and context embeddings are integrated to improve the performance of aspect extraction. Finally, extensive experiments are conducted on real-world datasets, and the results exhibit that our proposed framework can outperform the competitive baselines. Compared with the latest baselines, the proposed framework achieves the comparatively higher F1 scores of 80.7%, 84.7%, 72.2%, and 74.8% on the four groups of datasets. Furthermore, the ablation study shows that the proposed method with the designed feature selection module significantly outperforms the method with the original artificial bee colony, having 4.15%, 4.4%, 4.4%, and 3.2% improvements in F1 score on all the four datasets, respectively. [ABSTRACT FROM AUTHOR]

The Fetal Electrocardiogram (FECG) signal plays a crucial role in monitoring the health of the fetus, but there are numerous challenges in eliminating the maternal thorax signal and reducing noise interference. This paper proposes a novel objective function that combines a Least Mean Squares (LMS) adaptive filter with a heuristic algorithms to enhance the quality of the extracted FECG signal. To achieve better results, we introduce the Discrete Artificial Bee Colony (DABC) algorithm with a new initialization strategy, a random wavelet basic function strategy, and Gaussian distribution. These improvements enhance global search capabilities and ensure a faster convergence rate. The application of heuristic algorithms can reduce noise signals and provides clearer and more accurate results compared to the traditional LMS filter. Furthermore, the effectiveness of this innovative algorithm is compared with other widely used heuristic algorithms. The experiment results demonstrate that the novel algorithm significantly enhances performance by up to 8% compared to other conventional extraction methods in some indicators. • A new objective function is designed to combine heuristic algorithms and LMS filter to extract the FECG signal from AECG signal. • This paper propose a novel Discrete Artificial Bee Colony, which introduce a tailored initialization strategy to improve the algorithm's convergence speed, based on the specific characteristics of the FECG signal extraction problem. The novel algorithm can obtain clearer extracted signal than other existed heuristic algorithms. • We incorporate the strategies of random wavelet basic functions and Gaussian distribution in the DABC algorithm to enhance the global search ability and prevent the population from prematurely becoming trapped in a local optimum. [ABSTRACT FROM AUTHOR]

The successful use of fly ash (FA) and silica fume (SF) materials has been reported in the design of concrete samples in the literature. Due to the benefits of using these materials, they can be utilized in many industrial applications. However, the proper use of them in the right mixes is one of the important factors with respect to the strength and weight of concrete. Therefore, this paper develops relationships based on meta-heuristic (MH) algorithms (artificial bee colony technique) to evaluate the compressive strength of concrete specimens using laboratory experiments. A database comprising silica fume replacement ratio, fly ash replacement ratio, total cementitious material, water content coarse aggregate, high-rate water-reducing agent, fine aggregate, and age of samples, as model inputs, was used to evaluate and predict the compressive strength of concrete samples. Developed models of the MH technique created relationships between the mentioned parameters. In the new models, the influence of each parameter on the compressive strength was determined. Finally, using the developed model, optimum conditions for compressive strength of concrete samples were presented. This paper demonstrated that the MH algorithms are able to develop relationships that can serve as good substitutes for empirical models. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper presents compared performances of three metaheuristic algorithms in determining the cost of hybrid renewable energy system. Using genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC), the best affordable sizes of solar photovoltaic array, battery bank, and a minimum-rated diesel generator that could be hybridized to meet the demand of a community in Southwest Nigeria were determined. Load profile, solar radiation, and temperature data were employed as required inputs, and the parameters of the algorithms were properly set to ensure the best result. Bonferroni– Holm method was deployed to ascertain the statistical significance among the algorithms. It was found that ABC produced the best configuration comprising 427 numbers of solar photovoltaic panels, 19 battery units, and 163.2 kW-rated diesel generator. With this, a total annualized cost of $167 284 and 0.2443 estimated cost of energy were obtained. These were the lowest when compared with PSO and GA. The t-test between PSO and ABC are both 5.83 × 10−10 < 0.01666667, between ABC and GA are 6.09 × 10−6 <0.01666667 and 6.09 × 10−6 <0.025, while between GA and PSO are 9.13 × 10−1 > 0.01666667 and 9.13 × 10−1 > 0.05. PSO/ABC and ABC/GA groups are clarified significant, while GA/PSO group is insignificant; post hoc test reveals that ABC produced the best result. Hence, a reliable and sustainable power supply at a reduced cost is guaranteed for the community. [ABSTRACT FROM AUTHOR]

Rapid growth in the development of wireless sensor networks (WSN) in the field of distributed audio signal processing is one of the critical research areas. Intruders can easily track the unprotected audio signals through sinkhole in WSN. To identify the source of the unauthorized audio signals can be retrieve using wireless acoustic sensor networks (WASN). Detection of sinkhole attacks in WASN is a challenging task as there exists centralized level of control only for the collecting the data. Since it is distributed in computation detection of a compromised node and alerting the other nodes in the network can be considered as a challenging issue in this research domain. In this paper, the detection of sinkhole attacks using voting-based method is implemented along with the optimization perspective. The main contribution of this paper is to adapt Artificial Bee Colony algorithm for effective generation of minimized Boolean key for sharing the suspect list among the alerted nodes. The experimental results of our proposed work has been evaluated and compared with the recent algorithms. The metrics used for evaluation are energy value of bee, overall network lifetime and authentication overhead. The performance shows the impact of the proposed algorithm on WSN scenario when compared to other existing algorithms. [ABSTRACT FROM AUTHOR]

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

This paper presents an advanced method in urban growth modeling to discover transition rules of cellular automata (CA) using the artificial bee colony (ABC) optimization algorithm. Also, comparisons between the simulation results of CA models optimized by the ABC algorithm and the particle swarm optimization algorithms (PSO) as intelligent approaches were performed to evaluate the potential of the proposed methods. According to previous studies, swarm intelligence algorithms for solving optimization problems such as discovering transition rules of CA in land use change/urban growth modeling can produce reasonable results. Modeling of urban growth as a dynamic process is not straightforward because of the existence of nonlinearity and heterogeneity among effective involved variables which can cause a number of challenges for traditional CA. ABC algorithm, the new powerful swarm based optimization algorithms, can be used to capture optimized transition rules of CA. This paper has proposed a methodology based on remote sensing data for modeling urban growth with CA calibrated by the ABC algorithm. The performance of ABC-CA, PSO-CA, and CA-logistic models in land use change detection is tested for the city of Urmia, Iran, between 2004 and 2014. Validations of the models based on statistical measures such as overall accuracy, figure of merit, and total operating characteristic were made. We showed that the overall accuracy of the ABC-CA model was 89%, which was 1.5% and 6.2% higher than those of the PSO-CA and CA-logistic model, respectively. Moreover, the allocation disagreement (simulation error) of the simulation results for the ABC-CA, PSO-CA, and CA-logistic models are 11%, 12.5%, and 17.2%, respectively. Finally, for all evaluation indices including running time, convergence capability, flexibility, statistical measurements, and the produced spatial patterns, the ABC-CA model performance showed relative improvement and therefore its superiority was confirmed. [ABSTRACT FROM AUTHOR]

Feature selection is an essential step of preprocessing in Machine Learning (ML) algorithms that can significantly impact the performance of ML models. It is considered one of the most crucial phases of automated ML (AutoML). Feature selection aims to find the optimal subset of features and remove the noninformative features from the dataset. Feature selection also reduces the computational time and makes the data more understandable to the learning model. There are various heuristic search strategies to address combinatorial optimization challenges. This paper develops an Automated Artificial Bee Colony-based algorithm for Feature Selection (A2BCF) to solve a classification problem. The application domain evaluating our proposed algorithm is education science, which solves a binary classification problem, namely, undergraduate student success. The modifications made to the original Artificial Bee Colony algorithm make the algorithm a well-performed approach. [ABSTRACT FROM AUTHOR]

Optic disc localization offers an important clue in detecting other retinal components such as the macula, fovea, and retinal vessels. With the correct detection of this area, sudden vision loss caused by diseases such as age-related macular degeneration and diabetic retinopathy can be prevented. Therefore, there is an increase in computer-aided diagnosis systems in this field. In this paper, an automated method for detecting optic disc localization is proposed. In the proposed method, the fundus images are moved from RGB color space to a new color space by using an artificial bee colony algorithm. In the new color space, the localization of the optical disc is clearer than in the RGB color space. In this method, a matrix called the feature matrix is created. This matrix is obtained from the color pixel values of the image patches containing the optical disc and the image patches not containing the optical disc. Then, the conversion matrix is created. The initial values of this matrix are randomly determined. These two matrices are processed in the artificial bee colony algorithm. Ultimately, the conversion matrix becomes optimal and is applied over the original fundus images. Thus, the images are moved to the new color space. Thresholding is applied to these images, and the optic disc localization is obtained. The success rate of the proposed method has been tested on three general datasets. The accuracy success rate for the DRIVE, DRIONS, and MESSIDOR datasets, respectively, is 100%, 96.37%, and 94.42% for the proposed method. [ABSTRACT FROM AUTHOR]

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With the advancement in personalized healthcare technology, the usage of wearable devices for continuous monitoring and analysis of long-term biomedical signals, such as electrocardiography (ECG) has shown explosive growth. However, the existing ECG monitoring devices exhibit limited performance, such as they only store the ECG data, have low accuracy and their inability to perform event-by-event diagnosis at the place of data acquired. Therefore, the personalized healthcare demands an efficient method and point-of-care platform capable of providing real-time feedback to consumers as well as subjects. In this paper, a novel ECG signal analysis method using discrete cosine stockwell transform for feature extraction and artificial bee colony optimized least-square twin support vector machines as classifier is developed and prototyped using commercially available 32-bit microcontroller test platform. The prototype is evaluated under two schemes, i.e., the class and personalized schemes and validated on the benchmark MIT-BIH arrhythmia data. A higher overall accuracy of 96.14% and 86.5% respectively is reported by the prototype in the aforesaid two evaluation schemes than the existing works. The platform can be utilized as an early warning system in detecting abnormal ECG in home care environment to the state-of-art diagnosis. [ABSTRACT FROM AUTHOR]

This paper presents a novel parameter identification and uncertainty quantification method for flutter derivatives estimation of bridge decks. The proposed approach is based on free-decay vibration records of a sectional model in wind tunnel tests, which consists of parameter identification by a heuristic optimization algorithm in the sense of weighted least squares and uncertainty quantification by a bootstrap technique. The novel contributions of the method are on three fronts. Firstly, weighting factors associated with vertical and torsional motion in the objective function are determined more reasonably using an iterative procedure rather than preassigned. Secondly, flutter derivatives are identified using a hybrid heuristic and classical optimization method, which integrates a modified artificial bee colony algorithm with the Powell's algorithm. Thirdly, a statistical bootstrap technique is used to quantify the uncertainties of flutter derivatives. The advantages of the proposed method with respect to other methods are faster and more accurate achievement of the global optimum, and refined uncertainty quantification in the identified flutter derivatives. The effectiveness and reliability of the proposed method are validated through noisy data of a numerically simulated thin plate and experimental data of a bridge deck sectional model. [ABSTRACT FROM AUTHOR]

The natural behaviour of the honeybee has attracted the attention of researchers in recent years and several algorithms have been developed that mimic swarm behaviour to solve optimisation problems. This paper introduces an artificial bee colony (ABC) algorithm for the vehicle routing problem with time windows (VRPTW). A Modified ABC algorithm is proposed to improve the solution quality of the original ABC. The high exploration ability of the ABC slows-down its convergence speed, which may due to the mechanism used by scout bees in replacing abandoned (unimproved) solutions with new ones. In the Modified ABC a list of abandoned solutions is used by the scout bees to memorise the abandoned solutions, then the scout bees select a solution from the list based on roulette wheel selection and replace by a new solution with random routs selected from the best solution. The performance of the Modified ABC is evaluated on Solomon benchmark datasets and compared with the original ABC. The computational results demonstrate that the Modified ABC outperforms the original ABC also produce good solutions when compared with the best-known results in the literature. Computational investigations show that the proposed algorithm is a good and promising approach for the VRPTW. [ABSTRACT FROM AUTHOR]

This paper describes the development of BOLE, a MATLAB-based interactive learning environment, that facilitates the process of learning bio-inspired optimization algorithms, and that is dedicated exclusively to unmanned aerial vehicle path planning. As a complement to conventional teaching methods, BOLE is designed to help students consolidate the concepts taught in the course and motivate them to explore relevant issues of bio-inspired optimization algorithms through interactive and collaborative learning processes. BOLE differs from other similar tools in that it places greater emphasis on fundamental concepts than on complex mathematical equations. The learning tasks using BOLE can be classified into four steps: introduction, recognition, practice, and collaboration, according to task complexity. It complements traditional classroom teaching, enhancing learning efficiency and facilitating the assessment of student achievement, as verified by its practical application in an undergraduate course “Bio-Inspired Computing.” Both objective and subjective measures were evaluated to assess the learning effectiveness. [ABSTRACT FROM PUBLISHER]

Introduction. Network expansion, substation planning, generating expansion planning, and load forecasting are all aspects of modern power system planning. The aim of this work is to solve network planning considering both future demand and all equality and inequality constraints. The transmission network design problem for the 6-bus system is considered and addressed using the Global Best Artificial Bee Colony (GABC) method in this research. The program is written in the Matrix Laboratory in MATLAB environment using the proposed methodology. Novelty of the work consist in considering the behavior of bees to find food source in most optimized way in nature with feature of user based accuracy selection and speed of execution selection on any scale of the system to solve Transmission Lines Expansion Problem (TLEP). The proposed method is implemented on nonlinear mathematical function and TLEP function. When demand grows, the program output optimally distributes new links between new generation buses and old buses, determines the overall minimum cost of those links, and determines if those linkages should meet power system limits. Originality of the proposed method is that it eliminated the need of load shedding while planning the future demand with GABC method. Results are validated using load flow analysis in electrical transient analyzer program, demonstrating that artificial intelligence approaches are accurate and particularly effective in non-linear transmission network planning challenges. Practical value of the program is that it can use to execute cost oriented complex transmission planning decision. [ABSTRACT FROM AUTHOR]

Wireless computing has revolutionized our life with the technological advancement from the traditional networking into a new epoch for communication in ad hoc decorum. An energy efficient network of sensors based on wireless communication and networking principles can enhance the effectiveness of computing during unpredictable circumstances. If a computable required resource is readily available within the reachable region and it is identified to be idle and it is ready to share the corresponding information from its end without affecting the normal behavior of the device or the node, then there exists an opportunity to utilize those resources for computing. Opportunistic computing has a great potential of growth in the field of wireless ad hoc network computing. In traditional network computing technology, mobile computing, grid computing, distributed computing, ubiquitous computing and cloud computing, formerly ensues the communication with minimal resources currently available at the terminal point. This paper proposes a novel framework for an effective utilization of sharable resources, which are available within the reachable region, by creating an opportunity to frame an opportunistic computing while preserving the user's privacy. The proposed framework adopts a bio-inspired technique for identifying and collecting resources information, and thereby recognizes which resource is ready to participate in the opportunistic computing. Experimental results of a system that implements the bio-inspired technique along with the natural behavior of the bee colony approach was analyzed and found that the proposed system shows comparatively high performance in terms of computation resource searching, identifying, emergency data transfer, and participative node privacy preserving. [ABSTRACT FROM AUTHOR]

Accurately determining geomechanical parameters and rock mass deformation are key components of slope stability assessment as well as structural design and safe construction. Numerical models are therefore commonly applied to these issues as a component of back analysis, which provides a practical tool for determining geomechanical parameters in rock engineering. Although once quantified, rock mass deformation can also be calculated using an appropriate numerical model, the traditional approaches tend to be time-consuming and cannot be used to accurately determine deformation uncertainty in practical rock engineering. A relevance vector machine (RVM) was adopted in this study to build the complex mapping relationship between geomechanical parameters and slope deformation, while an artificial bee colony (ABC) was also utilized to search for optimal parameters based on the RVM model and displacement monitored in the field. On this basis, both deformation and associated uncertainty can be predicted using this model via the input of determined geomechanical parameters. The method advocated in this paper was then applied to a rock slope at the Longtan hydropower station, China; results demonstrate that the RVM model accurately describes the relationship between geomechanical parameters and displacement increments and can thus be used to predict both deformation and associated uncertainty. [ABSTRACT FROM AUTHOR]

Feature selection, which can reduce the dimensions of feature space without sacrificing the performance of the classifier, is an effective technique for text classification. Because many classifiers cannot deal with the features with high dimensions, filtering the redundant information from the original feature space becomes one of the core goals in feature selection field. In this paper, the concept of equivalence word set is introduced and a set of equivalence word sets (represented as EWS1) is constructed using the rich semantic information of the Open Directory Project (ODP). On this basis, an artificial bee colony based feature selection method is proposed for filtering the redundant information, and a feature subset FS is obtained by using an optimal feature selection (OFS) method and two predetermined thresholds. In order to obtain the best predetermined thresholds, an improved memory based artificial bee colony method (IABCM) is proposed. In the experiments, fuzzy support vector machine (FSVM) and Naïve Bayesian (NB) classifiers are used on six datasets: LingSpam, WebKB, SpamAssian, 20-Newsgroups, Reuters21578 and TREC2007. Experimental results verify that when FSVM and NB are applied, the proposed method is efficient and achieves better accuracy than several representative feature selection methods. [ABSTRACT FROM AUTHOR]

Artificial bee colony (ABC) algorithm has attracted significant interests recently for solving the multivariate optimization problem. However, it still faces insufficiency of slow convergence speed and poor local search ability. Therefore, in this paper, a modified ABC algorithm with bees' number reallocation and new search equation is proposed to tackle this drawback. In particular, to enhance solution accuracy, more bees in the population are assigned to execute local searches around food sources. Moreover, elite vectors are adopted to guide the bees, with which the algorithm could converge to the potential global optimal position rapidly. A series of classical benchmark functions for frequency-modulated sound waves are adopted to validate the performance of the modified ABC algorithm. Experimental results are provided to show the significant performance improvement of our proposed algorithm over the traditional version. [ABSTRACT FROM AUTHOR]

Software testing is both a time and resource-consuming activity in software development. The most difficult parts of software testing are the generation and prioritization of test data. Principally these two parts are performed manually. Hence introducing an automation approach will significantly reduce the total cost incurred in the software development lifecycle. A number of automatic test case generation (ATCG) and prioritization approaches have been explored. In this paper, we propose two approaches: (1) a pathspecific approach for ATCG using the following metaheuristic techniques: the genetic algorithm (GA), particle swarm optimization (PSO) and artificial bee colony optimization (ABC); and (2) a test case prioritization (TCP) approach using PSO. Based on our experimental findings, we conclude that ABC outperforms the GA and PSO-based approaches for ATC.G Moreover, the results for PSO on TCP arguments demonstrate biased applicability for both small and large test suites against random, reverse and unordered prioritization schemes. Therefore, we focus on conducting a comprehensive and exhaustive study of the application of metaheuristic algorithms in solving ATCG and TCP problems in software engineering. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Three-phase induction motors are extensively used in industrial processes due to their robustness, adaptability to different operating conditions, and low operation and maintenance costs. Induction motor fault diagnosis has received special attention from industry since it can reduce process losses and ensure the reliable operation of industrial systems. Therefore, this paper presents a study on the use of meta-heuristic tools in the diagnosis of bearing failures in induction motors. The extraction of the fault characteristics is performed based on mutual information measurements between the stator current signals in the time domain. Then, the Artificial Bee Colony algorithm is used to select the relevant mutual information values and optimize the pattern classifier input data. To evaluate the classification accuracy under various levels of failure severity, the performance of two different pattern classifiers was compared: The C4.5 decision tree and the multi-layer artificial perceptron neural networks. The experimental results confirm the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

ABC_elite, a novel artificial bee colony algorithm with elite-guided search equations, has been put forward recently, with relatively good performance compared with other variants of artificial bee colony (ABC) and some non-ABC methods. However, there still exist some drawbacks in ABC_elite. Firstly, the elite solutions employ the same equation as ordinary solutions in the employed bee phase, which may easily result in low success rates for the elite solutions because of relatively large disturbance amplitudes. Secondly, the exploitation ability of ABC_elite is still insufficient, especially in the latter half of the search process. To further improve the performance of ABC_elite, two novel search equations have been proposed in this paper, the first of which is used in the employed bee phase for elite solutions to exploit valuable information of the current best solution, while the second is used in the onlooker bee phase to enhance the exploitation ability of ABC_elite. In addition, in order to better balance exploitation and exploration, a parameter Po is introduced into the onlooker bee phase to decide which search equation is to be used, the existing search equation of ABC_elite or a new search equation proposed in this paper. By combining the two novel search equations together with the new parameter Po, an improved ABC_elite (IABC_elite) algorithm is proposed. Based on experiments concerning 22 benchmark functions, IABC_elite has been compared with some other state-of-the-art ABC variants, showing that IABC_elite performs significantly better than ABC_elite on solution quality, robustness, and convergence speed. [ABSTRACT FROM AUTHOR]

An effective non-revisiting artificial bee colony (NrABC) algorithm based on the paradigm of artificial bee colony (ABC) is developed in this paper. NrABC is applied to tackle the synthesis of phased linear arrays. Pros and cons of NrABC is provided along with a comparison to standard ABC. Binary space partitioning tree structure is used to record history evolutionary information. Non-revisiting scheme assures NrABC keeping good diversity of population. Moreover, scout bee stage is discarded in NrABC which also removes an algorithmic parameter of standard ABC. Three phased array synthesis examples are employed to study the performance of NrABC. It turns out that under the same experimental configuration, NrABC outperforms standard ABC in terms of both solution quality and reliability in repeated runs. [ABSTRACT FROM AUTHOR]