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In this paper, a novel Artificial Bee Colony algorithm is proposed to solve a variant of the Job Shop Scheduling Problem where only an interval of possible processing times is known for each operation. The solving method incorporates a diversification strategy based on the seasonal behaviour of bees. That is, the bees tend to explore more at the beginning of the search (spring) and be more conservative towards the end (summer to winter). This new strategy helps the algorithm avoid premature convergence, which appeared to be an issue in previous papers tackling the same problem. A thorough parametric analysis is conducted and a comparison of different seasonal models is performed on a set of benchmark instances from the literature. The results illustrate the benefit of using the new strategy, improving the performance of previous ABC-based methods for the same problem. An additional study is conducted to assess the robustness of the solutions obtained under different ranking operators, together with a sensitivity analysis to compare the effect that different levels of uncertainty have on the solutions' robustness. [ABSTRACT FROM AUTHOR]

In this paper, we introduce an extended version of artificial bee colony with a local search method (EABC) for solving the QoS uncertainty-aware web service composition (IQSC) problem, where the ambiguity of the QoS properties are represented using the interval-number model. At first, we formulate the addressed problem as an interval constrained single-objective optimization model. Then, we use the skyline operator to prune the redundant and dominated web services from their sets of functionally equivalent ones. Whereas, EABC is employed to solve the IQSC problem in a reduced search space more effectively and more efficiently. For the purpose of validation of the performance and the efficiency of the proposed approach, we present the experimental comparisons to an existing skyline-based PSO, an efficient discrete gbest-guided artificial bee colony and a recently provided Harris Hawks optimization with an elite evolutionary strategy algorithms on an interval extended version of the public QWS dataset. [ABSTRACT FROM AUTHOR]

Renewable energy presently occupies a prominent position in India's overall energy generation scheme. In the midst of numerous alternative energy resources, solar energy is widely used as it persists in large volumes with varying specification ratings. It is suited for both stand-alone and grid-coupled application. The Maximum Power Tracking (MPPT) scheme has a profound effect on the operating efficiency of a Photovoltaic (PV) power plant. This paper proposes an inventive hybridized Human Psychology Optimization-Perturb and Observation (HPO-PO) MPPT approach for obtaining the optimal duty cycle of the boost converter to harvest global maxima from a grid-connected Total Cost Tied (TCT) configured PV array of 4080 W. The suggested method provides enriched performance both in steady-state, as well as in rapid and randomly changing weather conditions. Comparison studies of various MPPT procedures, including Perturbation and Observation (PO), Artificial Bee Colony (ABC), and Human Psychology Optimization (HPO) in MATLAB environment, illustrate the usefulness of the evoked system in meeting its goals. The suggested MPPT procedure has offered enhanced activities in terms of voltage quality, maximum power tracking capability, and converter efficiency compared to other methods. The recommended hybridized MPPT approach is experimentally validated on a hardware set-up using a 16-bit dsPIC30F2010 Digital Signal Controller in enhancing the behavior of the grid-connected PV system. [ABSTRACT FROM AUTHOR]

In the increasingly competitive logistics industry, much more emphasis has been put on customer satisfaction by firms to distinguish themselves from their competitors. Motivated by the practices, the consistent vehicle routing problem (ConVRP) incorporates service consistency into the vehicle routing problem to improve customer satisfaction. However, the majority of the existing research considers the ConVRP in a deterministic environment while the uncertainties are not fully studied. Therefore, this paper solves the consistent vehicle routing problem under uncertain environment (UnConVRP) taking into account uncertain customer demands, travel times, and service times. Over a multi-day planning horizon, customers may have multi-day or single-day service requirements. The same driver is assigned to each customer almost at the same time each day when customers require service. The objective is to design the routes for vehicles over the planning horizon under uncertain environment while maintaining service consistency. Two uncertain programming models are established based on different decision criteria and the crisp equivalents are proposed using uncertainty theory. An efficient template-based solution framework is designed to solve the models where the artificial bee colony algorithm is embedded. Initially, the template route is constructed to guarantee the service consistency of frequent customers. Then the final daily routes can be derived from the template route. Finally, numerical experiments are performed to show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Multilevel thresholding is one of the effective image segmentation methods. However, it faces three big challenges: (1) how to adaptively determine the number of multiple thresholds; (2) how to overcome the sensitivity to image noise; (3) how to perform multilevel thresholding under several segmentation requirements. In order to solve these problems, an adaptive multilevel thresholding algorithm based on multiobjective artificial bee colony optimization (AMT-MABCO) segmentation is presented for noisy image in this paper. To improve the robustness of AMT-MABCO to image noise, a line intercept histogram which considers both the intensity and coordinate information in the neighborhood of the pixels is firstly utilized to define a novel between-class variance function as one fitness function. Then, an interval-valued fuzzy entropy function is constructed as another fitness function to deal with the blurred characteristic in images. AMT-MABCO tries to obtain a compromising multilevel thresholding result under these two segmentation requirements. To adaptively determine the number of thresholds, a grouping population initialization and evaluation strategies are proposed in AMT-MABCO. Furthermore, two novel search equations are constructed in AMT-MABCO to generate candidate solutions in the employed bees and onlookers phases, respectively. Experimental results show that AMT-MABCO outperforms state-of-the-art thresholding methods in noise robustness and segmentation performance. [ABSTRACT FROM AUTHOR]

Vehicular Ad-hoc Networks (VANETs) have unique capabilities and are associated with Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications to reduce traffic dynamics and provide support to the providers and the drivers to rely on security and communication alerts. Our proposed method is only supported by V2V and V2I communications and neighbourhood localization records, which are summarized in an adaptive adjustment of the forwarded information related to the messages(data communication). We optimize these message information, and only the relevant information is transmitted to the relevant vehicles, and all other unnecessary information is terminated. Because, irrelevant information causes many problems, like unnecessary resource utilization and energy consumption. In this case, the driver will be promptly reminded to take appropriate measures. A swarm intelligence algorithm such as the Artificial Bee Colony (ABC) is utilized to optimize the communication mechanism. In this paper, we present a high-level framework for ABC based on the fuzzy logic for the VANETs. We prove the validity and efficiency of the proposed method. Our proposed method achieves higher optimization accuracy in information broadcasting with minimum delay and with the lowest energy consumption. [ABSTRACT FROM AUTHOR]

The regions obtained by image segmentation need to satisfy both the requirements of uniformity and connectivity. Image segmentation is the process of dividing an image into several specific regions. The result of image segmentation is a set of combinations covering the main feature areas of the whole image. The pixels in an area are similar to some or calculated characteristics, but there are obvious differences between adjacent areas. In this paper, a gray image segmentation algorithm based on fuzzy C-means combined with bee colony algorithm is proposed, which has strong optimization ability for multi-objective problems. By using the fuzzy membership function of the fuzzy C-means algorithm, the optimal clustering centers in the artificial bee colony optimization algorithm can be quickly calculated. It makes image segmentation faster and more accurate. The bee colony search algorithm is optimized and an effective local search algorithm is designed, it makes the bee colony converge to the optimal solution efficiently. Finally, the improved fuzzy C-means and artificial bee colony optimization algorithm are used to improve and optimize the seed region growth method. The multi-criteria are taken as the multi-objective optimization problem, and the segmentation results are finally obtained. Benefiting from our local search program and feature extraction in multi-color space, it makes the stability; efficiency and accuracy of image segmentation are higher. [ABSTRACT FROM AUTHOR]

BACKGROUND: A neurological disorder is one of the significant problems of the nervous system that affects the essential functions of the human brain and spinal cord. Monitoring brain activity through electroencephalography (EEG) has become an important tool in the diagnosis of brain disorders. The robust automatic classification of EEG signals is an important step towards detecting a brain disorder in its earlier stages before status deterioration. OBJECTIVE: Motivated by the computation capabilities of natural evolution strategies (NES), this paper introduces an effective automatic classification approach denoted as natural evolution optimization-based deep learning (NEODL). The proposed classifier is an ingredient in a signal processing chain that comprises other state-of-the-art techniques in a consistent framework for the purpose of automatic EEG classification. METHODS: The proposed framework consists of four steps. First, the L1-principal component analysis technique is used to enhance the raw EEG signal against any expected artifacts or noise. Second, the purified EEG signal is decomposed into a number of sub-bands by applying the wavelet transform technique where a number of spectral and statistical features are extracted. Third, the extracted features are examined using the artificial bee colony approach in order to optimally select the best features. Lastly, the selected features are treated using the proposed NEODL classifier, where the input signal is classified according to the problem at hand. RESULTS: The proposed approach is evaluated using two benchmark datasets and addresses two neurological disorder applications: epilepsy disease and motor imagery. Several experiments are conducted where the proposed classifier outperforms other deep learning techniques as well as other existing approaches. CONCLUSION: The proposed framework, including the proposed classifier (NEODL), has a promising performance in the classification of EEG signals, including epilepsy disease and motor imagery. Based on the given results, it is expected that this approach will also be useful for the identification of the epileptogenic areas in the human brain. Accordingly, it may find application in the neuro-intensive care units, epilepsy monitoring units, and practical brain-computer interface systems in clinics. [ABSTRACT FROM AUTHOR]

Conversion of the conventional vehicle (CV) into the plug-in hybrid electric vehicle (PHEV) is one of the promising solutions to improve transport sustainability and reduce outdoor air pollution. Energy management is crucial for the performance of PHEV. The paper presents combine rule based-artificial bee colony optimization algorithm for energy management of converted plug-in hybrid electric vehicle (CPHEV). The diesel operated parallel hybrid topology is considered for study with the designed electric powertrain. NOx and PM are considered as optimization parameters along with specific fuel consumption. The performance based on fuel consumption and emissions (NOx and PM) is analyzed by considering sample Indian urban and highway driving cycle. The complete vehicle is simulated using MATLAB Simulink linked with coding. The results of converted PHEV obtained is compared with conventional one for both driving cycles for analysis of the fuel consumption and emissions considering real-time benchmarking norms. The results indicate that the combine rule based-artificial bee colony strategy keeps pollution under control required as per BSIII norms. [ABSTRACT FROM AUTHOR]

3D Reconstruction has been an enduring problem in Image understanding and computer vision. There is an increasing interest on 3D information in the general public due to rapid development of 3D imaging techniques, marketing of 3D movies and games, and low cost of depth cameras. Numerous algorithms that have been proposed for performing 3D reconstruction using different variants of Iterative Closest Point(ICP) algorithm focusses mainly on reducing the computation time. The accuracy of 3D reconstruction is not taken in to consideration. An efficient, accurate, real time and active 3D reconstruction method using Kinect sensor is developed in this paper focusing on improving the accuracy of 3D reconstruction in less computation time. An Artificial Bee colony based ICP algorithm is proposed by incorporating several efficient variants of ICP algorithm. The proposed algorithm is intended to improve the accuracy and stability of the standard ICP algorithm. The performance of the proposed algorithm is satisfactory when compared with structured light technique and several ICP variants with respect to accuracy, complexity and computation speed. [ABSTRACT FROM AUTHOR]

With the progress in science and technology, many types of electrical equipment have been invented, making the use of electricity more extensive, and living environment more comfortable. However, in modern times, every country stresses the need to promote green energy in order to reduce environmental damage, while the Taiwanese government made an attempt to adjust electricity price as a means to make Taiwan people to reduce carbon emissions and pollution on the planet. Therefore, the paper takes electricity price on the power consumption of Taiwan people as the research object, observes tariff adjustment trends of relevant government departments, and builds Taiwan's average electricity consumption and the average price forecast model to provide references to government and researchers. Firstly, we gather data of electricity consumption and price from Taiwan Power Company's website, and draw a trend chart to explore the relationship between the two; and respectively work out technical indicators of average electric quantity and electricity prices by referring to stock technical indicators; finally, we compare Neural Network parameters optimized by Grey Fruit Fly Optimization Algorithm (GFOA) to build average power consumption and average electricity price forecasting models, and compare the best prediction model with other three algorithms. The study results demonstrate that the electricity consumption and electricity price trends have different characteristics; it is found out that the prediction model of smoothing parameter σ of General Regression Neural Network optimized by GFOA has better predictive ability compared to prediction models constructed by other three algorithms. [ABSTRACT FROM AUTHOR]

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]

Feature selection, which can reduce the dimension of feature space without sacrificing the performance of the classifier, is an effective technique in text categorization. As many classifiers cannot deal with the features with large dimensions, the noisy, irrelevant and redundant information must be filtered from the original feature space. On this basis, a random walk algorithm based feature selection method (called RWFS) is proposed in this paper. Firstly, an optimal feature selection method (called OPFS) is used to select some features from the training set. Secondly, the redundant features are filtered by combining the random walk algorithm and a pre-determined threshold. Moreover, in order to search the optimal threshold, an improved artificial bee colony method (called IMABC) is proposed for parameter optimization. In the experiments, support vector machine (SVM) and k-Nearest Neighbor (KNN) classifiers are used on four corpuses. The experimental results show that, the proposed method is significantly superior to six typical feature selections, and can greatly reduce the dimension of vector space while guaranteeing the classification accuracy as measured by F1 measurement. [ABSTRACT FROM AUTHOR]

Comprehensive Learning Particle Swarm Optimizer (CLPSO) is a state-of-the-art variant of PSO, which maintains the diversity of its swarm by learning from different exemplars on different dimensions. Preserving the swarm diversity enables CLPSO to address the premature convergence problem associated with the canonical PSO. In this paper, the performance of the recently proposed fuzzy-controlled CLPSO (FC-CLPSO) is investigated on 24 problems; five of them are real-world engineering problems and six high-dimensional problems. In addition, two new CLPSO variants inspired by the Artificial Bee Colony (ABC) algorithm are proposed, CLPSO-ABC and FC-CLSPO-ABC. These two methods are compared with CLPSO and FC-CLPSO. The results show that FC-CLPSO-ABC outperforms the other three methods. FC-CLSPO-ABC is then compared with three other state-of-the-art swarm intelligence approaches on 24 problems. The results show that FC-CLPSO-ABC generally outperforms the other approaches. [ABSTRACT FROM AUTHOR]

Color quantization is the process of reducing the number of colors in a digital image. The main objective of quantization process is that significant information should be preserved while reducing the color of an image. In other words, quantization process shouldn't cause significant information loss in the image. In this paper, a short review of color quantization is presented and a new color quantization method based on artificial bee colony algorithm (ABC) is proposed. The performance of the proposed method is evaluated by comparing it with the performance of the most widely used quantization methods such as K-means, Fuzzy C Means (FCM), minimum variance and particle swarm optimization (PSO). The obtained results indicate that the proposed method is superior to the others. [ABSTRACT FROM AUTHOR]