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For environmentally conscious and sustainable manufacturing, many more manufacturers are acting to recycle and remanufacture their post-consumed products. The most critical process of remanufacturing is disassembly, since it allows for the selective extraction of the valuable components and materials from returned products to reduce the waste disposal volume. It is, therefore, important to design and balance the disassembly line to work efficiently due to its vital role in effective resource usage and environmental protection. Considering the disassembly precedence relationships and sequence-dependent parts removal time increments, this paper presents an improved discrete artificial bee colony algorithm (DABC) for solving the sequence-dependent disassembly line balancing problem (SDDLBP). The performance of the proposed algorithm was tested against nine other approaches. Computational results evidently indicate the superior efficiency of the proposed algorithm for addressing the environmental and economic concerns while optimizing the multi-objective SDDLBP. [ABSTRACT FROM AUTHOR]

The distributed permutation flowshop scheduling problem (DPFSP) has attracted a lot of attentions in recent years. Furthermore, sequence-dependent setup time (SDST) often occurs in the job's production, and total flowtime becomes a more important and meaningful objective for the modern dynamic productive environment. Therefore, the DPFSP with SDST under minimization of total flowtime is investigated in this paper, and the mathematical model is established. To solve the problem, this paper presents three constructive heuristics and an effective discrete artificial bee colony (EDABC) method based on the acceleration. In the EDABC, the constructive heuristics are used to initialize population. Moreover, based on the problem-specific of sequence-dependent, the employed bees stage, onlooker bees stage and scout bees stage are improved by exploring the acceleration methods to evaluate the neighboring solutions. Further, to strengthen the performance of the proposed algorithm, three effective problem-oriented local search methods are developed, including referenced local search (RLS), improved local search 1 (ILS1) and improved local search 2 (ILS2). RLS is used to the best solution after the initialization, and ILS1 and ILS2 are designed in the framework of improved variable neighborhood descent. The extensive computational experiences are carried out, and the results show that the proposed methods, including three heuristics and EDABC, have outstanding performance for the SDST/DPFSP to minimize the total flowtime. • The DPFSP with SDST under minimization of total flowtime is investigated, and 3 heuristics are presented. • An effective discrete artificial bee colony (EDABC) method based on acceleration is proposed. • Experimental design and analysis of variance (ANOVA) are carried out to verify the proposed EDABC. • The experimental evaluation is used to verify the validity of constructive heuristics and EDABC. [ABSTRACT FROM AUTHOR]

Artificial Bee Colony (ABC) algorithm is a meta-heuristic algorithm, is inspired by the bee's food search behaviour based on swarm intelligence. Successful applications were performed on many optimization problems using this algorithm rising in popularity over the past few years. The update mechanism of the ABC algorithm, despite the fact that its exploration is good, faces the problem of convergence performance. For solving convergence problem of ABC Algorithm An Artificial Bee Colony Algorithm Based Fitness Weighted Search (ABCFWS) algorithm proposed in this paper. In this approach, an intelligent search space is proposed instead of the random search space of the ABC algorithm. In this method, the fitness values of the food source and the selected neighbour food source are taken as weights and a more balanced search space was found in the direction of the food source with better fitness value. The proposed method has been applied to 28 unconstrained numerical optimization test problems with different characteristics and the results were compared with the ABC algorithm variations. The results show that the proposed method is successful and competitive. [ABSTRACT FROM AUTHOR]

Modern power systems require advanced monitoring and control, a capability made possible by Phasor Measurement Units (PMUs) aided by synchrophasor technology. Because PMUs have significant costs, it is necessary to optimally place them in an electrical power network. This paper proposes the Optimal PMU Placement (OPP) on the Kenya Power Transmission Network (Nairobi Region 30-bus system) using the existing methods; The Depth-First method, the Mixed Integer Linear Programming (MILP) using intlinprog solver, and the Artificial Bee Colony (ABC) algorithm. The algorithms are first implemented on the IEEE-14 and 30 bus test systems for verification before implementing the Kenya Power Transmission Network (Nairobi Region 30-bus system). Finally, the results for the three methods are compared. A key consideration is the System Observability Redundancy Index (SORI) under normal baseload conditions, with and without the inclusion of Zero Injection Buses (ZIBs). A higher value of SORI increases the measurement redundancy of the PMUs installed at a given bus. This paper further proposes the modelling of ZIB with adjacent buses by considering their Observability Index (OI). The case studies are modelled in Power System Analysis Toolbox (PSAT), and the simulations are carried out in MATLAB. From the simulation results, the ABC algorithm gives the optimal solution with the highest SORI compared to the Depth-First method and MILP, with the exclusion of the ZIB. The Nairobi region 30-bus system require 12 PMUs located at buses; 2, 5, 8, 9, 11, 13, 14, 16, 21, 24, 27 and 29 for complete power system observability with a SORI of 43.

Drug names recognition (DNR) that seeks to discriminate drug mentions within the unstructured biomedical texts and categorize them into drug classes, is an essential task in mining medical information. The neural network structure considerably influences their overall performance. If there were lots of neurons within a network, it might fit the training dataset better, and the network might achieve few training errors, however it could be prone to over-fitting and the poor network generalization. Therefore, this paper proposes a novel network model for DNR based on a hybrid genetic artificial bee colony (GABC) algorithm. The algorithm enhances exploitation process of ABC and avoids the premature convergence problem by combining genetic operators into the ABC. These operators are hybridized in two phases of ABC: (1) exploitation process of onlooker bees phase, to enhance information sharing in between employed and onlooker bees which can find better solutions, and (2) scout bees phase to improve the replacement of any exhausted solution. The proposed network model for DNR was compared to some ether established models using different bio-inspired algorithms. The results reveal that the proposed model outperforms the other models in terms of predictive ability and computational time. [ABSTRACT FROM AUTHOR]

The N-puzzle problem is one of the most classical problems in mathematics. Since the number of states in the N-puzzle is equal to the factorial of the number of tiles, traditional algorithms can only provide solutions for small-scale ones, such as 8-puzzle. Various uninformed and informed search algorithms have been applied to solve the N-puzzle, and their performances have been evaluated. Apart from traditional methods, artificial intelligence algorithms are also used for solutions. This paper introduces a new approach based on a meta-heuristic algorithm with a solving of the 15-puzzle problem. Generally, only Manhattan distance is used as the heuristic function, while in this study, a linear conflict function is used to increase the effectiveness of the heuristic function. Besides, the puzzle was divided into subsets named pattern database, and solutions were obtained for the subsets separately with the artificial bee colony (ABC) algorithm. The proposed approach reveals that the ABC algorithm is very successful in solving the 15-puzzle problem. [ABSTRACT FROM AUTHOR]

Vibration-based feature extraction of multiple transient fault signals is a challenge in the field of rotating machinery fault diagnosis. Variational mode decomposition (VMD) has great potential for multiple faults decoupling because of its equivalent filtering characteristics. However, the two key hyper-parameters of VMD, i.e., the number of modes and balancing parameter, require to be predefined, thereby resulting in sub-optimal decomposition performance. Although some studies focused on the adaptive parameter determination, the problems in these improved methods like mode redundancy or being sensitive to random impacts still need to be solved. To overcome these drawbacks, an adaptive variational mode decomposition (AVMD) method is developed in this paper. In the proposed method, a novel index called syncretic impact index (SII) is firstly introduced for better evaluation of the complex impulsive fault components of signals. It can exclude the effects of interference terms and concentrate on the fault impacts effectively. The optimal parameters of VMD are selected based on the index SII through the artificial bee colony (ABC) algorithm. The envelope power spectrum, proved to be more capable for fault feature extraction than the envelope spectrum, is applied in this study. Analysis on simulated signals and two experimental applications based on the proposed method demonstrates its effectiveness over other existing methods. The results indicate that the proposed method outperforms in separating impulsive multi-fault signals, thus being an efficient method for multi-fault diagnosis of rotating machines. • A new syncretic impact index SII is proposed for detecting impulsive fault signals. • The AVMD method is developed for multi-fault feature separation. • Effectiveness and merits of AVMD are verified by simulated and experimental signals. [ABSTRACT FROM AUTHOR]

Plagiarism detection (PD) in natural language processing involves locating similar words in two distinct sources. The paper introduces a new approach to plagiarism detection utilizing bidirectional encoder representations from transformers (BERT)-generated embedding, an enhanced artificial bee colony (ABC) optimization algorithm for pre-training, and a training process based on reinforcement learning (RL). The BERT model can be incorporated into a subsequent task and meticulously refined to function as a model, enabling it to apprehend a variety of linguistic characteristics. Imbalanced classification is one of the fundamental obstacles to PD. To handle this predicament, we present a novel methodology utilizing RL, in which the problem is framed as a series of sequential decisions in which an agent receives a reward at each level for classifying a received instance. To address the disparity between classes, it is determined that the majority class will receive a lower reward than the minority class. We also focus on the training stage, which often utilizes gradient-based learning techniques like backpropagation (BP), leading to certain drawbacks such as sensitivity to initialization. In our proposed model, we utilize a mutual learning-based ABC (ML-ABC) approach that adjusts the food source with the most beneficial results for the candidate by considering a mutual learning factor that incorporates the initial weight. We evaluated the efficacy of our novel approach by contrasting its results with those of population-based techniques using three standard datasets, namely Stanford Natural Language Inference (SNLI), Microsoft Research Paraphrase Corpus (MSRP), and Semantic Evaluation Database (SemEval2014). Our model attained excellent results that outperformed state-of-the-art models. Optimal values for important parameters, including reward function are identified for the model based on experiments on the study dataset. Ablation studies that exclude the proposed ML-ABC and reinforcement learning from the model confirm the independent positive incremental impact of these components on model performance. • BERT-based plagiarism detection with RL and ML-ABC. • Reward function in RL improves detection of minority plagiarism class. • Model outperforms state-of-the-art on SNLI, MSRP, SemEval2014 datasets. • Ablation studies highlight the impact of ML-ABC and RL on performance. [ABSTRACT FROM AUTHOR]

Artificial bee colony (ABC) algorithm has shown good performance on many optimization problems. However, these problems mainly focus on single-objective and ordinary multi-objective optimization problems (MOPs). For many-objective optimization problems (MaOPs), ABC encounters some difficulties. The selection pressure based on Pareto-dominance degrades severely. It is hard to balance convergence and population diversity. To help ABC solve MaOPs, this paper proposes an improved two-archive many-objective ABC (called MaOABC-TA) algorithm. Inspired by the improved two-archive (Two_Arch2) method, MaOABC-TA uses two archives namely convergence archive (CA) and diversity archive (DA) to promote convergence and diversity. Based on CA and DA, three different search strategies are designed to strengthen convergence or diversity in different search stages. In addition, a new probability selection strategy is proposed to choose solutions with good diversity. To verify the performance of MaOABC-TA, it is compared with 10 many-objective evolutionary algorithms (MaOEAs) and 3 many-objective ABCs on DTLZ and MaF benchmark sets with 3, 5, 8, and 15 objectives. Two performance indicators including inverted generational distance (IGD) and hypervolume (HV) and utilized. Experimental results show that MaOABC-TA is more competitive than the compared algorithms in term of the IGD and HV values. • Two archives namely convergence archive (CA) and diversity archive (DA) are introduced into ABC. • Based on CA and DA, different search strategies are designed to enhance convergence or diversity. • A new probability selection strategy is proposed to choose solutions with good diversity. • Our approach is compared with 10 popular MaOEAs and 3 many-objective ABCs on two benchmarks. [ABSTRACT FROM AUTHOR]

This research work addresses the integrated scheduling of jobs and flexible (non-systematic) maintenance interventions in permutation flowshop production systems. We propose a coordinated model in which the time intervals between successive maintenance tasks as well as their number are assumed to be non-fixed for each machine on the shopfloor. With such a flexible nature of maintenance activities, the resulting joint schedule is more practical and representative of real-world scenarios. Our goal is to determine the best job permutation in which flexible maintenance activities are properly incorporated. To tackle the NP-hard nature of this problem, an artificial bee colony (ABC) algorithm is developed to minimize the total production time (Makespan). Experiments are conducted utilizing well-known Taillard's benchmarks, enriched with maintenance data, to compare the proposed algorithm performance against the variable neighbourhood search (VNS) method from the literature. Computational results demonstrate the effectiveness of the proposed algorithm in terms of both solution quality and computational times. [ABSTRACT FROM AUTHOR]

Background: Dento-maxillofacial deformities are common problems. Orthodontic–orthognathic surgery is the primary treatment but accurate diagnosis and careful surgical planning are essential for optimum outcomes. This study aimed to establish and verify a machine learning–based decision support system for treatment of dento-maxillofacial malformations. Methods: Patients (n = 574) with dento-maxillofacial deformities undergoing spiral CT during January 2015 to August 2020 were enrolled to train diagnostic models based on five different machine learning algorithms; the diagnostic performances were compared with expert diagnoses. Accuracy, sensitivity, specificity, and area under the curve (AUC) were calculated. The adaptive artificial bee colony algorithm was employed to formulate the orthognathic surgical plan, and subsequently evaluated by maxillofacial surgeons in a cohort of 50 patients. The objective evaluation included the difference in bone position between the artificial intelligence (AI) generated and actual surgical plans for the patient, along with discrepancies in postoperative cephalometric analysis outcomes. Results: The binary relevance extreme gradient boosting model performed best, with diagnostic success rates > 90% for six different kinds of dento-maxillofacial deformities; the exception was maxillary overdevelopment (89.27%). AUC was > 0.88 for all diagnostic types. Median score for the surgical plans was 9, and was improved after human–computer interaction. There was no statistically significant difference between the actual and AI- groups. Conclusions: Machine learning algorithms are effective for diagnosis and surgical planning of dento-maxillofacial deformities and help improve diagnostic efficiency, especially in lower medical centers. [ABSTRACT FROM AUTHOR]

Optimum path planning is a fundamental necessity for the successful functioning of a mobile robot in industrial applications. This research work investigates the application of the artificial bee colony (ABC) approach, probabilistic roadmap (PRM) method, and evolutionary programming (EP) algorithm to tackle the issue of single and multi-robot path planning in partially known or unknown industrial complex environments. Conventional techniques depend on external factors such as delay of information from one bee's stage to another for selecting neighbour food points. Due to this, its efficiency is comparatively low and might result in longer runtimes. To address these challenges, a novel hybrid framework based on ABC-PRM-EP has been introduced. Firstly, a suboptimal initial feasible path is attained by a new framework (ABC-PRM) within the mobile robot sensor detection range. Then, EP performs refinement of that attained suboptimal path to provide a short and optimum path. Also, a multi-robot collaboration strategy has been introduced based on the concept of hold-up. A number of comparative studies have been conducted in three different test scenarios with different complexity to validate the proposed framework efficiency and performance. Different performance indices such as path length (m), smoothness (rad), collision safety value, success rate, processing time (s), and convergence speed have been measured to validate the effectiveness of the proposed framework. The comparative analysis obtained from these test scenarios indicates that the proposed framework outperforms conventional ABC, ABC-EP and HPSO-GWO-EA, while performing path planning. [ABSTRACT FROM AUTHOR]

Frequency-domain electromagnetic induction (FDEMI) methods are frequently used in non-invasive, area-wise mapping of the subsurface electromagnetic soil properties. A crucial part of data analysis is the geophysical inversion of the data, resulting in either conductivity and/or magnetic susceptibility subsurface distributions. We present a novel 1D stochastic optimization approach that combines dimension-adapting reversible jump Markov chain Monte Carlo (MCMC) with artificial bee colony (ABC) optimization for geophysical inversion, with specific application to frequency-domain electromagnetic induction (FDEMI) data. Several solution models of simplified model geometry and a variable number of model knots, which are found by the inversion method, are used to create re-sampled resulting average models. We present synthetic test inversions using conductivity models based on 14 direct-push (DP) EC logs from Greece, Italy, and Germany, as well as field data applications using multi-coil FDEMI devices from three sites in Azerbaijan and Germany. These examples show that the method can effectively lead to solutions that resemble the known DP input models or image reasonable stratigraphic and archaeological features in the field data. Neighboring 1D solutions on field data examples show high coherence along profiles even though each 1D inversion is independently handled. The computational effort for one 1D inversion is less than 120,000 forward calculations, which is much less than usually needed in MCMC inversions, whereas the resulting models show more plausible solutions due to the dimension-adapting properties of the inversion method. [ABSTRACT FROM AUTHOR]

Cloud computing is increasing rapidly as a successful paradigm presenting on-demand infrastructure, platform, and software services to clients. Load balancing is one of the important issues in cloud computing to distribute the dynamic workload equally among all the nodes to avoid the status that some nodes are overloaded while others are underloaded. Many algorithms have been suggested to perform this task. Recently, worldview is turning into a new paradigm for optimization search by applying the osmosis theory from chemistry science to form osmotic computing. Osmotic computing is aimed to achieve balance in highly distributed environments. The main goal of this paper is to propose a hybrid metaheuristics technique which combines the osmotic behavior with bio-inspired load balancing algorithms. The osmotic behavior enables the automatic deployment of virtual machines (VMs) that are migrated through cloud infrastructures. Since the hybrid artificial bee colony and ant colony optimization proved its efficiency in the dynamic environment in cloud computing, the paper then exploits the advantages of these bio-inspired algorithms to form an osmotic hybrid artificial bee and ant colony (OH_BAC) optimization load balancing algorithm. It overcomes the drawbacks of the existing bio-inspired algorithms in achieving load balancing between physical machines. The simulation results show that OH_BAC decreases energy consumption, the number of VMs migrations and the number of shutdown hosts compared to existing algorithms. In addition, it enhances the quality of services (QoSs) which is measured by service level agreement violation (SLAV) and performance degradation due to migrations (PDMs).

This paper presents an efficient Artificial Bee Colony (ABC) algorithm for solving large scale economic load dispatch (ELD) problems in power networks. To realize the ELD, the valve-point loading effect, system load demand, power losses, ramp rate limits and prohibited operation zones are considered here. Simulations were performed on four different power systems with 3, 6, 15 and 40 generating units and the results are compared with two forms of power systems, one power system is with a wind power generator and other power system is without a wind power generator. The results of this study reveal that the proposed approach is able to find appreciable ELD solutions than those of previous algorithms. [ABSTRACT FROM AUTHOR]

Accurate fruit classification is difficult to accomplish because of the similarities among the various categories. In this paper, we proposed a novel fruit-classification system, with the goal of recognizing fruits in a more efficient way. Our methodology included the following steps. First, a four-step pre-processing was employed. Second, the features (colour, shape, and texture) were extracted. Third, we utilized principal component analysis to remove excessive features. Fourth, a novel fruit-classification system based on biogeography-based optimization (BBO) and feedforward neural network (FNN) was proposed, with the short name of BBO-FNN. The experiment employed over 1653 chromatic fruit images (18 categories) by fivefold stratified cross-validation. The results showed that the proposed BBO-FNN yielded an overall accuracy of 89.11%, which was higher than the five state-of-the-art methods: genetic algorithm-FNN, artificial bee colony-FNN, particle swarm optimization-FNN, kernel support vector machine, and ant colony optimization-FNN. Also, the BBO-FNN achieved the same accuracy as fitness-scaling chaotic artificial bee colony-FNN, but it performed much faster than the latter. The proposed BBO-FNN was effective in fruit-classification in terms of classification accuracy and computation time. This indicated that it can be applied in credible use. [ABSTRACT FROM AUTHOR]

In this paper, a grasping posture control for a robotic arm is developed based on novel adaptive particle swarm optimization (PSO) for the home service robot. To grasp an object using the robotic arm of the home-service robot, both the spatial coordinates of the target and the appropriate collocation of the grasping posture should be examined. In this paper, we present another method for dealing with this problem, which integrates the artificial bee colony (ABC) algorithm into the adaptive particle swarm optimization (APSO) algorithm, where the mutation concept of the scout bee in the ABC algorithm is used to increase the diversity of the particles. In addition, adaptive acceleration coefficients and adaptive inertia weight are presented to ameliorate the convergence rate of the PSO algorithm. We name this control scheme AIWCPSO-S, which represents Adaptive Inertia Weight and acceleration Coefficients PSO with the aid of the Scout bee. Performance comparisons of existing ABC, global ABC, adaptive inertia weight PSO, low-discrepancy sequence initialized PSO algorithm with high-order nonlinear time-varying inertia weight (LHNPSO), oscillating triangular inertia weight PSO (OTIWPSO) and AIWCPSO-S algorithms are conducted by computer simulations. The experiment results show that the presented algorithm gives the most correct and fastest convergence capability. [ABSTRACT FROM AUTHOR]

Modeling user knowledge and creating user profiles not only for special web-based social media but also for complex and mixed personalized learning environments are important research challenges. The key component for adaptation is the user's knowledge model. This paper introduces fuzzy metric (FM)-based novel and efficient similarity measurement method and adaptive artificial neural network (AANN) and artificial bee colony (ABC)-based knowledge classification approaches for personalized learning environments. For this purpose, FM-based method has been developed to measure distances more efficiently among the users and their knowledge model using the web logs/session data. In addition, a novel knowledge classifier based on ABC and AANN having combined with the generic object model has been developed for user modeling strategies and user modeling server of adaptive educational electric course (AEEC). Finally, the approaches have been tested to compare the classification performance of the user modeling methods developed for user modeling task. The experimental results have shown that proposed methods have improved similarity measurements considerably and decreased the misclassifications in user modeling processes. Thus, powerful user modeling approaches have been presented to the literature. It is expected that the approaches introduced in this article can be a reference to others researches and to develop more adaptive and personalized web applications in future. [ABSTRACT FROM AUTHOR]

In this paper, we improve D. Karaboga's Artificial Bee Colony (ABC) optimization algorithm, by using the sensitivity analysis method described by Morris. Many improvements of the ABC algorithm have been made, with effective results. In this paper, we propose a new approach of random selection in neighborhood search. As the algorithm is running, we apply a sensitivity analysis method, Morris’ OAT (One-At-Time) method, to orientate the random choice selection of a dimension to shift. Morris’ method detects which dimensions have a high influence on the objective function result and promotes the search following these dimensions. The result of this analysis drives the ABC algorithm towards significant dimensions of the search space to improve the discovery of the global optimum. We also demonstrate that this method is fruitful for more recent improvements of ABC algorithm, such as GABC, MeABC and qABC. [ABSTRACT FROM AUTHOR]

Data clustering is a common data mining techniques used in many applications such as data analysis and pattern recognition. K-means algorithm is the common clustering method which has fallen into the trap of local optimization and does not always create the optimized response to the problem, although having more advantages such as high speed. Artificial bee colony (ABC) is a novel biological-inspired optimization algorithm, having the advantage of less control parameters, strong global optimization ability and easy to implement. However, there are still some problems in ABC algorithm, like inability to find the best solution from all possible solutions. Due to the large step of searching equation in ABC, the chance of skipping the true solution is high. Therefore, in this paper, to balance the diversity and convergence ability of the ABC, Mantegna Lévy distribution random walk is proposed and incorporated with ABC. The new algorithm, ABCL, brings the power of the Artificial Bee Colony algorithm to the K-means algorithm. The proposed algorithm benefits from Mantegna Lévy distribution to promote the ABC algorithm in solving the number of functional evaluation and also obtaining better convergence speed and high accuracy in a short time. We empirically evaluate the performance of our proposed method on nine standard datasets taken from the UCI Machine Learning Repository. The experimental results show that the proposed algorithm has ability to obtain better results in terms of convergence speed, accuracy, and reducing the number of functional evaluation. [ABSTRACT FROM AUTHOR]

Nature-inspired meta-heuristics have gained popularity for solutions to many real-world complex problems, and the artificial bee colony algorithm is one of the most powerful optimisation methods among meta-heuristics. However, inefficient exploitation of onlooker bees prevents the artificial bee colony algorithm from finding the final result accurately and efficiently for complex problems. In this paper, a novel optimisation method is proposed based on the artificial bee colony algorithm. The proposed optimisation method adaptively exploits onlooker bees over generations. In addition, the proposed optimisation method is applied to a stereo-matching problem to minimise the segment-based integer energy function, which is also introduced in this paper. The experimental results show that the proposed optimisation method outperforms state-of-the-art population-based meta-heuristics, such as the genetic algorithm, differential evolution, conventional artificial bee colony, and clonal selection algorithm, for benchmark functions as well as for the stereo-matching problem. [ABSTRACT FROM AUTHOR]

When designing radar, mobile, or satellite communication systems, optimization problems often come out and need to be handled to accomplish certain requirements. Polyphase code design and circular antenna array design problems are considered in this paper. To deal with the two problems, a novel artificial bee colony (ABC) algorithm is proposed. A population reduction method is used in the proposed algorithm. Large population is initially set for exploration search, while population is reduced to a small one for exploitation search. Moreover, a new boundary repair method is proposed to amend the candidate solutions that violate boundary constraints. It hybridizes four popularly used repair methods in literature. The resulting algorithm is called population reduction and hybrid repair ABC (PRHRABC). Experiments are conducted on the two design problems. Results show that PRHRABC presents promising performance in dealing with the problems compared with standard and a state-of-the-art ABC algorithms. [ABSTRACT FROM AUTHOR]

In blasting works, the aim is to provide proper rock fragmentation and to avoid undesirable environmental impacts such as back-break. Therefore, predicting fragmentation and back-break is a significant step in achieving a technically and economically successful outcome. In this paper, considering the robustness of artificial intelligence methods utilized in engineering problems, an artificial neural network (ANN) was applied to predict rock fragmentation and back-break; an artificial bee colony (ABC) algorithm was also utilized to optimize the blasting pattern parameters. In this regard, blasting parameters, including burden, spacing, stemming length, hole length and powder factor, as well as back-break and fragmentation were collected at the Anguran mine in Iran. Root mean square error (RMSE) values equal to 2.76 and 0.53 for rock fragmentation and back-break, respectively, reveal the high reliability of the ANN model. In addition, ABC algorithm results suggest values of 29 cm and 3.25 m for fragmentation and back-break, respectively. For comparison purposes, an empirical model (Kuz-Ram) was performed to predict the mean fragment size in the Anguran mine. A mean fragment size of 33.5 cm shows the ABC algorithm can optimize rock fragmentation with a high degree of accuracy. [ABSTRACT FROM AUTHOR]

In data mining, classification rule learning extracts the knowledge in the representation of IF_THEN rule which is comprehensive and readable. It is a challenging problem due to the complexity of data sets. Various meta-heuristic machine learning algorithms are proposed for rule learning. Cooperative rule learning is the discovery process of all classification rules with a single run concurrently. In this paper, a novel cooperative rule learning algorithm, called CoABCMiner, based on Artificial Bee Colony is introduced. The proposed algorithm handles the training data set and discovers the classification model containing the rule list. Token competition, new updating strategy used in onlooker and employed phases, and new scout bee mechanism are proposed in CoABCMiner to achieve cooperative learning of different rules belonging to different classes. We compared the results of CoABCMiner with several state-of-the-art algorithms using 14 benchmark data sets. Non parametric statistical tests, such as Friedman test, post hoc test, and contrast estimation based on medians are performed. Nonparametric tests determine the similarity of control algorithm among other algorithms on multiple problems. Sensitivity analysis of CoABCMiner is conducted. It is concluded that CoABCMiner can be used to discover classification rules for the data sets used in experiments, efficiently. [ABSTRACT FROM AUTHOR]

The demand for better intrusion detection systems, especially anomaly intrusion detection, increases daily, as new attacks arise and Internet speeds increase. The criterion for a good intrusion detection system is to detect emerging attacks with high accuracy at line rates. Existing systems suffer from high false positives and negatives, and are unable to handle increasing traffic rates. This paper applies artificial bee colony for anomaly-based intrusion detection systems. In addition, it uses two feature selection techniques to reduce the amount of data used for detection and classification. KDD Cup 99 dataset was used to evaluate the proposed algorithm. Experimental results show that artificial bee colony achieves average accuracy rate of 97.5% for known attacks and 93.2% overall for known and unknown attacks. The new algorithm outperforms all methods reported in the literature. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Feature selection is the basic pre-processing task of eliminating irrelevant or redundant features through investigating complicated interactions among features in a feature set. Due to its critical role in classification and computational time, it has attracted researchers’ attention for the last five decades. However, it still remains a challenge. This paper proposes a binary artificial bee colony (ABC) algorithm for the feature selection problems, which is developed by integrating evolutionary based similarity search mechanisms into an existing binary ABC variant. The performance analysis of the proposed algorithm is demonstrated by comparing it with some well-known variants of the particle swarm optimization (PSO) and ABC algorithms, including standard binary PSO, new velocity based binary PSO, quantum inspired binary PSO, discrete ABC, modification rate based ABC, angle modulated ABC, and genetic algorithms on 10 benchmark datasets. The results show that the proposed algorithm can obtain higher classification performance in both training and test sets, and can eliminate irrelevant and redundant features more effectively than the other approaches. Note that all the algorithms used in this paper except for standard binary PSO and GA are employed for the first time in feature selection. [ABSTRACT FROM AUTHOR]

In this paper, the multiuser linear cooperative spectrum sensing optimization problem in cognitive radio system where the primary user (PU) and cognitive radio (CR) employ multiple antennas is investigated. By optimizing the different weights assigned on the statistic of each CR given a targeted probability of false alarm, the cooperative spectrum sensing optimization focuses on maximizing the probability of detection. Statistical characteristics of parameters in cooperative spectrum sensing for the PU with a single antenna and the CR with multiple antennas (SPMC) system, the PU with multiple antennas and the CR with a single antenna (MPSC) system, both the PU and the CR with multiple antennas (MPMC) system as well as the PU with Alamouti coding system have been investigated. Due to the non-convex characteristic of the cooperative spectrum sensing problem, an efficient parallel artificial bee colony (PABC) method motivated by the intelligent foraging behavior of a honeybee colony is introduced to address the problem without approximations and convexity constraints. Furthermore, classical benchmark functions are presented to validate the searching ability of the proposed algorithm. Simulation results showed that the PABC shows a superior ability when applied in both the typical test functions and the cooperative spectrum sensing scenario. Also, the reliability of spectrum sensing can be significantly promoted with the use of multiple antennas and Alamouti coding. [ABSTRACT FROM AUTHOR]

K-means clustering is a simple and efficient, fast in convergence and easy to implement statistical analysis method. However, the traditional [K-means] clustering algorithm is sensitive to the selection of initial clustering centers and easy to fall into a local optimum, and at the same time, most unsupervised feature selection algorithms are easy to ignore the relationship between features. To solve the above issues, this paper proposes a feature selection algorithm combining artificial bee colony with [K-means] clustering. Firstly, to make the similarity of samples in the same cluster high and the similarity of the samples in different clusters low, a new fitness function is constructed based on the clustering degree within the cluster and the dispersion degree between the clusters, which can better reflect the characteristics of each sample, and then a new probability expression of the honey source being selected is constructed. Secondly, the weight which decreases gradually with the increase of the number of iterations is designed, and the honey source location update expression that makes the search range of the bee colony dynamically indent is proposed. Thirdly, to make up for the limitation of the traditional Euclidean distance which only considers the cumulative difference between vectors when calculating the distance, a weighted Euclidean distance expression which simultaneously considers both the different influence degrees of the samples and the similarity of the samples is constructed. Finally, the standard deviation and distance correlation coefficient are introduced to define feature discrimination and feature representativeness, and the product of them is used to measure the importance of features. Experimental results show that the proposed algorithm accelerates the convergence speed of artificial bee colony algorithm and improves the clustering effect of [K-means] algorithm, and also effectively improves the classification effect of feature selection.

Scouring of bridge piers is a major cause of bridge failure worldwide. Thus, designing safe depths for new bridge foundations and assessing/monitoring the safety of existing bridge foundations are critical to reducing the risk of bridge collapse and the subsequent potential losses in terms of life and property. This paper develops and tests the evolutionary radial basis function neural network (ERBFNN) as a model to forecast scour depth at bridge piers. The ERBFNN is an artificial intelligence (AI) inference model that integrates the radial basis function neural network (RBFNN) and the artificial bee colony (ABC). In the ERBFNN, the RBFNN handles the learning and fitting curves and ABC uses optimization to search for the optimal hidden neuron number Nn and width s of the Gaussian function. The performance of the ERBFNN is compared with four other AI techniques, including the back-propagation neural network (BPNN), genetic programming (GP), M5 regression tree (M5), and support vector machine (SVM). Further, the prediction accuracy of the ERBFNN is benchmarked against four prevalent mathematical methods, including the HEC-18 method, Mississippi's method, Laursen and Toch's method, and Froehlich's method. Results of these comparisons demonstrate that the ERBFNN predicts scour depth at bridge piers with a degree of accuracy that is significantly better than current, widely used methods. [ABSTRACT FROM AUTHOR]

In the modern manufacturing arena, environmental and economical concerns draw considerable attention from both practitioners and researchers towards remanufacturing practices. The success of remanufacturing firms depends on how efficiently the recovery process is executed. Radio Frequency Identification (RFID) technology holds immense potential to enhance the recovery process. The deployment of RFID technology at reverse echelons has the advantage of having a real time system with reduced inventory shrinkage, reduced processing time, reduced labor cost, process accuracy, and other directly measurable benefits. In spite of these expected benefits, the heavy financial investment required in implementing the RFID system is a big threat for remanufacturing companies. This paper examines the economical impact of RFID adoption to remanufacturing. The aim of the research is to compare the basic and RFID-diffused reverse logistics model, and to quantitatively decide whether RFID implementation is economically viable. In order to meet these objectives, we have proposed a Chaos-based Interactive Artificial Bee Colony (CI-ABC) algorithm. Numerical results from using the CI-ABC for optimal performance are presented and analyzed. Comparison between the canonical Artificial Bee Colony and the Particle Swarm Optimization reveals the superiority of the CI-ABC for this application. [ABSTRACT FROM AUTHOR]

This paper proposed an artificial bee colony optimization (ABC) algorithm based despeckling framework to overcome the effect of speckle noise present in real ultrasound images. A low pass filter and fast non-local mean filter along with Artificial Bee Colony (ABC) optimization algorithm are used for the quality enhancement of ultrasound images. The output results obtained for the real ultrasound images filtered with the proposed approach and the other most studied approaches discussed in the literature. The outperformance of the proposed method is verified by calculation of peak signal to noise ratio (PSNR), mean square error (MSE), mean absolute error (MAE), and structure similarity index (SSIM) quality measures. The proposed filtering approach is tested on eight real clinical ultrasound images of adrenal gland, appendicitis, bladder, pancreas, parathyroid gland, scrotal gland, thoracic wall, and uterus. The experimental results yield that the quantitative and qualitative results of the proposed framework are better than benchmark despeckling methods compared to real ultrasound images. Further, the proposed framework also preserves the fine details in real ultrasound images. [ABSTRACT FROM AUTHOR]

In most wireless sensor networks (WSN), multi-hop routing algorithm is used to transmit the data collected by sensors to user. Multi-hop forwarding leads to energy hole problem and high transmission overhead in large scale WSN. In order to address these problems, this paper proposes multiple mobile sink based data collection algorithm, which introduces energy balanced clustering and Artificial Bee Colony based data collection. The cluster head election is based on the residual energy of the node. In this study, we focused on a large-scale and intensive WSN which allows a certain amount of data latency by investigating mobile Sink balance from three aspects: data collection maximization, mobile path length minimization, and network reliability optimization. Simulation results show that, in comparison with other algorithms such Random walk and Ant Colony Optimization, the proposed algorithm can effectively reduce data transmission, save energy, improve network data collection efficiency and reliability, and extend the network lifetime. [ABSTRACT FROM AUTHOR]

The determination is presented of seven parameters of a DC motor's drive. The determination was based on a comparison between the measured and simulated current and speed responses. For the parameters' determination, different evolutionary methods were used and compared to each other. The mathematical model presenting the DC drives model was written using two coupled differential equations, which were solved using the Runge–Kutta first-, second-, third- and fourth-order methods. The approach allows determining the parameters of controlled drives in such a way that the controller is taken into account with the measured voltage. Between the tested evolutionary methods, which were Differential Evolution with three strategies, Teaching-Learning Based Optimization and Artificial Bee Colony, the Differential Evolution (DE/rand/1/exp) can be suggested as the most appropriate for the presented problem. Measurements with different sampling times were used, and it was found out that at least some measuring points should be at the speed-up interval. Different lengths of the measured signal were tested, and it is sufficient to use a signal consisting of the drive's acceleration and a short part of the stationary operation. The analysis showed that the procedure has good repeatability. The biggest deviation of calculated parameters considering 10 repeated measurements was 6% in case of the La calculation. The deviations of all the other parameters' calculations were less than 2%. [ABSTRACT FROM AUTHOR]

The artificial bee colony algorithm (ABC) struggles in handling complex optimization problems with high dimensions in light of its search operators' strong exploration and weak exploitation properties. To tackle this situation, in this study, we propose a bi-preference linkage-driven ABC algorithm with multi-operator fusion, named BPLABC. BPLABC couples a preference-free stochastic search operator with a global best-guided search operator in the employed bee phase to maintain the population diversity while enhancing the population quality. During the onlooker bee phase, a tailored bi-type elite-guided exploitation mechanism is employed to regulate the exploitation intensity of the promising elite nectar sources selected via a new roulette selection probability calculation paradigm. To discourage the onlooker bees from slipping into local traps, after the scout bee phase, an auxiliary adversarial search operator is assembled to tug certain promising elite solutions away from the present pseudo-global best solution. To illustrate the effectiveness and efficiency of BPLABC, two sets of test suits consisting of 23 benchmark problems, 30 complex CEC2014 functions, and two real-world problems are picked for testing. Experimental results showed that BPLABC can achieve superior or equivalent performance to several representative ABC variants on the majority of the tested problems. [ABSTRACT FROM AUTHOR]

Linear Quadratic Regulator is one of the most common ways to control a linear system. Despite Linear Quadratic Regulator’s (LQR) strong performance and solid resilience, developing these controllers have been challenging, largely because there is no reliable way to choose the Q and R weighing matrices. In this regard a deterministic method is used for choosing them in this paper, providing the designers a precise control over performance variables. An Artificial Bee Colony (ABC) optimisation is also used to find the sub-optimal gain matrices along with an analytical approach based on neural networks. A comparative study of the three approaches is performed using MATLAB simulations. These three approaches are applied on an inverted pendulum–cart system due to its complexity and dexterity. The results show that all the three methods show comparable performances with the proposed analytical method being slightly better in terms of transient characteristics.

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]

In this paper, a multi-product multi-machine serial production line operated under a constant-work-in-process protocol is considered. A mathematical model for the system is first presented, and then an artificial bee colony optimization algorithm is applied to simultaneously find the optimal work-in-process inventory level as well as job sequence order in order to minimize the overall makespan time. Unlike many existing approaches, which are based on deterministic search algorithms such as nonlinear programming and mixed integer programming, the proposed method does not use a linearized or simplified model of the system. A production line simulator implemented on MATLAB is, instead, employed to model the highly nonlinear dynamics of the production line and is used to evaluate the candidate solutions. The efficiency of the proposed approach, even for systems of large sizes, is validated via numerical simulations. [ABSTRACT FROM AUTHOR]

Abstract: The design of reliable DNA sequences is crucial in many engineering applications which depend on DNA-based technologies, such as nanotechnology or DNA computing. In these cases, two of the most important properties that must be controlled to obtain reliable sequences are self-assembly and self-complementary hybridization. These processes have to be restricted to avoid undesirable reactions, because in the specific case of DNA computing, undesirable reactions usually lead to incorrect computations. Therefore, it is important to design robust sets of sequences which provide efficient and reliable computations. The design of reliable DNA sequences involves heterogeneous and conflicting design criteria that do not fit traditional optimization methods. In this paper, DNA sequence design has been formulated as a multiobjective optimization problem and a novel multiobjective approach based on swarm intelligence has been proposed to solve it. Specifically, a multiobjective version of the Artificial Bee Colony metaheuristics (MO-ABC) is developed to tackle the problem. MO-ABC takes in consideration six different conflicting design criteria to generate reliable DNA sequences that can be used for bio-molecular computing. Moreover, in order to verify the effectiveness of the novel multiobjective proposal, formal comparisons with the well-known multiobjective standard NSGA-II (fast non-dominated sorting genetic algorithm) were performed. After a detailed study, results indicate that our artificial swarm intelligence approach obtains satisfactory reliable DNA sequences. Two multiobjective indicators were used in order to compare the developed algorithms: hypervolume and set coverage. Finally, other relevant works published in the literature were also studied to validate our results. To this respect the conclusion that can be drawn is that the novel approach proposed in this paper obtains very promising DNA sequences that significantly surpass other results previously published. [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.

This paper presents the results of a simulation model regarding the productivity and safety working space for construction workers through the floors of a building using swarm intelligence (SI), a field of artificial intelligence (AI), and specifically using artificial bee colony (ABC) optimization. After designing the algorithm used to build the simulation model, the simulation was used in an actual building project by comparing the travel times of workers conventionally transporting material with another group working on routes optimized by the algorithm. Thus, the proposed algorithm provides routes combining shorter travel times and correct distances between workers when transporting materials in a construction site, handling the interference between crews. After validating the algorithm on-site, no statistically significant differences were found between the travel times of workers and the times delivered by the algorithm. Additionally, the travel times using the routes obtained through the algorithm were significantly lower than those made by workers who moved freely without a predefined route. In summary, the algorithm proposed may help construction practitioners maintain safe movements that respond to hazard contexts imposed by any restriction that demands a safety distance.

Abstract: The Permutation Flow Shop Scheduling Problem (PFSSP) is an NP-hard problem of wide engineering and theoretical background. In this paper, a kind of discrete artificial bee colony with composite mutation strategies is presented to compensate the defects of the single mutation scheme that is easy to get into the local best for PFSSP, named CDABC. Firstly, to make ABC suitable for PFSSP, we regard each discrete job permutation as a food source and apply discrete operations to generate a new neighbourhood food source for different bees. Secondly, the Nawaz-Enscore-Ham (NEH) heuristic is combined with the random initialization to initialize the population with a certain quality and diversity. Thirdly, the composite mutation strategies are proposed to enable the DABC to solve the permutation flow shop scheduling. Finally, the fast local search is used for enhancing the best individual. Within our knowledge, there are few papers to discuss artificial bee colony algorithm about PFSSP with the objective of minimizing total flow time and maximum lateness of jobs. In this sense, our work can be viewed as a start point for researchers to develop ABC-based algorithms to solve PFSSP. Additionally, simulations and comparisons based on PFSSP benchmarks are carried out, which shows that our algorithm is very competitive. We have also evaluated our algorithm with the well known DMU problems. For the problems with the objective of minimizing makespan, the algorithm CDABC obtains 26 new upper bounds of the 40 instances, and for the problems with the objective of maximum lateness, CDABC obtains 137 new upper bounds of the 160 instances. These new upper bounds can be used for future algorithms to compare their results with ours. [Copyright &y& Elsevier]

Software test suite optimization is one of the most important problems in software engineering research. This paper deals with Automatic Generation of Feasible Independent Paths and Software Test Suite Optimization using Artificial Bee Colony (ABC) based novel search technique. In this approach, ABC combines both global search methods done by scout bees and local search method done by employed bees and onlooker bees. The parallel behavior of these three bees makes generation of feasible independent paths and software test suite optimization faster. Test Cases are generated using Test Path Sequence Comparison Method as the fitness value objective function. This paper also presents an approach for the automated generation of feasible independent test path based on the priority of all edge coverage criteria. Finally, this paper compares the efficiency of ABC based approach with various approaches. [ABSTRACT FROM AUTHOR]

Abstract: Artificial bee colony (ABC) algorithm is an optimization algorithm based on the intelligent foraging behavior of honeybee swarm. Optimal reactive power flow (ORPF) based on ABC algorithm to minimize active power loss in power systems is studied in this paper. The advantage of ABC algorithm is that it does not require these parameters, because it is very difficult to determine external parameters such as cross over rate and mutation rate as in case of genetic algorithm and differential evolution. The other advantage is that global search ability of the algorithm is implemented by introducing a neighborhood source production mechanism which is similar to mutation process. Because of these features, ABC algorithm attracts much attention in recent years and has been used successfully in many areas. ORPF problem is one of these areas. In this paper, proposed algorithm is tested on both standard IEEE 30-bus test system and IEEE 118-bus test system. To show the effectiveness of proposed algorithms, the obtained results are compared with different approaches as available in the literature. [Copyright &y& Elsevier]

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

Mobile ad hoc networks (MANETs) are multi-hop wireless networks of mobile nodes constructed dynamically without the use of any fixed network infrastructure. Due to inherent characteristics of these networks, malicious nodes can easily disrupt the routing process. A traditional approach to detect such malicious network activities is to build a profile of the normal network traffic, and then identify an activity as suspicious if it deviates from this profile. As the topology of a MANET constantly changes over time, the simple use of a static profile is not efficient. In this paper, we present a dynamic hybrid approach based on the artificial bee colony (ABC) and negative selection (NS) algorithms, called BeeID, for intrusion detection in AODV-based MANETs. The approach consists of three phases: training, detection, and updating. In the training phase, a niching artificial bee colony algorithm, called NicheNABC, runs a negative selection algorithm multiple times to generate a set of mature negative detectors to cover the nonself space. In the detection phase, mature negative detectors are used to discriminate between normal and malicious network activities. In the updating phase, the set of mature negative detectors is updated by one of two methods of partial updating or total updating. We use the Monte Carlo integration to estimate the amount of the nonself space covered by negative detectors and to determine when the total updating should be done. We demonstrate the effectiveness of BeeID for detecting several types of routing attacks on AODV-based MANETs simulated using the NS2 simulator. The experimental results show that BeeID can achieve a better tradeoff between detection rate and false-alarm rate as compared to other dynamic approaches previously reported in the literature. [ABSTRACT FROM AUTHOR]

Intrusion detection systems (IDSs) has been considered as the main component of a safe network. One of the problems of these security systems is false alarm report of intrusion to the network and intrusion detection accuracy that happens due to the high volume of network data. This paper proposes a new reliable hybrid method for an anomaly network-based IDS (A-NIDS) using artificial bee colony (ABC) and AdaBoost algorithms in order to gain a high detection rate (DR) with low false positive rate (FPR). ABC algorithm is used to feature selection and AdaBoost are used to evaluate and classify the features. Results of the simulation on NSL-KDD and ISCXIDS2012 datasets confirm that this reliable hybrid method has a significant difference from other IDS, which are accomplished according to the same dataset. It has demonstrated differently better performance in different attacks-based scenarios. The accuracy and detection rate of this method has been improved in comparison with legendary methods. [ABSTRACT FROM AUTHOR]