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This paper proposes a new intrusion detection system (IDS) based on a combination of a multilayer perceptron (MLP) network, and artificial bee colony (ABC) and fuzzy clustering algorithms. Normal and abnormal network traffic packets are identified by the MLP, while the MLP training is done by the ABC algorithm through optimizing the values of linkage weights and biases. The CloudSim simulator and NSL-KDD dataset are used to verify the proposed method. Mean absolute error (MAE), root mean square error (RMSE), and the kappa statistic are considered as evaluation criteria. The obtained results have indicated the superiority of the proposed method in comparison with state-of-the-art methods.

The quality of the protein structure can be determined by physical and chemical properties, therefore it has been used to distinguish native or native like structure from other predicted structures. In this study, the machine learning classification models are explored with six physical and chemical properties to classify the root mean square deviation (RMSD) of the protein structure in absence of its true native state and each protein structure lies between 0A˚ to 6A˚ RMSD space. Physical and chemical properties used in this paper are total surface area, Euclidean distance, total empirical energy, secondary structure penalty, residue length, and pair number. There are total 24294 decoys, having 4919 native structures. Artificial bee colony algorithm is used to determine the feature importance. The K-fold cross validation is used to measure the robustness of the best classification model. The results show that random forest method outperforms other machine learning models in the classification of protein structure prediction with sensitivity of 0.72 and accuracy of 70.33% on testing data set. The data set used in the study is available at http://bit.ly/RMSD-Classification-DS.

In order to reduce the node position error of DV-Hop algorithm in wireless sensor network, the artificial bee colony algorithm is introduced to design the DV-Hop algorithm. A new ABCDV-Hop (Artificial Bee Colony DV-Hop) algorithm is proposed in this paper. Based on the traditional DV-Hop algorithm, by using the minimum hops of nodes and position information of anchor nodes, the average distance per hop is solved by artificial bee colony algorithm to make it more close to the actual value. The simulation results show that compared with the traditional DV-Hop algorithm, the improved algorithm can effectively reduce the positioning error without increasing the node hardware overhead.

Crowd animation is a new and continuous challenge in computer animation. In tradition, crowd animation can be realized by key frame technology, and animators should set every character’s expression, action, motion, and behavior. Therefore animators’ workload by hand will increase tremendously with characters growing, which lead to difficult to realize crowd animation for low efficiency and poor global controllability, especially in path planning by appointing a target position for each individuals. In order to overcome these, an improved artificial bee colony (IM-ABC) algorithm is proposed to apply on the path planning of crowd animation. The IM-ABC is fit to simulate the crowd motion in animation based on the following two merits over the others in crowd animation. One is the rule of role transformation, which can make the rapid convergence of the result and avoid getting trapped in the local optima. The other is the realization of multi-object optimization in the process of iteration, which reaches the uniformly distributed result of swarm motion and especially fits to realize the path plan. In this paper, we simply reviews classical ABC algorithm proposed by Karaboga at the beginning. Then, in order to speed the convergence and make individuals generate paths more realistic and natural, some measures are taken to modify the classical ABC (called IM-ABC) algorithm, which include initializing colony based on chaos sequence, self-adaptively selecting the follower bees, and adaptively controlling parameters, etc. After the experiments of benchmark functions, the results confirm that the IM-ABC have better performance than the classical ABC algorithm and others. Finally, the IM-ABC algorithm is used for path planning to generate the route from the initial to the destination without collision. Through simulation experiments based on four motion models it is showed that this method can succeed generating the optimum paths with efficiency, intelligence, and natural features.

For order acceptance and scheduling with hybrid blocking in flow shop, the improved artificial bee colony (IABC) with NEH_BV heuristic algorithm, local search and artificial bee colony (ABC) is proposed for resolving the problem. In this algorithm, we first use NEH_BV heuristic algorithm to get some initial solutions, then updating the above initial solutions with improved artificial bee colony. In updating process, employed bees, onlooker bees and scout bees are inserted heuristic will increase exploitation and exploration of the algorithm. At last, we will get the near optimal solution by IABC algorithm. The effectiveness and efficiencies of the IABC algorithm has been proved by the case’s analysis and been compared with artificial bee colony and harmony search (HS).

K-means algorithm is sensitive to initial cluster centers and its solutions are apt to be trapped in local optimums. In order to solve these problems, we propose an optimized ar-tificial bee colony algorithm for clustering. The proposed method first obtains optimized sources by improving the selection of the initial clustering centers; then, uses a novel dy-namic local optimization strategy utilizing roulette wheel selection algorithm for further enhancing local optimization. To prove its effectiveness, we validate the proposed algo-rithm on four datasets from UCI and compared the results with K-means, K-means++ and Artificial Bee Colony algorithm. Experiment results show that the proposed algo-rithm performs better than other clustering algorithms.

To further improve the performance of artificial bee colony algorithm (ABC), an improved ABC (IABC) algorithm is proposed for global optimization via employing orthogonal initialization method. Furthermore, to balance the exploration and exploitation abilities, a new search mechanism is also designed. The performance of this algorithm is verified by using 27 benchmark functions. And the comparison analyses are given between the proposed algorithm and other nature-inspired algorithms. Numerical results demonstrate that the proposed algorithm outperforms the original ABC algorithm and other algorithms for global optimization problems.

In order to improve the convergence and diversity of multiobjective optimization algorithms, the harmonic average distance is employed to improve the aggregating function combined L-rank value. Selection model and searching scheme of artificial bee colony algorithm and diversity maintaining scheme are improved in this paper. This novel many objectives optimization method based on improved artificial bee colony algorithm (ABC) in this paper is compared with other three many objectives optimization methods on 3 to 8 objectives DTLZ. Simulation results show that the proposed algorithm is superior to other algorithms in the diversity and convergence of solutions.

Optimum active and reactive power dispatch is an inherent part of power system generation planning and it is the need of an hour for the electrical utilities and power engineers to dig this area in short and long term planning scenarios. Load demand requirements subjected to economic feasible solutions matching voltage profile, power demand, minimization of losses, voltage stability and improve the capacity of the system is the need of the hour. Optimization techniques based on evolutionary computing, artificial intelligence, search method finds their applications in the area of economic load dispatch planning to reach global optimal solution for this multi-decision, multi-objective combinatorial problem subjected to different constraints. In this paper, ant bee colony based algorithm has been proposed to solve economic dispatch problem. Unlike other heuristic algorithms, ABC utilizes search space as multi-step decision process. It possesses a flexible and well-balanced operator to enhance and adapt the global and fine tune local search to follow the minimum cost path in the search boundary. The suggested technique is tested on IEEE 25 bus system. Test results are compared with other techniques presented in literature.

In this paper, a new three-level thresholding method for image segmentation is proposed based on nonextensive entropy and fuzzy sets theory. Firstly, the image histogram is transformed from crisp set to fuzzy domain using fuzzy membership function, such as triangular membership function. After that, the nonextensive entropy of each part of fuzzy domain of histogram is computed. The threshold is selected by maximizing the nonextensive fuzzy entropy. However, the search of combination of membership function’s parameters is costly. For reduce the computation time, the artificial bee colony algorithm is used to search the optimal combination of the membership function’s parameters. The experimental results on tested images demonstrate the success of the proposed approach compared with the competing methods.

The traditional K-Means clustering is sensitive to random selection of initial cluster centroids, easily into the local optimal solution. In this paper, an efficient aggregation algorithm which combined with Artificial bee colony and K-Means algorithm is proposed to apply to the diagnosis of liver function. The algorithm reduced the dependence on the initial cluster centroids and the probability to be trapped by local optimal solution, thus assigning data points to their appropriate cluster more efficient. The experimental results show that algorithm proposed in this paper is superior to the K-Means clustering in diagnosis of liver function.

In order to reduce the node position error of DV-Hop algorithm in wireless sensor network, the artificial bee colony algorithm is introduced to design the DV-Hop algorithm. A new ABCDV-Hop (Artificial Bee Colony DV-Hop) algorithm is proposed in this paper. Based on the traditional DV-Hop algorithm, by using the minimum hops of nodes and position information of anchor nodes, the average distance per hop is solved by artificial bee colony algorithm to make it more close to the actual value. The simulation results show that compared with the traditional DV-Hop algorithm, the improved algorithm can effectively reduce the positioning error without increasing the node hardware overhead.

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.

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.

This article addresses an efficient heuristic-based approach to assign static shunt capacitors along radial distribution networks using the artificial bee colony algorithm. The objective function is adapted to enhance the overall system static voltage stability index and to achieve maximum net yearly savings. Load variations have been considered to optimally scope the fixed and switched capacitors required. The numerical results are compared with those obtained using recent heuristic methods and show that the proposed approach is capable of generating high-grade solutions and validated viability.

Manufacturers and retailers are interested in how prices, promotions, discounts and other marketing variables can influence the sales and shares of the products that they produce or sell. Therefore, many models have been developed to predict the brand share. Since the customer choice models are usually used to predict the market share, here we use hybrid model of Probabilistic Neural Network and Artificial Bee colony Algorithm (PNN-ABC) that we have introduced to model consumer choice to predict brand share. The evaluation process is carried out using the same data set that we have used for modeling individual consumer choices in a retail coffee market. Then, to show good performance of this model we compare it with Artificial Neural Network with one hidden layer, Artificial Neural Network with two hidden layer, Artificial Neural Network trained with genetic algorithms (ANN-GA), and Probabilistic Neural Network. The evaluated results show that the offered model is outperforms better than other previous models, so it can be use as an effective tool for modeling consumer choice and predicting market share.

Cascaded H-bridge multilevel (CHBML) inverters usually include a large number of isolated dc-voltage sources. Some faults in the dc-voltage sources result in unequal cell dc voltages. Unfortunately, the conventional phase-shifted carrier (PSC) PWM method that is widely used for CHBML inverters cannot eliminate low frequency sideband harmonics when the cell dc voltages are not equal. This paper analyzes the principle of sideband harmonic elimination, and proposes an improved PSCPWM that can eliminate low frequency sideband harmonics under the condition of unequal dc voltages. In order to calculate the carrier phases, it is necessary to solve transcendental equations for low frequency sideband harmonic elimination. Therefore, an approach based on the artificial bee colony (ABC) algorithm is presented in this paper. The proposed PSCPWM method enhances the reliability of CHBML inverters. The proposed PSCPWM is not limited to CHBML inverters. It can also be applied to other types of multilevel inverters. Simulation and experimental result obtained from a prototype CHBML inverter verify the theoretical analysis and the achievements made in this paper.

A hybrid search method based on the artificial bee colony algorithm (ABCA) with harmony search (HS) is suggested for finding a global solution in the field of optimization. Three cases of the suggested algorithm were examined for improving the accuracy and convergence rate. The results showed that the case in which the harmony search was implemented with the onlooker phase in ABCA was the best among the three cases. Although the total computation time of the best case is a little bit longer than the original ABCA under the prescribed conditions, the global solution improved and the convergence rate was slightly faster than those of the ABCA. It is concluded that the suggested algorithm improves the accuracy and convergence rate, and it is expected that it can effectively be applied to optimization problems with many design variables and local solutions.