ECG heart beat classification method based on modified ABC algorithm

In this paper, we proposed a classification method based on a nature-inspired algorithm, i.e., modified artificial bee colony (MABC). This method was applied to electrocardiogram (ECG) heartbeat classification. ECG data was obtained from MITBIH database. Eight different types of heartbeats (N, j, V, F, f, A, a, and R) were analyzed. For a better classification result, both time domain and frequency domain features were used. Feature selection was done by divergence analysis. MABC classification accuracy and heartbeat sensitivity values were compared with the results of other methods. Among other classifiers, k-nearest neighbor (KNN), Kohonen's self-organizing map (SOM), and ant colony optimization (ACO) were the best performing ones, and therefore their results are presented. The MABC classifier achieved 97.18 % accuracy on the analyzed dataset, as well as high sensitivity values for heartbeat types.

To realize fast and accurate evaluation for flatness errors,a Modified Artificial Bee Colony(MABC) algorithm was proposed to implement the minimum zone evaluation of flatness errors.The minimum zone method and the criteria for flatness errors were introduced.According to the minimum zone condition,the mathematic model of flatness error evaluation was presented.By introducing two traction bees and a Tabu Strategy(TS),this modified method could enhance the rate of convergence and the quality of optimum solution.The implementation steps of the method were expounded.Then,two test functions were selected in the simulation experiments through analysis,and the results verified the feasibility of MABC algorithm.Finally,proposed approach was used to evaluate flatness errors.The results calculated meet the criterion of minimal condition.On the basis of a group of metrical data,this approach can find the optimal plane by 0.436 second,which saves 0.411 second as compared with that of ABC algorithm.In addition,the flatness value from the MABC algorithm is 18.03 μm lower than that of the Least-Square Method(LSM),and 6.13 μm than the Genetic Algorithm(GA).According to other five measurement data sets available from the Coordinate Measuring Machines(CMMs),the results obtained by the MABC algorithm are more accurate than those by the GA and Particle Swarm Optimization(PSO),and the maximum gap of flatness values is 0.9 μm.Experimental results show that the MABC-based approach outperforms ABC-base method in optimization efficiency,solution quality and stability,and its calculating precision is superior to that given by LSM,GA or PSO.It is suited for the evaluation of position measuring instruments and CMMs.

The satellite image segmentation is a key area for current research and numerous work has been done for exploration of this area. The nature inspired optimization algorithms are very promising with image segmentation techniques to provide a platform for processing of satellite images. In this paper a literature review of different nature based optimization algorithms such as modified artificial bee colony (MABC) algorithm, ABC algorithm, particle swarm optimization (PSO), Darwinian PSO, genetic algorithm (GA), Wind driven optimization (WDO) and cuckoo search(CS) using different objective functions has been discussed to find the optimized multilevel thresholds. These nature influenced optimization methods and their performances are compared using different objective functions for optimal multilevel thresholding. The comparative study shows that the MABC algorithm and different variants of CS algorithm are very strong and accurate in results generating using image segmentation. Both methods search multilevel thresholds very efficiently and correctly, and in MABC an improved bee’s search solution are used and in CS the cuckoo search solution are used.

Cellular manufacturing (CM) is now an established international practice to integrate: equipment, people, and systems into 'focused factories', 'mini-businesses' or 'cells' with clear customers, responsibilities and boundaries. The major element in exploiting the benefits of CM is efficient layout designs. In this paper, problem of machine assignment is considered in a cellular layout. The focus is on the intra-cell machines allocation, because the inter-cell material flows should be minimal in CM. The modified artificial bee colony (MABC) algorithm is proposed to solve the layout problem. The algorithm tackles one cell at a time with a maximum of nine machines, which will be assigned into a 3 × 3 grid space according to the ranking result. This problem involves five parts and five quantitative demand periods are considered. To examine the performance of the proposed MABC, datasets are taken from the literature and the results are compared with those of other approaches. The proposed MABC provides an optimal layout exactly.

Based on the coupled vertical-torsional free vibration records of the spring-suspended section model of bridge decks, an improved Artificial Bee Colony (ABC) algorithm is proposed to identify the flutter derivatives. The ABC algorithm is a novel biological-inspired optimization algorithm, comparing with other iteration methods, the ABC algorithm can facilitate the identification process with no need for initial value. The standard ABC algorithm is good at exploration but poor at exploitation. To enhance the exploitation of the ABC algorithm, Powell’s method is used as a local search tool and a nonlinear factor is introduced for convergence control. Search equations applied to generate candidate solutions in the onlookers and scout bees phase are modified to improve the search ability of this method. The objective function in the optimization is weighted residual sum of squares between measured records and predicted values, where the weighting factors are optimized by an iteration process. The modified ABC algorithm is tested on several benchmark functions compared with the standard ABC algorithm. Simulation results show that the proposed algorithm has a higher convergence speed, better solution quality and stronger robustness. In order to investigate the effectiveness of the modified ABC algorithm in the flutter derivatives identification, numerical simulations of an ideal thin-plate model are carried out. The identification results show that the modified ABC algorithm for flutter derivatives identification of bridge decks is robust and reliable.

Artificial Bee Colony (ABC) algorithm is one of the efficient nature-inspired optimization algorithms for solving continuous problems. It has no sensitive control parameters and has been shown to be competitive with other well-known algorithms. However, the slow convergence, premature convergence, and being trapped within the local solutions may occur during the search. In this paper, we propose a new Modified Artificial Bee Colony (MABC) algorithm to overcome these problems. All phases of ABC are determined for improving the exploration and exploitation processes. We use a new search equation in employed bee phase, increase the probabilities for onlooker bees to find better positions, and replace some worst positions by the new ones in onlooker bee phase. Moreover, we use the Firefly algorithm strategy to generate a new position replacing an unupdated position in scout bee phase. Its performance is tested on selected benchmark functions. Experimental results show that MABC is more effective than ABC and some other modifications of ABC.

Artificial bee colony (ABC) algorithm, explored in recent literature, is an efficient optimization technique which simulates the foraging behavior of honeybees. ABC algorithm is good at exploration but poor at exploitation. This paper presents a new modified ABC algorithm for numerical optimization problems to improve the exploitation capability of the ABC algorithm. A different probability function and a new searching mechanism are proposed. The modified ABC algorithm is tested on seven numerical optimization problems. The results demonstrate that the modified ABC algorithm outperforms the ABC algorithm on solution quality and faster convergence.

A human planner's expertise is currently the most important consideration for determining optimal beam angles for external beam radiotherapy. The necessity of automatically selecting beam angles is especially important in intensity-modulated radiation therapy (IMRT) since fewer modulated beams are utilized in conformal radiotherapy. For an automated beam angle selection (ABAS) approach, the ideal coplanar beam angles correspond to the lowest objective function (OF) value of the dose distributions produced from this collection of candidate beams' intensity-modulated maps. Because of the task's intricacy and the large search space concerned, the ABAS and optimization of intensity maps are addressed independently and repeatedly. The Modified Artificial Bee Colony (MABC) optimization, the integration of Artificial Bee Colony (ABC), and a Firefly algorithm are employed to choose suitable beam angles, and the conjugate gradient (CG) technique is employed to fasten the optimized intensity maps for every selected beam. A 3D full scatter convolution (FSC) approach based on the pencil beam is employed for dose assessment. The effectiveness of MABC is examined using a more difficult instance representing a prostate tumor, and 2 simple cases The simulated MABC output is compared to the ABC optimization method. The results illustrate the reliability and efficiency of the suggested MABC-based ABAS can improve dosage distributions with clinically acceptable computation time.

To develop sustainable groundwater management strategies, generally coupled simulation-optimization (SO) models are used. In this study, a new SO model is developed by coupling moving least squares (MLS)-based meshless local Petrov-Galerkin (MLPG) method and modified artificial bee colony (MABC) algorithm. The MLPG simulation model utilizes the advantages of meshless methods over the grid-based techniques such as finite difference (FDM) and finite element method (FEM). For optimization, the basic artificial bee colony algorithm is modified to balance the exploration and exploitation capacity of the model more effectively. The performance of the developed MLPG-MABC model is investigated by applying it to hypothetical and field problems with three different management scenarios. The model results are compared with other available SO model solutions for its accuracy. Further, sensitivity analyses of various model parameters are carried out to check the robustness of the SO model. The proposed model gave quite promising results, showing the applicability of the present approach.

In this paper, the multi-UAVs collaboration routing plan problem is formally defined, and the route penalty degree is used to evaluate the multi-UAVs routes. A Modified Artificial Bee Colony (M-ABC), which includes collaboration strategy and slow communication style, is proposed to solve the multi-UAVs collaboration real-time routing plan problem. The simulation results show that M-ABC has great real-time and stability in solving multi-UAVs collaboration real-time route planning, which can quickly find collaboration routes of satisfying multi-UAVs’ requirements.

Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem

Artificial bee colony (ABC) algorithm has been proven to be an effective swarm intelligence-based algorithm to solve various numerical optimization problems. To improve the exploration and exploitation capabilities of ABC algorithm a new phase, namely disruption phase is introduced in the basic ABC. In disruption phase, disrupted operator in which the solutions are attracted or disrupted from the best solution based on the their respective distance from the best solution, is applied to all the solutions except the best solution. Further, the proposed strategy has been evaluated on 15 different benchmark functions and compared with basic ABC and two of its variants, namely modified ABC (MABC) and best so for ABC (BSFABC).

This paper enhances the hierarchical fuzzy model to deal with the classification problems by adopting evolutionary genetic algorithm (GA) with a modified artificial bee colony (ABC) algorithm. Traditionally, fuzzy classifier could not provide a sufficiently high classification rate in higher feature dimension with few rules. In the literature, the genetic algorithm can take advantage from the global searching; moreover, the characteristic of ABC can enhance the local searching. Therefore, the hierarchical fuzzy model integrates GA with a modified ABC algorithm is constructed in this study to recognize some classification problems. The classification simulation includes three benchmark databases such as Glass, Wine, and Iris database. The result demonstrates that using evolutionary GA and modified ABC algorithm is beneficial than that without turning. Therefore, it is clearly that our methodology considers not only the global exploration but also the local exploitation.

The rise of multi-cloud systems has been spurred. For safety-critical missions, it is important to guarantee their security and reliability. To address trust constraints in a heterogeneous multi-cloud environment, this work proposes a novel scheduling method called matching and multi-round allocation (MMA) to optimize the makespan and total cost for all submitted tasks subject to security and reliability constraints. The method is divided into two phases for task scheduling. The first phase is to find the best matching candidate resources for the tasks to meet their preferential demands including performance, security, and reliability in a multi-cloud environment; the second one iteratively performs multiple rounds of re-allocating to optimize tasks execution time and cost by minimizing the variance of the estimated completion time. The proposed algorithm, the modified cuckoo search (MCS), hybrid chaotic particle search (HCPS), modified artificial bee colony (MABC), max-min, and min-min algorithms are implemented in CloudSim to create simulations. The simulations and experimental results show that our proposed method achieves shorter makespan, lower cost, higher resource utilization, and better trade-off between time and economic cost. It is more stable and efficient.

In recent decades, the classification of fake fingerprints and real fingerprint images has become an attractive research topic because of the most advanced threats. A number of research works have been carried out to classify fake and real fingerprints. But, most of the existing techniques did not utilize swarm intelligence techniques in their fingerprint classification system. Swarm intelligence has been widely used in various applications due to its robustness and potential in solving a complex optimization problem. The proposed classification methodology comprises of four steps, namely image preprocessing, feature extraction, feature selection and classification. This work uses efficient min-max normalization and median filtering for preprocessing, and multiple static features are extracted from Gabor filtering. To perform multiple static feature selection, Artificial Bee Colony (ABC) and Modified Artificial Bee Colony (MABC) optimization algorithm is used in this work to choose the best optimal static features on the basis of specific fitness values. This approach uses Fuzzy Feed Forward Neural Network (FFFNN) for classification and classification of feature vector into fake and real fingerprint images by the semi supervised graph based classification approach which enables efficient classification of real and fake fingerprints and its performance is compared with the SVM classifier. Evaluation of the performance has been carried out by taking the fingerprint images that had been collected from the FVC2000 and synthetically generated database by employing the SFinGE. The results showed that the proposed work provided improvised and enhanced results with respective to characteristics like sensitivity, precision, specification as well as classification accuracy.