Artificial Bee Colony Technique Research Articles

Automated recognition of cardiac arrhythmias using sparse decomposition over composite dictionary

Background and objective: Cardiovascular diseases (CVDs) are the leading cause of deaths worldwide. Due to an increase in the rate of global mortalities, biopathological signal processing and evaluation are widely used in the ambulatory situations for healthcare applications. For decades, the processing of pathological electrocardiogram (ECG) signals for arrhythmia detection has been thoroughly studied for diagnosis of various cardiovascular diseases. Apart from these studies, efficient diagnosis of ECG signals remains a challenge in the clinical cardiovascular domain due to its non-stationary nature. The classical signal processing methods are widely employed to analyze the ECG signals, but they exhibit certain limitations and hence, are insufficient to achieve higher accuracy.Methods: This study presents a novel technique for an efficient representation of electrocardiogram (ECG) signals using sparse decomposition using composite dictionary (CD). The dictionary consists of the stockwell, sine and cosine analytical functions. The technique decomposes an input ECG signal into stationary and non-stationary components or atoms. For each of these atoms, five features i.e., permutation entropy, energy, RR-interval, standard deviation and kurtosis are extracted to determine the feature sets representing the heartbeats that are classified into different categories using the multi-class least-square twin support vector machines. The artificial bee colony (ABC) technique is used to determine the optimal classifier parameters. The proposed method is evaluated under category and personalized schemes and its validation is performed on MIT-BIH data.Results: The experimental results reported a higher overall accuracy of 99.21% and 90.08% in category and personalized schemes respectively than the existing techniques reported in the literature. Further a sensitivity, positive predictivity and F-score of 99.21% each in the category based scheme and 90.08% each in the personalized schemes respectively.Conclusions: The proposed methodology can be utilized in computerized decision support systems to monitor different classes of cardiac arrhythmias with higher accuracy for early detection and treatment of cardiovascular diseases.

Infrared and visible images fusion using visual saliency and optimized spiking cortical model in non-subsampled shearlet transform domain

Aiming at some problems in existing infrared and visible image fusion methods such as edge blurring, low contrast, loss of details, a novel fusion scheme based on non-subsampled shearlet transform (NSST), visual saliency and multi-objective artificial bee colony (MOABC) optimizing spiking cortical mode (SCM) is proposed. NSST has many advantages such as multi-scale features and sparse representation. Moreover, the visual saliency map can improve the low frequency fusion strategy, and SCM has coupling and pulse synchronization properties. Firstly, NSST is utilized to decompose the source image into a low-frequency subband and a series of high-frequency subbands. Secondly, the low-frequency subband is fused by SCM, where SCM is motivated by the edge saliency map of the low-frequency subband of the source image, and then the high-frequency subbands are also fused by SCM, where the modified spatial frequency of the high-frequency subbands of the source image is adopted as the input stimulus of SCM, the parameters of SCM are optimized by the novel multi-objective artificial bee colony technique. Finally, the fused image is reconstructed by inverse NSST. Experimental results indicate that the proposed scheme performs well and has obvious superiorities over other current typical ones in both subjective visual performance and objective criteria.

Design optimization of CPW-fed microstrip patch antenna using constrained ABFO algorithm

This paper explores the potential of bio-inspired soft computational technique known as adaptive bacterial foraging optimization (ABFO) for the joint optimization of geometrical parameters of compact coplanar waveguide (CPW)-fed microstrip patch antenna with defected ground structure. The presented research work is divided into three phases. In the initial phase, the intended antenna is designed and analyzed using finite element-based electromagnetic simulator Ansoft HFSS 15.0. In the subsequent phase, the analytical equations of various design parameters are modeled using curve fitting technique in MATLAB and root mean square error-based fitness functions are derived for individual design parameters. Then, a joint cost function is formulated from individual fitness functions for evaluation in optimization algorithm. Adaptive BFO is an improvement in classical BFO algorithm that dynamically adjusts the run-length unit parameter to maintain the balance between exploration–exploitation trade-off. In the final phase, a variation in the adaptive BFO algorithm termed as ‘constrained ABFO’ is projected and designed to suit the bounded constraints imposed by anticipated antenna structure. The modified algorithm is efficaciously used for joint optimization of specific design parameters to transform ‘dual-band performance’ into ‘broadband performance’ for high-speed point-to-point wireless services. The performance of design optimization using constrained ABFO is compared with the original BFO, particle swarm optimization (PSO), hybrid bacterial foraging–particle swarm optimization (BF-PSO), invasive weed optimization (IWO) and artificial bee colony (ABC) techniques to scrutinize its adequacy.

Environmental economic dispatch using improved artificial bee colony algorithm

Due to emissions from fossil fuel consumption in power plants, not only operational costs, but the minimization of the resulting pollution should be also considered in environmental economic dispatch problem. In this research, environmental economic dispatch problem is solved by minimization of operational cost and environmental pollution considering nonlinear constraints of generating units, forbidden regions, and ramp-rate of generating units using an improved artificial bee colony technique. With the proposed approach, data transactions among bees have been conducted using Newton’s and gravitational laws, leading to a full employment of honey bees mating optimization’s capability in finding the optimum solution. In order to show the effectiveness of the proposed algorithm, it is tested on IEEE 6-bus and IEEE 11-bus power systems in different load levels. Then, the obtained results are compared with those of other previously validated techniques. It is revealed that the proposed technique is superior in terms of accuracy and speed in solving power system complex problems over the other methods. In addition, it is unlikely for this approach to be trapped in local minima. Results compared to many recent competitive methods confirm the efficiency of the proposed method in term of solution quality and convergence characteristics.

Hybrid Multiobjective Optimization Algorithm for PM Motor Design

This paper proposes a hybrid, multiobjective optimization algorithm enabling global optimum tracking in permanent-magnet (PM) traction motor design. The methodology developed is based on the Artificial Bee Colony technique, strength Pareto evolutionary algorithm, and differential evolution strategy ensuring fast and reliable convergence to the optimal Pareto front. The effectiveness of the derived methodology is compared with other well-established and powerful algorithms from the literature through both appropriate test functions and an application example concerning an unequal teeth surface-mounted PM wheel motor design.

A Novel Multiobjective Optimization for Cement Stabilized Soft Soil based on Artificial Bee Colony

Cement is the most widely used additive in soft soil stabilization due to its high strength and availability. The cement content and curing time have a direct influence on the stabilization cost and hence it is prudent to minimize these variables to achieve optimality. Thus, it is a classical multi-objective optimization problem to find the optimum combination of cement content used and the curing time provided to achieve the target strength. This paper brings out the use of Vector Evaluated Artificial Bee Colony (VEABC) algorithm, a multi-objective variant of Artificial Bee Colony (ABC) technique, for the problem on hand. VEABC is a swarm intelligence algorithm, which employs multiple swarms to handle the multiple objectives and the information migration between these swarms ensures a global optimum solution is reached. Due to the stochastic nature of ABC algorithm, the resulting Pareto Curve will cover a good range of data with smooth transition. The Pareto fronts obtained for target strength could be used as calibration charts for scheduling the soft soil stabilization activities.

Wind Energy Power System Stabilizer Design using H∞ Robust Technique based on Enhance ABC Optimal Power System

system stabilizers (PSS) are now regularly used in the industry to damp out power system oscillations. High performance excitation system have become very important as limited generation capacity and consumer desires for power continue to increase. In this work, enhanced Artificial Bee Colony (ABC) technique is applied to design a robust power system stabilizer (PSS) in order to improve transient and dynamic stabilities of a turbo-alternator connected to an infinite bus system. The design problem of the proposed controller is formulated as an optimization problem and enhanced ABC is employed to search for optimal controller parameters. The robust power system stabilizer (RPSS) is designed using enhanced ABC for designing the controllers for dynamical systems in electrical engineering. Comparisons are also made between the Conventional power system stabilizer with a strong action (CPSS) and PSS with H∞ optimization. The simulation results show the effectiveness of proposed method for a stabilizer by enhancing the performance and robustness.

Lessons learned from using some bio-inspired optimizers for real-time controller design for a low-cost electrohydraulic system

Well-designed searching procedures following natural processes have been developed for finding optimized solutions of complex systems. Here, a comparison of performances of some optimizers, namely differential evolution, genetic algorithm, bacterial foraging and artificial bee colony technique, have been carried out for designing a fuzzy-feedforward real-time controller of an electrohydraulic motion actuation system. The first two optimizers execute dominatingly exploratory search, while the latter two execute a combination of exploratory search with intensified exploitive search in prospective regions, thus providing faster convergence. The optimized controller has been designed by minimizing a response error integral for some standard displacement demands of the highly nonlinear system. The strong nonlinearities in the system arise from the friction of low-cost industry-grade cylinder and large deadband of rugged proportional valve. The convergences to the minimum of zero for a number of nonlinear functions have also been demonstrated for all the optimization processes. These optimizers with faster convergence rate have been shown to be robust against arbitrary demands like variable frequency sinusoidal demands and sinusoidal demands with superimposed log concave-convex variations.

An effective technique for the content based image retrieval to reduce the semantic gap based on an optimal classifier technique

Content Based Image Retrieval (CBIR) systems use Relevance Feedback (RF) in order to improve the retrieval accuracy. Research focus has been shifted from designing sophisticated low-level feature extraction algorithms to reducing the "semantic gap" between the visual features and the richness of human semantics. In this paper, a novel system is proposed to enhance the gain of long-term relevance feedback. In the proposed system, the general CBIR involves two steps--ABC based training and image retrieval. First, the images other than the query image are pre-processed using median filter and gray scale transformation for removal of noise and resizing. Secondly, the features such as Color, Texture and shape of the image are extracted using Gabor Filter, Gray Level Co-occurrence Matrix and Hu-Moment shape feature techniques and also extract the static features like mean and standard deviation. The extracted features are clustered using k-means algorithm and each cluster are trained using ANN based ABC technique. A method using artificial bee colony (ABC) based artificial neural network (ANN) to update the weights assigned to features by accumulating the knowledge obtained from the user over iterations. Eventually, the comparative analysis performed using the commonly used methods namely precision and recall were clearly shown that the proposed system is suitable for the better CBIR and it can reduce the semantic gap than the conventional systems.

Using evolutionary multivariate adaptive regression splines approach to evaluate the eutrophication in the Pozón de la Dolores lake (Northern Spain)

Eutrophication is a water enrichment in nutrients (mainly phosphorus) that generally leads to symptomatic changes when the productions of algae and other aquatic vegetations are increased, and deterioration of water quality and all its uses in general. In this sense, eutrophication has caused a variety of impacts, such as high levels of Chlorophyll a (Chl-a). Consequently, anticipate its presence is a matter of importance to prevent future risks. The aim of this study was to obtain a predictive model able to perform an early detection of the eutrophication in water bodies such as lakes. This study presents a novel hybrid algorithm, based on multivariate adaptive regression splines (MARS) approach in combination with the artificial bee colony (ABC) technique, for predicting the eutrophication from biological and physical-chemical input parameters determined experimentally through sampling and subsequent analysis in a certificate laboratory. This optimization technique involves hyperparameter setting in the MARS training procedure, which significantly influences the regression accuracy. The results of the present study are two-fold. In the first place, the significance of each biological and physical-chemical variables on the eutrophication is presented through the model. Secondly, a model for forecasting eutrophication is obtained with success. Indeed, regression with optimal hyperparameters was performed and coefficients of determination equal to 0.85 for the Total phosphorus estimation and 0.84 for the Chlorophyll concentration were obtained when this hybrid ABC–MARS–based model was applied to the experimental dataset, respectively. The agreement between experimental data and the model confirmed the good performance of the latter. Finally, conclusions of this innovative research work are exposed.

A New Predictive Model Based on the ABC Optimized Multivariate Adaptive Regression Splines Approach for Predicting the Remaining Useful Life in Aircraft Engines

Remaining useful life (RUL) estimation is considered as one of the most central points in the prognostics and health management (PHM). The present paper describes a nonlinear hybrid ABC–MARS-based model for the prediction of the remaining useful life of aircraft engines. Indeed, it is well-known that an accurate RUL estimation allows failure prevention in a more controllable way so that the effective maintenance can be carried out in appropriate time to correct impending faults. The proposed hybrid model combines multivariate adaptive regression splines (MARS), which have been successfully adopted for regression problems, with the artificial bee colony (ABC) technique. This optimization technique involves parameter setting in the MARS training procedure, which significantly influences the regression accuracy. However, its use in reliability applications has not yet been widely explored. Bearing this in mind, remaining useful life values have been predicted here by using the hybrid ABC–MARS-based model from the remaining measured parameters (input variables) for aircraft engines with success. A correlation coefficient equal to 0.92 was obtained when this hybrid ABC–MARS-based model was applied to experimental data. The agreement of this model with experimental data confirmed its good performance. The main advantage of this predictive model is that it does not require information about the previous operation states of the aircraft engine.

Load Frequency Control of Multi - area Hybrid Power System by Artificial Intelligence Techniques

paper presents the application of different Artificial intelligence techniques on the tuning of PID controller in a load frequency control system. The algorithms of PSO technique, Genetic algorithm technique, and Artificial Bee Colony technique has been applied on four area power system with six tie lines. The system dynamic model is formulated in state variables form. A comparison between these techniques with different performance indices is presented. The effect of including different types of generating units (i.e. a hybrid power system) on the dynamical performance of a load frequency control is also presented. Three types of control criterion are adopted. Simulation of the applied artificial techniques on a typical hybrid power system has been carried out. It is observed that a hybrid power system can track the load fluctuation quickly.

ARM Amelioration Based On Artificial Bee Colony

Association rule mining which is the most significance and use is one of a relevant approach for data mining. The fundamental of the association rule mining approach have been Apriori and introduce many access with changes in the apriori but though main idea continue to be the same that is use of support and confidence threshold (s). Conforming to the theory it is well know that no work has been done in the domain of Enhancing pruning step of Apriori. This paper introduces a new algorithm M-APRIORI. This algorithm advances to Enhance the Apriori algorithm by using mean support (supmean) rather than minimum support (supmin), to produce probable item-set instead of large item-set and Artificial bee Colony technique used to optimization the rules. In this paper Apriroi and M-Apriori are based On Artificial Bee Colony.

A hybrid wavelet kernel SVM-based method using artificial bee colony algorithm for predicting the cyanotoxin content from experimental cyanobacteria concentrations in the Trasona reservoir (Northern Spain)

Cyanotoxins, a kind of poisonous substances produced by cyanobacteria, are responsible for health risks in surface waters used for drinking or for recreation. Additionally, cyanobacteria blooms are often called blue–green algae. A harmful algal bloom (HAB) is an algal bloom that causes negative impacts to other organisms via production of natural toxins such as cyanotoxins. Consequently, anticipating its presence is a matter of importance to prevent risks. This study presents a novel hybrid algorithm, support vector machines with Mexican hat wavelet kernel function (wavelet SVMs) in combination with the artificial bee colony (ABC) technique, for predicting the cyanotoxin content from cyanobacterial concentrations determined experimentally in the Trasona reservoir (recreational reservoir used as a high performance training center of canoeing in the Northern Spain). This optimization technique involves kernel parameter setting in the SVM training procedure, which significantly influences the regression accuracy. Bearing this in mind, cyanotoxin contents have been predicted here by using the hybrid wavelet ABC–SVM-based model from the remaining measured water quality parameters (input variables) in the Trasona reservoir (Northern Spain) with success. In other words, the results of the present study are two-fold. In the first place, the significance of each biological and physical–chemical variable on the cyanotoxin content in the reservoir is presented through the model. Secondly, a predictive model able to predict the possible presence of cyanotoxins is obtained. The agreement of the wavelet ABC–SVM-based model with experimental data confirmed its good performance. Indeed, a coefficient of determination equal to 0.91 was obtained. Finally, conclusions of this innovative research work are exposed.

Forecasting CO<SUB align="right">2 emission of Turkey: swarm intelligence approaches

It is known that among the various greenhouse gases, CO2 is the most frequently implicated in global warming and climate change. Therefore, there is a need for developing efficient quantitative tools that allow forecasting CO2 emissions. This paper presents application of particle swarm optimisation (PSO) and artificial bee colony (ABC) techniques to estimate CO2 emission in Turkey, based on socio-economic indicators. The models are developed in three forms which are linear, exponential and quadratic. PSOCO2 and ABCCO2 (PSO and ABC CO2 estimation models) are developed to estimate the future CO2 emission values based on energy consumption, population, gross domestic product (GDP), and number of motor vehicles data. Emitted CO2 emission in Turkey from 1980 to 2008 is considered as the case of this study. While the first 25 years data of 29 years data is used for validation of four models, full data is used for future projections. Sum square error (SSE) is used as a fitness function in proposed models. Finally, CO2 emission in Turkey is forecasted up to year 2030 under different scenarios.

Adaptive filtering using PSO, MPSO and ABC algorithms for ECG signal

In this paper, the design of Adaptive Noise Canceller (ANC) filter using evolutionary algorithms such as Particle Swarm Optimisation (PSO), Modified PSO (MPSO) and Artificial Bee Colony (ABC) algorithms is presented. The performance of proposed ANC filter is tested on a corrupted ECG signal. Based on simulation results, it is observed that the ANC filter constructed using these evolutionary algorithms achieves significant improvement in fidelity parameters such as SNR, MSE, ME and correlation factor when compared with other reported techniques in literature. ANC filter based on ABC with scaling factor provides 78% improvement in output SNR, 76% and 87% reduction in MSE and ME, respectively, as compared to ANC filter based on PSO. Further, ANC filter designed using ABC technique enhances the correlation between output and pure ECG signal.

Distorted Waveform Balancing Using an Artificial Bee Colony (ABC) Based Optimal Control for Mitigating Total Harmonics in Single Phase Inverter

The main objective of this paper is to reduce the total harmonics in a single phase voltage source inverter using Artificial Bee Colony (ABC) optimization technique for critical load applications. Single phase inverter is a non-linear load using power electronic components causing distortions in the load voltage and current wave patterns from the sinusoidal waveforms due to harmonics. The mapping state space model for a full bridge voltage source inverter was developed using output load resistance. An optimal ABC technique has been designed and optimized values are estimated using a full bridge voltage controlled inverter using Proportional Integral Algorithm. The MATLAB/SIMULINK tool and Experimental setup were implemented and their THD values were estimated. Also this ABC scheme is compared with the previous results such as PI Algorithm, Fuzzy logic controller and Neuro-fuzzy controllers. From the simulation and experimental results using ABC algorithm, it is observed that the total harmonics are mitigated considerably compared to previous results with respect to the power quality standards such as IEEE-519 and IEC 61000.

Improving Displacement Number and Overheads of DRFN using Artificial Bee Colony Technique in WSNs

Failure in Wireless Sensor Networks is common due to deployment of sensor nodes in harsh or hostile environment with limited power backup. Node failures could degrade the efficiency of sensor networks. Thus, failure detection and recovery techniques are very crucial for effective performance of nodes in Wireless Sensor Networks. In this paper we presented an improved DRFN technique by improving its failover mechanism. The failure handling scheme will be improved by using Artificial Bee Colony (ABC) based optimization technique. We analyze existing DRFN technique and compare it with the proposed technique on the basis of performance metrics such as occurrence of displacements and displacement overheads. Experimental results show that the proposed failure detection and recovery technique outperform over the available technique. The presented scheme is implemented and analyzed in 2013 version of MATLAB simulation tool.

Conservatory of Lifespan of Heterogeneous Wireless Sensor Network using ABC Algorithm

Wireless Sensor Network (WSNs) as the usage and development of wireless sensor network the problems related to wireless sensor network are apparent. The main issue considers here is the lifetime. When we consider about the homogeneous wireless sensor network several methodology available but in to maximize the lifetime of the wireless sensor network is progress in a slow space. The Artificial Bee Colony (ABC) algorithm which is swarm intelligence based technique is used to maximize the lifetime of the heterogeneous wireless sensor. This methodology is based on finding maximum number of disjoint connected covers that satisfy both sensing coverage and the network connectivity. The ABC technique involves three types of bees, employee bee. Onlookers and scout bee. The proposed method has been applied to variety of wireless sensor network. Based on the functions performed by these bees the energy consumption can be reduced and the lifetime of the heterogeneous WSNs is increased.

A new predictive model for the cyanotoxin content from experimental cyanobacteria concentrations in a reservoir based on the ABC optimized support vector machine approach: A case study in Northern Spain

Cyanotoxins, a kind of poisonous substances produced by cyanobacteria, are responsible for health risks in surface waters used for drinking or for recreation. Consequently, anticipation of its presence is a matter of importance to prevent risks. The aim of this study is to build a cyanotoxin diagnostic model by using support vector machines (SVMs) in combination with the artificial bee colony (ABC) technique from cyanobacterial concentrations determined experimentally in the Trasona reservoir (Northern Spain), to forecast the cyanotoxins' presence in the Trasona reservoir (Northern Spain). The ABC–SVM model is aimed at highly nonlinear biological problems with sharp peaks and the tests carried out have proven its high performance. The results of the present study are two-fold. In the first place, the significance of each biological and physical–chemical variables on the cyanotoxin content in the reservoir is presented through the model. Secondly, a predictive model of the cyanotoxin content is obtained. The agreement of the ABC–SVM-based model with experimental data confirmed its good performance. Finally, conclusions of this innovative research work are exposed.