Gbest-guided Artificial Bee Colony Algorithm Research Articles

Impact of demand response on battery energy storage degradation using gbest-guided artificial bee colony algorithm with forecasted solar insolation

The role of energy storage technology is gaining momentum as prosumers are actively participating in the retail electricity market. For the local energy community equipped with a grid-tied rooftop photovoltaic (PV) system, battery energy storage (BES) is a vital element to overcome the reliability issues occurring due to intermittency in renewable energy sources (RES). The PV-BES combination at the residential level is quite challenging as it involves optimal sizing and economical operation of the system. Also, rechargeable batteries are prone to aging and are majorly affected by temperature, state-of-charge (SOC), and charge/discharge rates (Crate), resulting in degradation and shortened useful life of BES. To this end, a single-objective optimization for minimizing the total degradation cost of BES is presented. In the first phase of this work, the day-ahead solar insolation is forecasted with an outlier filter-based autoencoder long short-term memory (LSTM-AE) for enhanced prediction accuracy. The mean absolute error (MAE), mean bias error (MBE), and coefficient of determination (R) of the LSTM-AE model are found to be 0.0423, −0.0117, and 0.984 respectively. The emergency demand response program (EDRP) is modeled in the second phase. An incentive of 20 ₵/kWh is found to balance the service provider (SP) revenue and prosumer benefit (PB) for prosumer participation in EDRP. Furthermore, the degradation cost of BES with an initial status of 20%, 55%, and 95% of SOC is optimized using a tuned gbest-guided artificial bee colony (GABC) algorithm both in the absence and presence of EDRP. The optimal solution obtained with EDRP shows daily savings, with three initial status of SOC, to be 18.78%, 12.14%, and 11.18% for summer, 16.15%, 13.77%, and 8.88% for mild, and 20%, 15.65%, and 12.16% for winter respectively, when compared without EDRP. Hence, this study demonstrates the merits of planning and prosumer participation in the retail electricity market and has a potential application in the real local energy community. The grid-connected PV-BES residential system is implemented in Python-Jupyter Notebook and MATLAB.

Fault-Tolerant Control Design for Multirotor UAVs Formation Flight

This paper proposes the use of the Gbest-Guided Artificial Bee Colony (GABC) algorithm to solve an online optimization problem for the leaderless distributed formation control of hexarotor Unmanned Aerial Vehicles (UAVs). The GABC is employed to optimize a cost function for each agent while ensuring the convergence of the fleet to the target position and averting both obstacles and collisions with other UAVs. The GABC algorithm has been shown to be competitive with some other conventional biological-inspired algorithms such as the Particle Swarm Optimization (PSO). Fault-Tolerant Control (FTC) methods are presented and tested on several scenarios, particularly we considered the cases of loss of agents and actuator faults in the fleet. Results show the success of the proposed FTC methods to minimize the faults effect on the formation final goal.

Impact of optimal integration of renewable energy sources and electric vehicles in practical distribution feeder with uncertain load demand

Distributed energy resources (DERs) are a better option for fulfillment of load demand near the load centers. Optimal location and rating of DERs plays a vital role in the enhancement of distribution system performance in terms of power loss reduction, voltage profile enhancement, environmental friendliness, increasing reliability, and postponement of system upgrading. This paper addresses the Gbest-guided artificial bee colony (GABC) optimization technique for the optimal integration of renewable energy sources (RESs) and electric vehicles (EVs) in distribution feeder to improve the power loss reduction, voltage level, cost reduction, and emissions reduction under load demand uncertainty. In this work, mathematical models of RESs, that is, wind turbine generation system (WTGS) and photovoltaic generation system (PVGS), are employed to determine the generated output power. The foremost aim of this research work is simultaneous abatement of total active power loss, entire cost, and emissions generation; therefore a multiobjective-based fitness function is formed. Moreover, the impact of charging/discharging behavior of EVs on the distribution grid has also been considered in this study. The proposed methodology is applied on two test systems: (a) 28-bus practical rural distribution network located at Kakdwip zone in the province of West Bengal, India, and (b) standard 69-bus distribution feeders. Finally, from the achieved numerical outcomes, it is analyzed that, GABC algorithm generates better outcomes as compared to other optimization techniques. This indicates the viability and effectiveness of the proposed methodology.

A novel multi-classifier based on a density-dependent quantized binary tree LSSVM and the logistic global whale optimization algorithm

The least squares support vector machine (LSSVM) is a useful binary classifier, but its performance is limited due to the lack of sparseness. The density-dependent quantized LSSVM (DSM) with quantized input data can increase the sparseness to effectively accomplish binary classification. However, the DSM cannot be directly used in multi-classification applications for most practical data-classification problems. We propose a novel multi-classifier based on a density-dependent quantized binary tree LSSVM (DBSM) and the logistic global whale optimization algorithm (LWA) to improve multi-classification accuracy and computational efficiency. The DBSM consists of multiple DSM classifiers, which hierarchically divide data according to a modified binary tree architecture. The tree architecture is constructed quickly and correctly with the quantized data instead of the original input data. An appropriate initial population of DBSM parameters is generated by using a logistic map and an improved opposition-based learning strategy. Then, the DBSM parameters are optimized by the whale optimization algorithm integrated with the gbest-guided artificial bee colony algorithm. According to the experimental results, the DBSM solves multi-classification problems faster than the one-versus-one based support vector machine (OVO-SVM) and the one-versus-all based LSSVM without sacrificing accuracy. The LWA precisely finds the optimal DBSM parameters without a heavy computational burden, in contrast to recent optimization algorithms. The proposed classifier achieves a 3.39% higher accuracy and consumes 52.83% less time than the genetic algorithm-based OVO-SVM. These results prove that the LWA-DBSM can complete multi-class classification tasks precisely and quickly.

A Novel Optimization Algorithm Combing Gbest-Guided Artificial Bee Colony Algorithm with Variable Gradients

The artificial bee colony (ABC) algorithm, which has been widely studied for years, is a stochastic algorithm for solving global optimization problems. Taking advantage of the information of a global best solution, the Gbest-guided artificial bee colony (GABC) algorithm goes further by modifying the solution search equation. However, the coefficient in its equation is based only on a numerical test and is not suitable for all problems. Therefore, we propose a novel algorithm named the Gbest-guided ABC algorithm with gradient information (GABCG) to make up for its weakness. Without coefficient factors, a new solution search equation based on variable gradients is established. Besides, the gradients are also applied to differentiate the priority of different variables and enhance the judgment of abandoned solutions. Extensive experiments are conducted on a set of benchmark functions with the GABCG algorithm. The results demonstrate that the GABCG algorithm is more effective than the traditional ABC algorithm and the GABC algorithm, especially in the latter stages of the evolution.

A novel machine learning technique for computer-aided diagnosis

The primary motivation of this paper is twofold: first, to employ a heuristic optimization algorithm to optimize the dendritic neuron model (DNM) and second, to design a tidy visual classifier for computer-aided diagnosis that can be easily implemented on a hardware system. Considering that the backpropagation (BP) algorithm is sensitive to the initial conditions and can easily fall into local minima, we propose an evolutionary dendritic neuron model (EDNM), which is optimized by the gbest-guided artificial bee colony (GABC) algorithm. The experiments are performed on the Liver Disorders Data Set, the Wisconsin Breast Cancer Data Set, the Haberman’s Survival Data Set, the Diabetic Retinopathy Debrecen Data Set and Hepatitis Data Set, and the effectiveness of our model was rigorously validated in terms of the classification accuracy, the sensitivity, the specificity, the F_measure, Cohen’s Kappa, the area under the receiver operating characteristic curve (AUC), convergence speed and the statistical analysis of the Wilcoxon signed-rank test. Moreover, after training, the EDNM can simplify its neural structure by removing redundant synapses and superfluous dendrites by the neuronal pruning mechanism. Finally, the simplified structural morphology of the EDNM can be replaced by a logic circuit (LC) without sacrificing accuracy. It is worth emphasizing that once implemented by an LC, the model has a significant advantage over other classifiers in terms of speed when handling big data. Consequently, our proposed model can serve as an efficient medical classifier with excellent performance.

A comparative study between artificial bee colony (ABC) algorithm and its variants on big data optimization

The big data term and its formal definition have changed the properties of some of the computational problems. One of the problems for which the fundamental properties change with the existence of the big data is the optimization problems. Artificial bee colony (ABC) algorithm inspired by the intelligent source search, consumption and communication characteristics of the real honey bees has proven its efficiency on solving different numerical and combinatorial optimization problems. In this study, the standard ABC algorithm and its well-known variants including the gbest-guided ABC algorithm, the differential evolution based ABC/best/1 and ABC/best/2 algorithms, crossover ABC algorithm, converge-onlookers ABC algorithm and quick ABC algorithm were assessed using the electroencephalographic signal decomposition based optimization problems introduced at the 2015 Congress on Evolutionary Computing Big Data Competition. The experimental studies on solving big data optimization problems showed that the phase-divided structure of the standard ABC algorithm still protects its advantageous sides when the candidate food sources or solutions are generated by referencing the global best solution in the onlooker bee phase.

Improved Gbest artificial bee colony algorithm for the constraints optimization problems

Living beings in nature are most intelligent creation of nature as they evolve with time and try to find optimum solution for each problem individually or collectively. Artificial bee colony algorithm is nature inspired algorithm that mimic the swarming behaviour of honey bee and successfully solved various optimization problems. Solution quality in artificial bee colony depends on the step size during position update. Randomly decided step size always has high possibility of miss out the exact solution. Its popular variant, namely Gbest-guided artificial bee colony algorithm tried to balance it and accomplished effectively for unconstrained optimization problems but, not satisfactory for the constrained optimization problems. Further, in the Gbest-guided artificial bee colony, individuals, which are going to update their positions, attract towards the current best solution in the swarm, which sometimes leads to premature convergence. To avoid such situation as well as to enhance the efficiency of Gbest-guided artificial bee colony to solve the unconstrained continuous optimization problems, an improved variant is proposed here. The improved Gbest-guided artificial bee colony proposed modifications in the position update during both the phase i.e. employed and onlooker bee phase to introduce diversification in search space additionally intensification of the identified region. The performance of new algorithm is evaluated for 21 benchmark optimization problems. Based on statistical analyses, it is shown that the new variant is a viable alternate of Gbest-guided artificial bee colony for the constraint optimization problems.

Optimization of gas injection allocation to increase oil production using Gbest-guided artificial bee colony algorithm

Oil comes from fossils thousands of years ago and it is natural resource that can’t be renewed. If it used continuously, the oil supplies will eventually run out. One way to optimize an oil production is by using gas lift. When the supply of the gas injection is limited, the total available gas should optimally distributed among the oil wells of the field such that the total production of oil is maximized. In this paper, Gbest-guided artificial bee colony (GABC) will became the optimization techniques while Least square is used to make the mathematical model from the correlation between gas injection and oil production. Contribution of this research is to find the fittest mathematical model and implement GABC to find the maximum value of mathematical model. The selected model is 10th order polynomial with R-square value of 1 and Sum of squares error (SSE) is 34.091. GABC will optimize the model and it will be compared to another optimization method such as Artificial bee colony (ABC). The results are GABC is better than ABC for each case with 4812.877 barrel oil per day (Bopd), 5107.873 Bopd, and 5142.156 Bopd respectively, while the best C values in GABC is 2.

Global Gbest Guided-Artificial Bee Colony Algorithm for Numerical Function Optimization

Numerous computational algorithms are used to obtain a high performance in solving mathematics, engineering and statistical complexities. Recently, an attractive bio-inspired method—namely the Artificial Bee Colony (ABC)—has shown outstanding performance with some typical computational algorithms in different complex problems. The modification, hybridization and improvement strategies made ABC more attractive to science and engineering researchers. The two well-known honeybees-based upgraded algorithms, Gbest Guided Artificial Bee Colony (GGABC) and Global Artificial Bee Colony Search (GABCS), use the foraging behavior of the global best and guided best honeybees for solving complex optimization tasks. Here, the hybrid of the above GGABC and GABC methods is called the 3G-ABC algorithm for strong discovery and exploitation processes. The proposed and typical methods were implemented on the basis of maximum fitness values instead of maximum cycle numbers, which has provided an extra strength to the proposed and existing methods. The experimental results were tested with sets of fifteen numerical benchmark functions. The obtained results from the proposed approach are compared with the several existing approaches such as ABC, GABC and GGABC, result and found to be very profitable. Finally, obtained results are verified with some statistical testing.

Gbest-Guided Artificial Bee Colony Optimization Algorithm-Based Optimal Incorporation of Shunt Capacitors in Distribution Networks under Load Growth

Abstract In this work, a new technique is introduced for optimal incorporation of shunt capacitors (SCs) in distribution networks. This technique has been compared to other sensitivity-based approaches such as loss sensitivity factor, index vector method, power loss index, and index of voltage stability. In the proposed technique, the optimal positions as well as the ratings of SCs are identified through an optimization algorithm. In sensitivity-based approaches, the positions of SCs are determined through a sensitivity approach and the optimal ratings of SCs are computed through an optimization algorithm. The main target of this study is to minimize the total annual cost and power loss of the network under load growth. This has been done through the population-based Gbest-guided artificial bee colony (GABC) optimization technique. Furthermore, the outcomes obtained through the GABC algorithm are compared to those from the iteration particle swarm optimization algorithm. The whole work along with the proposed methodology has been demonstrated on standard 34-bus and 118-bus distribution networks for SC placement. The results show that it reduces the total annual expense of the network to a great value. Consequently, it improves the total power loss reduction, enhances the voltage profile and power factor, and reduces the total voltage deviation. The obtained numerical outcomes through the proposed technique have been compared with the published literature outcomes to show the viability and superiority of the algorithm.

Gbest-guided artificial bee colony algorithm based simultaneous placement of distributed generation and shunt capacitor in distribution networks

Gbest-guided artificial bee colony algorithm based simultaneous placement of distributed generation and shunt capacitor in distribution networks

Gbest-guided artificial bee colony algorithm based simultaneous placement of distributed generation and shunt capacitor in distribution networks

This paper presents a combined placement of distributed generation (DG) and shunt capacitor in distribution network for the purpose of power loss reduction. The locations of DG and shunt capacitor are evaluated by using power loss index approach and index vector method, respectively. The population based Gbest-guided artificial bee colony meta-heuristic optimisation technique has been implemented to evaluate the optimum size of DG and shunt capacitor simultaneously. This methodology is being demonstrated on 33-bus and 85-bus distribution network. In addition to that, the two load scenarios are considered in this study. The technical analysis has been carried out for different combinations of DG and shunt capacitor. Comparison between various cases for power loss, minimum system voltage, voltage stability index and total voltage deviation are also mentioned in this paper. The numerical results obtained through proposed approach have been compared to the published research papers for showing its effectiveness.

Nonmetric Correction of Lens Distortion Based on Entropy Measure

In the real vision system, lens always inevitably contains nonlinear distortion, which leads to geometric distortion of digital image, so it must be corrected. In this paper, a nonmetric correction algorithm for lens distortion based on entropy measure is proposed. The algorithm uses the imaging characteristics of the space line in the ideal perspective model, and the distortion entropy is defined to measure the degree of lens distortion. For distortion curves with different distribution, the calculation dimension of distortion entropy measure is uniform, which can reduce the influence of curve inhomogeneity. On this basis, the modified distortion entropy measure with normalized weight is put forward to enhance the capability of noise suppression, and the distortion correction performance of the traditional interior point optimization algorithm, basic artificial bee colony (ABC) algorithm, and Gbest-guided artificial bee colony (GABC) algorithm is compared and analyzed. The simulation experiments demonstrate that the correction performance of GABC to optimize the modified distortion entropy measure with normalized weight is best, and it has strong robustness to noise. Finally, the actual image distortion correction examples verify the effectiveness of the proposed algorithm.

Hybrid Gbest-guided Artificial Bee Colony for hard partitional clustering

Clustering is an unsupervised classification method in the field of data mining and plays an essential role in applications in diverse fields. The K-means and K-Medoids are popular examples of conventional partitional clustering methods, which have been prominently applied in various applications. However, they possess several disadvantages, e.g., final solution is dependent on the initial solution, they easily struck into a local optimum solution. The nature-inspired swarm intelligence (SI) methods are global search optimization methods, which offer to be effective to overcome deficiencies of the conventional methods as they possess several desired key features like upgrading the candidate solutions iteratively, decentralization, parallel nature, and a self organizing behavior. The Artificial Bee Colony (ABC) algorithm is one of the recent and well-known SI method, which has been shown effective in various real-world problems. However, it exhibits lack of balance in the exploration and exploitation and shows a poor convergence speed when the number of features (dimensions) increases. Therefore, we make two modifications in it to enhance its exploration and exploitation capabilities to improve quality of the clustering. First, we introduce a gbest-guided search procedure for the fast convergence, which works effectively in large number of features also as it considers all the dimensions simultaneously. Second, in order to avoid being trapped in a local optima and to enhance the information exchange (social learning) between bees for improved search, we incorporate a crossover operator of the genetic algorithm (GA) into it. The proposed strategy is named as Hybrid Gbest-guided Artificial Bee Colony (HGABC) algorithm. We compare clustering results of the HGABC with ABC, variants of the ABC and other recent competitive methods in the swarm and evolutionary intelligence domain on ten real and two synthetic data sets using external quality measures F-measure and Rand-index. The obtained results demonstrate superiority of the proposed method over its competitors in terms of efficiency and effectiveness.

Modified Gbest-guided artificial bee colony algorithm with new probability model

Artificial bee colony (ABC) is a very effective and efficient swarm-based intelligence optimization algorithm, which simulates the collective foraging behavior of the honey bees. However, ABC has strong exploration ability but poor exploitation ability because its solution search equation performs well in exploration but badly in exploitation. In order to enhance the exploitation ability and obtain a better balance between exploitation and exploration, in this paper, a novel search strategy which exploits the valuable information of the current best solution and a novel probability model which makes full use of the other good solutions on onlooker bee phase are proposed. To be specific, in the novel search strategy, a parameter P is used to control which search equation to be used, the original search equation of ABC or the new proposed search equation. The new proposed search equation utilizes the useful information from the current best solution. In the novel probability model, the selected probability of the good solution is absolutely significantly larger than that of the bad solution, which makes sure the good solutions can attract more onlooker bees to search. We put forward a new ABC variant, named MPGABC by combining the novel search strategy and probability model with the basic framework of ABC. Through the comparison of MPGABC and some other state-of-the-art ABC variants on 22 benchmark functions, 22 CEC2011 real-world optimization problems and 28 CEC2013 real-parameter optimization problems, the experimental results show that MPGABC is better than or at least comparable to the competitors on most of benchmark functions and real-world problems.

Temperature dependent optimal power flow using g-best guided artificial bee colony algorithm

Resistance is an important element in power flow calculation. The accuracy of active power loss calculation depends upon resistance of that branch. Although resistance is a function of temperature rise for pure conductor, conventional power studies neglect temperature effect totally. To calculate exact losses in branch, it is necessary to consider temperature rise in calculation. In a deregulated electricity structure, optimal power flow has been an important tool to assess the economic operation of electricity market. So, the accuracy of optimal power flow is greatly influenced by temperature effect. This paper presents implementation of an efficient gbest-guided artificial bee colony algorithm to solve optimal power flow as well as temperature dependent optimal power flow. This method is tested on IEEE 30 bus system and IEEE 57 bus test system for statistical analysis of gbest-guided artificial bee colony algorithm. This paper also presents the temperature dependent optimal power flow on IEEE 30 bus test system. Effect of temperature rise is also analysed on IEEE 30 bus system, 2383 bus winter peak Polish system and 2736 bus summer peak Polish system.

Impact of wind uncertainty, plug-in-electric vehicles and demand response program on transmission network expansion planning

The aim of this paper is to minimize the total cost of the system by incorporating wind power and plug-in-electric vehicles (PEVs) along with demand response (DR) program. The methodologies have proposed in contrast with the conventional algorithm in which the transmission line investment cost has been minimized without considering the dynamism of the deregulated environment. Moreover, the transmission network planning enhances the competitiveness of the power market, where more market players can participate. In this situation, the network planner has an important role in assessing the needs for transmission investments. Now-a-days practice of the network planner is to utilize more renewable power resources, PEVs and implementation of different electricity price tariffs. To achieve more benefits of PEVs and wind energy, their optimal utilization is a major concern. This paper proposes a mathematical model for solving the combined effect of PEVs and wind power integration with incentive-based DR program on static transmission network expansion planning (STNEP) problem. To solve this non-linear and non-convex problem, a nature-inspired optimization algorithm named gbest-guided artificial bee colony algorithm (GABC) is applied due to its robustness. The algorithm’s performance is evaluated through modified IEEE 24-bus, Brazilian 46-bus and Colombian 93-bus system. The test results indicate that the combined effect of DR, PEVs and wind has reduced the total system cost significantly.

Fully informed artificial bee colony algorithm

The Gbest-guided artificial bee colony (GABC) algorithm is a latest swarm intelligence-based approach to solve optimisation problem. In GABC, the individuals update their respective positions by drawing inspiration from the global best solution available in the current swarm. The GABC is a popular variant of the artificial bee colony (ABC) algorithm and is proved to be an efficient algorithm in terms of convergence speed. But, in this strategy, each individual is simply influenced by the global best solution, which may lead to trap in local optima. Therefore, in this paper, a new search strategy, namely “Fully Informed Learning” is incorporated in the onlooker bee phase of the ABC algorithm. The developed algorithm is named as fully informed artificial bee colony (FABC) algorithm. To validate the performance of FABC, it is tested on 20 well-known benchmark optimisation problems of different complexities. The results are compared with those of GABC and some more recent variants of ABC. The results are very promising and show that the proposed algorithm is a competitive algorithm in the field of swarm intelligence-based algorithms.

Application of Gbest-guided artificial bee colony algorithm to passive UHF RFID tag design

In this paper, new planar spiral antennas with meander lines and loads for passive Radiofrequency identification tag application at ultra-high-frequency band are designed and optimized using the global best (gbest)-guided Artificial Bee Colony (GABC) algorithm. The GABC is an improved Artificial Bee Colony algorithm, which includes gbest solution information into the search equation to improve the exploitation. The optimization goals are antenna size minimization, gain maximization, and conjugate matching. The antenna dimensions were optimized and evaluated in conjunction with commercial software FEKO. GABC is compared with other popular algorithms. The optimization results produced show that GABC is a powerful optimization algorithm that can be efficiently applied to tag antenna design problems.