Gbest-guided Artificial Bee Colony 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.

An extended artificial bee colony with local search for solving the Skyline-based web services composition under interval QoS properties

In this paper, we introduce an extended version of artificial bee colony with a local search method (EABC) for solving the QoS uncertainty-aware web service composition (IQSC) problem, where the ambiguity of the QoS properties are represented using the interval-number model. At first, we formulate the addressed problem as an interval constrained single-objective optimization model. Then, we use the skyline operator to prune the redundant and dominated web services from their sets of functionally equivalent ones. Whereas, EABC is employed to solve the IQSC problem in a reduced search space more effectively and more efficiently. For the purpose of validation of the performance and the efficiency of the proposed approach, we present the experimental comparisons to an existing skyline-based PSO, an efficient discrete gbest-guided artificial bee colony and a recently provided Harris Hawks optimization with an elite evolutionary strategy algorithms on an interval extended version of the public QWS dataset.

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.

An Artificial Bee Colony-Guided Approach for Electro-Encephalography Signal Decomposition-Based Big Data Optimization

The digital age has added a new term to the literature of information and computer sciences called as the big data in recent years. Because of the individual properties of the newly introduced term, the definitions of the data-intensive problems including optimization problems have been substantially changed and investigations about the solving capabilities of the existing techniques and then developing their specialized variants for big data optimizations have become important research topic. Artificial Bee Colony (ABC) algorithm inspired by the clever foraging characteristics of the real honey bees is one of the most successful swarm intelligence-based metaheuristics. In this study, a new ABC algorithm-based technique that is named source-linked ABC (slinkABC) was proposed by considering the properties of the optimization problems related with the big data. The slinkABC algorithm was tested on the big data optimization problems presented at the Congress on Evolutionary Computation (CEC) 2015 Big Data Optimization Competition. The results obtained from the experimental studies were compared with the different variants of the ABC algorithm including gbest-guided ABC (GABC), ABC/best/1, ABC/best/2, crossover ABC (CABC), converge-onlookers ABC (COABC), quick ABC (qABC) and modified gbest-guided ABC (MGABC) algorithms. In addition to these, the results of the proposed ABC algorithm were also compared with the results of the Differential Evolution (DE) algorithm, Genetic algorithm (GA), Firefly algorithm (FA), Phase-Based Optimization (PBO) algorithm and Particle Swarm Optimization (PSO) algorithm-based approaches. From the experimental studies, it was understood that the ABC algorithm modified by considering the unique properties of the big data optimization problems as in the slinkABC produces better solutions for most of the tested instances compared to the mentioned optimization techniques.

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.

A genetic Artificial Bee Colony algorithm for signal reconstruction based big data optimization

In recent years, the researchers have witnessed the changes or transformations driven by the existence of the big data on the definitions, complexities and future directions of the real world optimization problems. Analyzing the capabilities of the previously introduced techniques, determining possible drawbacks of them and developing new methods by taking into consideration of the unique properties related with the big data are nowadays in urgent demands. Artificial Bee Colony (ABC) algorithm inspired by the clever foraging behaviors of the real honey bees is one of the most successful swarm intelligence based optimization algorithms. In this study, a novel ABC algorithm based big data optimization technique was proposed. For exploring the solving abilities of the proposed technique, a set of experimental studies has been carried out by using different signal decomposition based big data optimization problems presented at the Congress on Evolutionary Computation (CEC) 2015 Big Data Optimization Competition. The results obtained from the experimental studies first were compared with the well-known variants of the standard ABC algorithm named gbest-guided ABC (GABC), ABC/best/1, ABC/best/2, crossover ABC (CABC), converge-onlookers ABC (COABC) and quick ABC (qABC). The results of the proposed ABC algorithm were also compared with the Differential Evolution (DE) algorithm, Genetic algorithm (GA), Firefly algorithm (FA), Fireworks algorithm (FW), Phase Base Optimization (PBO) algorithm, Particle Swarm Optimization (PSO) algorithm and Dragonfly algorithm (DA) based big data optimization techniques. From the experimental studies, it was understood that the newly introduced ABC algorithm based technique is capable of producing better or at least promising results compared to the mentioned big data optimization techniques for all of the benchmark instances.

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.

Techno-economic analysis-based optimal incorporation of distributed energy resources in distribution network under load uncertainty

ABSTRACT This paper addresses optimal incorporation of various distributed energy resources (DERs), ie wind, solar and electric vehicles (EVs), in distribution system for the purpose of minimisation of total real power loss, total cost and emissions produced from grid simultaneously. To achieve these proposed objectives and benefits, a multi-objective function has been formulated. In this study, various mathematical models of DERs have been used for analysing the impact on distribution system under uncertain load demand for 24 h. In addition, the charging/discharging concept of EVs during off-peak/peak hours of the grid has also been taken into consideration. This non-linear and non-convex optimisation task has been solved through Gbest-guided Artificial Bee Colony optimisation technique because of its robustness. However, from the obtained numerical outcomes through the proposed technique, it is observed that the integration of DERs is capable of reducing total real power loss, total cost and emissions to a great extent.

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.

Optimal integration of shunt capacitor banks in distribution networks for assessment of techno-economic asset

This article presents optimal incorporation of shunt capacitor banks in distribution feeders for the purpose of reduction of power loss, total annual expenses and voltage deviations. Additionally, it improves the voltage level, power factor and voltage stability of the network. To attain these proposed goals, a new multi-objective function has been formulated subjected to some operating constraints. This problem has been solved through Modified Gbest-guided Artificial Bee Colony (MGABC) meta-heuristic optimization technique. This proposed methodology is divided into two parts. In first part, the sensitivity buses are determined using Loss Sensitivity Factor (LSF) approach. In second, the optimization algorithm identifies the optimal buses from derived sensitive buses along with optimal size of shunt capacitors. This proposed methodology is implemented on standard 34-bus and 118-bus distribution feeders. In addition, the achieved numerical outcomes have been compared to the various intelligent approaches for showing the viability and effectiveness of the proposed approach.

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.

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.

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.

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

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.