Basic Artificial Bee Colony Research Articles

Prediction of compressive strength of concrete based on improved artificial bee colony-multilayer perceptron algorithm

There are many factors that affect the compressive strength of concrete. The relationship between compressive strength and these factors is a complex nonlinear problem. Empirical formulas commonly used to predict the compressive strength of concrete are based on summarizing experimental data of several different mix proportions and curing periods, and their generality is poor. This article proposes an improved artificial bee colony algorithm (IABC) and a multilayer perceptron (MLP) coupled model for predicting the compressive strength of concrete. To address the shortcomings of the basic artificial bee colony algorithm, such as easily falling into local optima and slow convergence speed, this article introduces a Gaussian mutation operator into the basic artificial bee colony algorithm to optimize the initial honey source position and designs an MLP neural network model based on the improved artificial bee colony algorithm (IABC-MLP). Compared with traditional strength prediction models, the ABC-MLP model can better capture the nonlinear relationship of the compressive strength of concrete and achieve higher prediction accuracy when considering the compound effect of multiple factors. The IABC-MLP model built in this study is compared with the ABC-MLP and particle swarm optimization (PSO) coupling algorithms. The research shows that IABC can significantly improve the training and prediction accuracy of MLP. Compared with the ABC-MLP and PSO-MLP coupling models, the training accuracy of the IABC-MLP model is increased by 1.6% and 4.5%, respectively. This model is also compared with common individual learning algorithms such as MLP, decision tree (DT), support vector machine regression (SVR), and random forest algorithms (RF). Based on the comparison of prediction results, the proposed method shows excellent performance in all indicators and demonstrates the superiority of heuristic algorithms in predicting the compressive strength of concrete.

An artificial bee colony algorithm with a cumulative covariance matrix mechanism and its application in parameter optimization for hearing loss detection models

Artificial bee colony (ABC) algorithms which are applied to various complex problems for their competitive performance and simple structures are a relatively popular paradigm of evolutionary algorithms (EAs). Although many variants based on the basic ABC algorithm have been developed, most variants have limited performance on non-separable problems. This is mainly because these variants usually focus on only individual quality information, but neglect population distribution information. To address this limitation, in this paper, we propose a new ABC with a cumulative covariance matrix mechanism (CCoM-ABC), in which the population distribution information in all previous generations is involved to guide the evolution process. Specifically, the CCoM-ABC algorithm applies a cumulative covariance matrix (CCoM) mechanism by which the population information of every generation is involved to improve the probability of finding the optimum. It also applies an adaptive selection mechanism by which the proper coordinate system used to generate candidate solutions can be dynamically selected between a natural one and an eigen one during the search process. Furthermore, for improving the search ability, a new search equation is adopted in the scout bees phase. A series of experiments are conducted on the CEC2014 to evaluate the performance of the proposed CCoM-ABC algorithm, and the results show that the CCoM-ABC outperforms state-of-the-art ABC variants and EAs in terms of convergence speed, solution accuracy, and numerical scalability. For validating the practicality of the proposed CCoM-ABC algorithm, we further apply it to optimize the parameters of the neural network for hearing loss (HL) detection. This study involves 60 brain images consisting of 20 left-sided HL, 20 right-side HL, and 20 healthy control images. The results show that this model outperforms state-of-the-art related methods in terms of overall accuracy by about 2%.

Optimization of PID trajectory tracking controller for a 3-DOF robotic manipulator using enhanced Artificial Bee Colony algorithm

This study introduces and compares two optimization techniques, the basic Artificial Bee Colony (ABC) and the enhanced Artificial Bee Colony with multi-elite guidance (MGABC), for determining optimal gains of a Proportional-Integral-Derivative (PID) controller in a 3 degrees of freedom (DOF) rigid link manipulator (RLM) system. The objective function used in the optimization process is a novel function that is based on the well-known Lyapunov stability functions. This function is evaluated against established error-based objective functions commonly used in control systems. The convergence curves of the optimization process demonstrate that the MGABC algorithm outperforms the basic ABC algorithm by effectively exploring the search space and avoiding local optima. The evaluation of the controller's performance in trajectory tracking reveals the superiority of the Lyapunov-based objective function (LBF), with significant improvements over other objective functions such as IAE, ISE, ITAE, MAE and MRSE. The optimized system demonstrates robustness to diverse disturbance conditions and uncertainty in the mass of the payload, while also exhibiting adaptability to joints flexibility without inducing any vibrations in the movement of the end-effector. The proposed techniques and objective function offer promising avenues for the optimization of PID controllers in various robotic applications.

On the Application of Quick Artificial Bee Colony Algorithm (qABC) for Attenuation of Test Suite in Real-Time Software Applications

Software testing plays a vital role during the software development process, as it ensures quality software deployment. Success of software testing depends on the design of effective test cases. To achieve the optimization of generated test cases, the proposed approach combines both global and local searches by means of intelligent agents which exhibit the behaviour of employed bees, onlooker bees, and scout bees in the qABC algorithm. The proposed qABC algorithm has key improvements over the basic artificial bee colony algorithm (ABC) in test optimization by reducing redundancy, filtering of test cases in each iteration and parallel working of the bees. Further, the fitness evaluation of the test cases is done by employing two test adequacy metrics namely path coverage and mutation score. Further, the experimental evaluation of qABC, GA, and the basic ABC based test cases is done using several case study applications. The result shows that qABC outperforms the other algorithms in terms of effectiveness of test cases in revealing the faults with less time and a smaller number of test cases.

A Hybrid Discrete Artificial Bee Colony Algorithm for Imaging Satellite Mission Planning

Imaging satellite mission planning has received more and more attention as one of the core problems in the field of imaging satellite applications. In this paper, a hybrid discrete artificial bee colony (HDABC) algorithm is proposed to address this problem. The HDABC algorithm improves the three search phases of the basic artificial bee colony (ABC) algorithm to make them applicable to the discrete satellite mission planning problem. In the employed bee search phase, the population is divided and a multi-strategy search equation mechanism is used to balance the exploration and development of the algorithm. In the following bee search phase, two kinds of neighborhood search operators are designed based on the problem characteristics to further improve the fitness values of the better solutions. In the scout bee search phase, a migration operator and an immigration operator are introduced to improve the fitness values of the worse solutions and promote the exchange of different subpopulations to achieve co-evolution. In the experimental part, orthogonal experimental design is used to determine the appropriate algorithm parameters. Simulation experiments are carried out to test problems of different sizes. The experimental results show that the proposed HDABC algorithm shows good performance.

Hybrid Artificial Bee Colony and Improved Simulated Annealing for the Capacitated Vehicle Routing Problem

Capacitated Vehicle Routing Problem (CVRP) is a type of NP-Hard combinatorial problem that requires a high computational process. In the case of CVRP, there is an additional constraint in the form of a capacity limit owned by the vehicle, so the complexity of the problem from CVRP is to find the optimum route pattern for minimizing travel costs which are also adjusted to customer demand and vehicle capacity for distribution. One method of solving CVRP can be done by implementing a meta-heuristic algorithm. In this research, two meta-heuristic algorithms have been hybridized: Artificial Bee Colony (ABC) with Improved Simulated Annealing (SA). The motivation behind this idea is to complete the excess and the lack of two algorithms when exploring and exploiting the optimal solution. Hybridization is done by running the ABC algorithm, and then the output solution at this stage will be used as an initial solution for the Improved SA method. Parameter testing for both methods has been carried out to produce an optimal solution. In this study, the test was carried out using the CVRP benchmark dataset generated by Augerat (Dataset 1) and the recent CVRP dataset from Uchoa (Dataset 2). The result shows that hybridizing the ABC algorithm and Improved SA could provide a better solution than the basic ABC without hybridization.

Hybrid Sine Cosine Algorithm for Solving Engineering Optimization Problems

Engineering design optimization problems are difficult to solve because the objective function is often complex, with a mix of continuous and discrete design variables and various design constraints. Our research presents a novel hybrid algorithm that integrates the benefits of the sine cosine algorithm (SCA) and artificial bee colony (ABC) to address engineering design optimization problems. The SCA is a recently developed metaheuristic algorithm with many advantages, such as good search ability and reasonable execution time, but it may suffer from premature convergence. The enhanced SCA search equation is proposed to avoid this drawback and reach a preferable balance between exploitation and exploration abilities. In the proposed hybrid method, named HSCA, the SCA with improved search strategy and the ABC algorithm with two distinct search equations are run alternately during working on the same population. The ABC with multiple search equations can provide proper diversity in the population so that both algorithms complement each other to create beneficial cooperation from their merger. Certain feasibility rules are incorporated in the HSCA to steer the search towards feasible areas of the search space. The HSCA is applied to fifteen demanding engineering design problems to investigate its performance. The presented experimental results indicate that the developed method performs better than the basic SCA and ABC. The HSCA accomplishes pretty competitive results compared to other recent state-of-the-art methods.

An improved feature selection approach using global best guided Gaussian artificial bee colony for EMG classification

Electromyography (EMG) measures muscle relaxes or contractions during muscular activity through EMG signal. It plays a vital role in identifying muscle-related problems for clinical diagnosis. This paper presents an efficient EMG feature selection technique for classifying 17 different prosthetic hand movements recorded from 11 subjects. Two variants of the Artificial Bee Colony (ABC) algorithm, namely: i) Global Best Guided ABC (GbestABC) and ii) Gaussian ABC (GABC), are employed to propose an Improved Artificial Bee Colony (ABC) algorithm called Global Best Guided Gaussian ABC (GGABC) for solving global optimization problems. GbestABC performs better in the exploitation phase, whereas GABC performs better in the exploration phase in searching for the optimal solutions. The proposed GGABC takes advantage of GbestABC and GABC to counterbalance basic ABC's exploitation and exploration capability. Further, a binary version of GGABC known as binary GGABC (BGGABC) is developed to solve binary optimization problems and select optimal EMG signal classification features. Extensive experiments are carried out in three phases: i) GGABC for global optimization problems ii) BGGABC for EMG feature selection problems with other meta-heuristic-based competitors iii) BGGABC for EMG feature selection problems with well-known filter based techniques. K-nearest neighbor (KNN) classifier is used in experiments to validate and investigate the effectiveness of the proposed algorithm. Experimental result shows that the BGGABC-based EMG feature selection achieved 94.13% average classification accuracy and 97.06% best classification accuracy. Obtained results confirm that the proposed algorithm outperforms or is competitive with state-of-the-art algorithms in EMG feature selection and classification.

A discrete artificial bee colony algorithm for quadratic assignment problem

The quadratic assignment problem (QAP) is a well-known challenging combinational optimization problem that has received many researchers’ attention with varied real-world and industrial applications areas. Using the framework of basic artificial bee colony algorithm, frequently used crossover and mutation operators, and combined with an effective local search method, this paper proposes a simple but effective discrete artificial bee colony (DABC) algorithm for solving quadratic assignment problems (QAPs). Typical QAP benchmark instances are selected from QAPLIB in order to conduct the simulation experiment where common performance metrics are used to evaluate the algorithm. The paper also investigates the influence factors of the algorithm’s performance. The results show that the proposed algorithm is a quite effective and practical new approach for handling QAP problems.

A self-adaptive level-based learning artificial bee colony algorithm for feature selection on high-dimensional classification

Feature selection is an important data preprocessing method in data mining and machine learning, yet it faces the challenge of “curse of dimensionality” when dealing with high-dimensional data. In this paper, a self-adaptive level-based learning artificial bee colony (SLLABC) algorithm is proposed for high-dimensional feature selection problem. The SLLABC algorithm includes three new mechanisms: (1) A novel level-based learning mechanism is introduced to accelerate the convergence of the basic artificial bee colony algorithm, which divides the population into several levels and the individuals on each level learn from the individuals on higher levels, especially, the individuals on the highest level learn from each other. (2) A self-adaptive method is proposed to keep the balance between exploration and exploitation abilities, which takes the diversity of population into account to determine the number of levels. The lower the diversity is, the fewer the levels are divided. (3) A new update mechanism is proposed to reduce the number of selected features. In this mechanism, if the error rate of an offspring is higher than or is equal to that of its parent but selects more features, then the offspring is discarded and the parent is retained, otherwise, the offspring replaces its parent. Further, we discuss and analyze the contribution of these novelties to the diversity of population and the performance of classification. Finally, the results, compared with 8 state-of-the-art algorithms on 12 high-dimensional datasets, confirm the competitive performance of the proposed SLLABC on both classification accuracy and the size of the feature subset.

Improved Artificial Bee Colony Algorithm with Adaptive Parameter for Numerical Optimization

ABSTRACT The problem that ABC (Artificial Bee Colony) algorithm is good at exploration but poor at exploitation for the numerical optimization is investigated in this paper. PA-ABC (Parameter Adaptive ABC) algorithm is proposed, which adopts different search equations with different search abilities for the employed bee and the onlooker bee. Firstly, the best-so-far solution is introduced into each search equation to enhance exploitation; secondly, the employed bee uses two random solutions to search, so as to keep high ability of exploration; thirdly, the onlooker bee searches around a random solution to keep population diversity; most importantly, adaptive parameter computed by fitness function is introduced in the search equation of the onlooker bee, which makes the search step adjust according to the search process. So the search equation of the employed bee has balanced abilities of exploration and exploitation, while the search equation of the onlooker bee can make the search focus transfer from exploration to exploitation adaptively. The experiment results on benchmark functions show that the search performance of PA-ABC is higher than or at least comparable to basic ABC and typical improved ABCs. In addition, compared to the performance of the state-of-the-art ABC variants under their original parameter configuration, PA-ABC is verified to have similar performance to them.

Artificial Bee Colony Algorithm with Proposed Discrete Nearest Neighborhood Algorithm for Discrete Optimization Problems

Travelling salesman problem (TSP) is one the problems of NP-complete family, which means finding shortest complete close tour in the graph. This study seeks to solve this problem using Artificial Bee Colony (ABC) Algorithm along with the proposed Discrete Nearest Neighborhood Algorithm (DNNA). DNNA finds shortest path among points by starting from an arbitrary point. In next steps this links will be a guide to make complete tour. In other words the links in partial tours have higher chance to be in the final solution. In order to improve the final solutions of a single created tour, The employee bees’ movement radius has been limited, because of avoidance of long random jump between nodes. To reduce the optimization time of the tours created by the artificial bee colony algorithm, the fixed-radius near neighbor 2-opt algorithm was used as well. In addition, 2 types of scout bee were used for to intensify the probability property of the algorithm. Also, convergence in the probability function of employee bees’ movement was prevented by increasing the number of route-creating tours. The first scout bee applies the proposed DNNA and the secondary scout bee improves the partial tours of employee bees in a probable way. Although Althought the average error of proposed ABC algorithm has been 0.371% higher than best solution of all methods, it could improve the solution of 3 problems with average of 3.305%. The proposed algorithm has been better than basic ABC in all tested problems with average of 0.570%.

Dynamic Artificial Bee Colony Algorithm with Hybrid Initialization Method

An improved basic artificial bee colony (ABC) algorithm with a self-adaptive technique, called dynamic ABC, is proposed. The dynamic ABC algorithm first uses a hybrid method combining the good-point-set method with the chaotic maps method to generate the initial population. Then, it applies self-adaptive population size at each generation, meaning that the population increases or decreases depending on some criteria, to enhance global convergence and avoiding local solutions. Experiments are carried out on a range of 10 popular benchmark functions. The results indicate that the dynamic ABC algorithm is superior to the basic ABC algorithm when considering both the speed and quality of the solution obtained.

A novel hybrid approach based artificial bee colony and salp swarm algorithms for solving ORPD problem

Optimal reactive power dispatch (ORPD) is an important task for achieving more economical, secure and stable state of the electrical power system. It is expressed as a complex optimization problem where many meta-heuristic techniques have been proposed to overcome various complexities in solving ORPD problem. A meta-heuristic search mechanism is characterized by exploration and exploitation of the search space. The balance between these two characteristics is a challenging problem to attain the best solution quality. The artificial bee colony (ABC) algorithm as a reputed meta-heuristic has proved its goodness at exploration and weakness at exploitation where the enhancement of the basic ABC version becomes necessary. Salp swarm algorithm (SSA) is a newly developed swarm-based meta-heuristic, which has the best local search capability by using the best global solution in each iteration to discover promising solutions. In this paper, a novel hybrid approach-based ABC and SSA algorithms (ABC-SSA) is that developed to enhance the exploitation capability of the ABC algorithm using SSA and applied for solving ORPD problem. The efficiency of ABC-SSA is investigated using two standard test systems IEEE-30 and IEEE-300 buses, and that by considering the famous objective functions in ORPD problem.

Computer-Aided Diagnosis System for Diagnosis of Cavitary and Miliary Tuberculosis Using Improved Artificial Bee Colony Optimization

A framework for Computer-Aided Diagnosis (CAD) to diagnose Cavitary TB and Miliary TB from chest Computed Tomography (CT) slices has been designed and implemented. The lung tissues from the CT slices are segmented using region-based Active Contour Model (ACM) and the Region of Interests (ROIs) labelled by an expert radiologist are extracted. Features based on shape and texture are extracted from each ROI. A wrapper-based Improved Artificial Bee Colony Optimization (I-ABCO) algorithm with the accuracy of the Support Vector Machine (SVM) classifier as the fitness function is used to select the optimal subset of features. The search process of I-ABCO is improved using two evaluation functions, namely, rough dependency measure (RDM) and mutual information (MI), to promote better exploitation of the search space. The selected features are used to train the Radial Basis Function Neural Network (RBFNN) classifier, using ten-fold cross validation and the performance is evaluated. Experimentation has been performed on CT slices using two datasets: Tuberculosis dataset and Lung Image Database Consortium-Image Database Resource Initiative (LIDC-IDRI) dataset. The accuracy of the CAD systems using I-ABCO with MI and I-ABCO with RDM for both datasets are (88.34%, 92.63%) and (87.32%, 90.17%), respectively. The CAD system using the basic Artificial Bee Colony Optimization (ABCO) algorithm for feature selection is also experimented with the same datasets and an accuracy of (85.68%, 88%) is achieved. The performance of the proposed algorithms outperforms the ABCO algorithm in diagnostic accuracy, which ensures that selecting the optimal feature subset efficiently has an impact on the classification accuracy.

Retinal damar segmantasyonuna yönelik yapay arı koloni algoritması tabanlı yaklaşımların performans mukayesesi

Structural changes in the retinal blood vessels provide important information about retinal diseases. Therefore, computer-aided segmentation of retinal blood vessels has become an active area of research in last decades. Due to the close contrast between the retinal blood vessels and the retinal background, robust methods should be developed to detect retinal blood vessels with high accuracy. In this work, artificial bee colony (ABC) algorithm which provides effective solutions to engineering problems has been applied to the retinal vessel segmentation. Clustering based ABC (basic ABC), quick-ABC (Q-ABC) and modified ABC (MR-ABC) algorithms have been analyzed for accurate segmentation of retinal blood vessels and their performances were compared. The simulations have been realized on the normal and abnormal retinal images taken from the DRIVE database. Simulation results and statistical analyses represent that ABC based approaches are stable and able to reach to optimal clustering performance with higher convergence rates. As a result it can be concluded that ABC based approaches can successfully be used for accurate segmentation of retinal blood vessels.

Effects of memory and genetic operators on Artificial Bee Colony algorithm for Single Container Loading problem

The Artificial Bee Colony (ABC) algorithm is widely used to achieve optimum solution in a short time in integer-based optimization problems. However, the complexity of integer-based problems such as Knapsack Problems (KP) requires robust algorithms to avoid excessive solution search time. ABC algorithm that provides both the exploitation and the exploration approach is used as an alternative approach for various KP problems in the literature. However, it is rarely used for the Single Container Loading problem (SCLP) which is an important part of the transportation systems. In this study, the exploitation and exploration aspects of the ABC algorithm are improved by using memory mechanisms and genetic operators to develop three different hybrid ABC algorithms. The developed algorithms and the basic ABC algorithm are applied to a SCLP dataset from the literature to observe the effects of the memory mechanism and the genetic operators separately. Besides, a joint hybrid ABC algorithm using both reinforcement approaches is proposed to solve the SCLP. The results show that the joint hybrid ABC algorithm has emerged as a promising approach to solving SCLP with an average performance, and the genetic operators are more effective than the memory mechanism to develop a hybrid ABC algorithm.

Improved artificial bee colony algorithm based on damping motion and artificial fish swarm algorithm

The basic artificial bee colony (ABC) algorithm is easy to fall into local optimum, and it has poor exploitation ability and slow convergence speed. According to the defects, the improved algorithm is proposed, which is based on damping motion and artificial fish swarm(AFS) algorithm. In the employed bees phase, the swarm behavior of AFS algorithm which can avoid sinking into local optimum is introduced. At the same time, considering the slow convergence speed, employed bees adopt the multidimensional updating process in the early stage of iteration. In the onlooker bees phase, an adaptive step size based on damping motion is designed to replace the random step size, which can balance the global exploration and the local exploitation. Through the simulation of six test functions, we get the average convergence algebra, the mean, the best and the variance of the optimal solution through 30 experiments. Simulation shows that the improved algorithm has a better performance than the basic one.

A Discrete Dynamic Artificial Bee Colony with Hyper-Scout for RESTful web service API test suite generation

Microservices have become quite important because they decompose an application into a collection of highly maintainable services and reduce the complexity of monolithic architecture. Although the most common method of microservice development is the REST architecture, the number of studies on the automatic testing of RESTful APIs are very limited. For automatic testing of RESTful APIs, different algorithms have been proposed. However, their performance depends on the type of problem being addressed. This study implements the basic Artificial Bee Colony (ABC) algorithm for automatic testing of RESTful web services, and addresses some shortcomings for the RESTful test generation. To overcome these shortcomings, we propose a Discrete Dynamic Artificial Bee Colony with Hyper-Scout (DABC-HS) algorithm for the service-oriented problem. We enhanced the exploration ability of the algorithm by integrating a hyper-scout unit. The presence of solutions that correspond to covered targets but are not related to the remaining targets causes overburdening of the search. In the proposed approach, the onlooker bee phase generates a pool of all solutions and their mutants and applies a dominance-based selection operator to select high-quality solutions related to uncovered targets. The experimental results demonstrate that the DABC-HS recorded the highest performance in four of the seven problems while demonstrating similarly good performance in other problems. MOSA, which is the powerful algorithm in the literature, was the best in two of seven problems while ABC algorithm achieved the best performance in one problem. Although the method could not achieve the best result in all problems, it is more successful than the other published methods in the literature.

Archive-based multi-criteria Artificial Bee Colony algorithm for whole test suite generation

Testing an object-oriented software is harder than testing a structural program due to inheritance, states, behaviour of different objects, association, and polymorphism properties of the object-oriented software, which is established based on classes. The classes should be carefully tested using predefined test cases to optimize coverage goals. Because generating test cases manually is labour-intensive, search-based software testing methods that maximize coverage criteria by a search algorithm can be used to generate test cases automatically. Artificial Bee Colony (ABC) algorithm is a powerful search tool that performs balanced exploration and exploitation. In this study, two new multi-criteria and combinatorial ABC algorithms using mutation and crossover operators are proposed to generate a test suite that maximizes a fitness function combining various goals for object-oriented software. First, an archive-based ABC algorithm is proposed to keep covered targets in the archive to use available search resource efficiently. Second, the archive-based ABC algorithm is improved to bring more diversity in the population. To investigate the effect of the archive, the basic ABC algorithm and the archive-based ABC algorithms are compared on a set of classes from SF110 corpus. To validate efficiency of the proposed methods, they are compared to state-of-the-art evolutionary test suite generators and random testing methodology on the classes divided into difficulty levels based on their weight method class values. The experimental results reveal that introducing an archive into the ABC algorithm provides fast convergence compared to the basic ABC and the improved archive-based ABC produces higher coverage for the problems with both low and high difficulty levels.