Original Artificial Bee Colony Algorithm Research Articles

OBABC: A one-dimensional binary artificial bee colony algorithm for binary optimization

Artificial bee colony (ABC) algorithm is a widely utilized swarm intelligence (SI) algorithm for addressing continuous optimization problems. However, most binary variants of ABC (BABC) algorithms may suffer from issues such as invalid searches and high complexity when applied to binary problems. To address these challenges, we first establish a set of criteria for developing a BABC algorithm. Following these criteria, we propose a novel BABC algorithm, denoted as oBABC, which not only adheres to the defined criteria but also successfully inherits the advantages of original ABC algorithm. To evaluate the performance of oBABC and verify its effectiveness, experiments are conducted on two typical binary problems: uncapacitated facility location problem (UFLP) and maximum cut problem (Max-Cut). The experimental results reveal the following findings: 1) The validity of the criteria and the accuracy of the theoretical analysis are confirmed. oBABC exhibits high search efficiency with an invalid learning rate (ILR) of 0 %, while the ILRs of other BABC algorithms almost exceeds 20 %. 2) In terms of search efficiency and capability, oBABC exhibits a significant improvement in search efficiency and consistently ranks at the top in terms of optimization capability. These results suggest that oBABC may be a highly efficient and effective tool for solving binary problems.

A modified version of the ABC algorithm and evaluation of its performance

The artificial bee colony (ABC) optimization algorithm has been widely used to solve the global optimization problems. Many versions of ABC algorithm exist in the literature intending to achieve optimum solution for problems in different domains. Some modifications of the ABC algorithm are general and can be applied to any problem domain, while some are application dependent. This paper proposes a modified version of the ABC algorithm named as, MABC-SS (modified artificial bee colony algorithm with selection strategy), that can be applied to any problem domain. The algorithm is modified in terms of population initialization and update of a bee position using the old and a new food source equation based on the algorithm's performance in the previous iteration. The selection strategy is measured based on a novel approach called the rate of change. The population initialization in any optimization algorithm plays an important role in achieving the global optimum. The algorithm proposed in the paper uses random and an opposition-based learning technique to initialize the population and updates a bee's position after exceeding a certain number of trial limits. The rate of change is based on the average cost and is calculated for the past two iterations and compared for a method to be used in the current iteration to achieve the best result. The proposed algorithm is experimented with 35 benchmark test functions and 10 real world test functions. The findings indicate that the proposed algorithm is able to achieve the optimal result in most cases. The proposed algorithm is compared with the original ABC algorithm, modified versions of the ABC algorithm, and other algorithms in the literature using the test mentioned above. The parameters such as population size, number of iterations and runs were kept same for comparison with non-variants of ABC. In case of ABC variants, ABC specific parameters such as abandonment limit factor (0.6) and acceleration coefficient (1) were kept same. Results indicate that in 40% of the traditional benchmark test functions, the suggested algorithm works better than other variants of ABC (ABC, GABC, MABC, MEABC, BABC, and KFABC), while 30% of the traditional benchmark test functions are comparable. The proposed algorithm was compared to non-variants of ABC as well. The results show that the proposed algorithm achieved the best mean result in 50% of the CEC2019 benchmark test functions and in 94% of the classical benchmark test functions. The result is confirmed by Wilcoxon sum ranked test which shows that MABC-SS achieved statistically significant result in 48% of the classical and 70% of the CEC2019 benchmark test functions when compared with the original ABC. Overall, based on assessment and comparison in benchmark test functions used in this paper, the suggested algorithm is superior to others.

Umbrellalike Hierarchical Artificial Bee Colony Algorithm

Many optimisation algorithms improve the algorithm from the perspective of population structure. However, most improvement methods simply add hierarchical structure to the original population structure, which fails to fundamentally change its structure. In this paper, we propose an umbrellalike hierarchical artificial bee colony algorithm (UHABC). For the first time, a historical information layer is added to the artificial bee colony algorithm (ABC), and this information layer is allowed to interact with other layers to generate information. To verify the effectiveness of the proposed algorithm, we compare it with the original artificial bee colony algorithm and five representative meta-heuristic algorithms on the IEEE CEC2017. The experimental results and statistical analysis show that the umbrellalike mechanism effectively improves the performance of ABC.

A hybrid artificial bee colony algorithmic approach for classification using neural networks

Artificial neural networks are an integral component of most corporate and research functions across different platforms. However, depending upon the nature of the problem and quality of initialisation values, the usage of standard stochastic gradient descent always risks the possibility of getting trapped in local minima and saddle points for smaller neural networks in particular. One way to overcome this is by using algorithms with proven global search capabilities to train the network. This allows the neural net to reach the optimum values for weights regardless of the initialisation parameters used during training. Two algorithms are proposed based on modifications to the original artificial bee colony algorithm and their performances are analysed extensively on three benchmark datasets of increasing complexity. The first (NMABC), employs neural network appropriate initialisation and linear search space expansion. This is integrated into the second (LHABC), and incorporates stochastic gradient descent into the employed phase of the bees for faster convergence. It is found that the proposed algorithms consistently outperform standard approaches in all cases.

A2BCF: An Automated ABC-Based Feature Selection Algorithm for Classification Models in an Education Application

Feature selection is an essential step of preprocessing in Machine Learning (ML) algorithms that can significantly impact the performance of ML models. It is considered one of the most crucial phases of automated ML (AutoML). Feature selection aims to find the optimal subset of features and remove the noninformative features from the dataset. Feature selection also reduces the computational time and makes the data more understandable to the learning model. There are various heuristic search strategies to address combinatorial optimization challenges. This paper develops an Automated Artificial Bee Colony-based algorithm for Feature Selection (A2BCF) to solve a classification problem. The application domain evaluating our proposed algorithm is education science, which solves a binary classification problem, namely, undergraduate student success. The modifications made to the original Artificial Bee Colony algorithm make the algorithm a well-performed approach.

The Prediction of Sports Economic Development Prospect in Different Regions by Improved Artificial Bee Colony Algorithm

In order to study the development of the sports economy in different regions and analyze the future development prospect of sports economy, this paper uses the k-clustering method to improve the artificial bee colony algorithm and further improve the clustering degree of the bee colony. Among them, the improved artificial bee colony algorithm reduces the incidence of local extreme and improves the accuracy of calculation by setting the weight and threshold of indicators. MATLAB simulation results show that the prediction accuracy of the improved artificial bee colony algorithm for the development prospect of sports economy is 96–99%, and the calculation time is 0–17 seconds. Therefore, the improved artificial bee colony algorithm can best predict the development of the sports economy in different regions, and its accuracy, periodicity, and calculation time are better than those of the original artificial bee colony algorithm.

A hybrid artificial bee colony algorithm for transformer vibration fundamental frequency amplitude prediction

The fundamental frequency amplitude of transformer surface vibration signal is an important basis for judging transformer status. It is very important to predict the amplitude of fundamental frequency quickly and accurately. In this paper, a method is proposed to optimize the prediction of the transformer vibration fundamental frequency amplitude by modifying the artificial bee colony algorithm. An opposition-based learning mechanism is introduced and the search formula of each bee species is improved at the initial stage of the artificial bee colony algorithm. The performance of the proposed method is evaluated by five standard test functions and transformer vibration fundamental frequency amplitude prediction. Experimental results show that the proposed method is much better than the original artificial bee colony algorithm in search accuracy, convergence speed, and robustness, and improve the prediction accuracy.

Nonlinear hysteretic parameter identification using improved artificial bee colony algorithm

Hysteresis is a common phenomenon arising in many engineering applications. It describes a memory-based relation between the restoring force and the displacement. Identification of the hysteretic parameters is central to practical application of the hysteretic models. To proceed so, a noteworthy thing is that the hysteretic models are often complex and non-differentiable so that getting the gradients is never straightforward and therefore, the swarm-based algorithm is often preferable to inverse hysteretic parameter identification. Along these lines, an improved artificial bee colony algorithm is developed in this paper for general hysteretic parameter identification. On the one hand, several hysteretic models along with the extensions to tackle the degradation and pinching behaviours are considered and how to model a structure with hysteretic components is also elaborated. As a result, the governing equation for the direct problem is established. On the other hand, the differential evolution mechanism is introduced to improve the original artificial bee colony algorithm. Numerical examples are conducted to testify the feasibility and accuracy of the proposed method in nonlinear hysteretic parameter identification.

A new standard error based artificial bee colony algorithm and its applications in feature selection

Feature selection is a basic task for pattern recognition and classification. It enhances the performance of the classification algorithms with the help of removing the redundant features. Thanks to eliminating irrelevant features, the computational time is decreased. Thus, intensive works have been carried out in this area. This paper proposes a new standard error-based artificial bee colony (SEABC) algorithm for the feature selection problem, which is developed by integrating standard error-based new solution search mechanisms into the original artificial bee colony algorithm. The SEABC algorithm is used for feature selection. Shannon entropy function is used to serve as the objective function of the SEABC algorithm. Thirteen datasets are used from UCI machine learning datasets. Features are selected according to Shannon conditional entropy values and then a threshold process is implemented to find their best relevant subset. Support Vector Machines (SVMs) and k-Nearest Neighbor (KNN) are used as the optimal classifiers. The proposed SEABC algorithm is compared with genetic algorithm (GA), particle swarm optimization (PSO), ABC, improved ABC (I-ABC), Gbest-guided ABC (GABC), and PS-ABC algorithms. In general, it is observed that the SEABC algorithm achieves better classification results than other well-known algorithms.

A Novel Cost-Effective Controller Placement Scheme for Software-Defined Vehicular Networks

Energy costs have dramatically increased in data center networks as an increasing number of large-scale Internet applications are used. In software-defined vehicular networks (SDVN), the communication delay between two vehicles and between vehicles and the controller will dramatically climb up as the number of vehicles increases. This requires more controllers to provide communication service to minimize the latency. More controllers lead to high energy costs. Therefore, the number of controllers and their placement, the so-called controller placement problem (CPP), should be addressed. The appropriate placement of controllers can decrease the energy cost, enabling green communication in SDVN. Although CPP has been studied for static networks, it has not been effectively solved in highly dynamic and complex networks. In this article, a novel cost-effective CPP scheme for SDVN is proposed. First, our proposed minimum controller selection mechanism (MOSA) can reduce the number of controllers and guarantee the coverage of the area. Besides, an improved multiobjective artificial bee colony algorithm (IMABC) is proposed based on the original artificial bee colony algorithm. The IMABC can judge which controller should be switched on for data transmission based on real-time traffic flow. A route computation mechanism is proposed to evaluate the performance of our CPP scheme. The experimental results confirm that compared to other existing CPP schemes, our scheme can achieve a higher packet delivery ratio while greatly reducing energy consumption and latency.

An improved artificial bee colony algorithm based on mean best-guided approach for continuous optimization problems and real brain MRI images segmentation

The artificial bee colony (ABC) algorithm is a relatively new algorithm inspired by nature and has been shown to be efficient in contrast to other optimization algorithms. Nonetheless, ABC has some similar drawbacks to the optimization algorithms in terms of the unbalanced search behavior. The original ABC algorithm shows strong exploration capability with ineffective exploitation due to the unbalanced search model. In this paper, a new ABC algorithm called MeanABC is introduced to achieve the search behavior balance via a modified search equation based on the information of the mean of the previous best solutions. To evaluate the performance of the proposed algorithm, experiments were divided into two parts: First, the proposed algorithm was tested on a comprehensive set of 14 benchmark functions. The results show that the proposed MeanABC enhances the performance of the original ABC in terms of faster global convergence speed, solution quality, and better robustness when compared to other ABC variants. Secondly, the proposed algorithm was applied as a hybrid with the FCM algorithm as a segmentation technique to a set of 20 volumes of real brain MRI images with 20 images for each volume. All of these images have several characteristics, levels of difficulty, and cover different domains. The obtained results are promising, especially when the performance of the proposed algorithm was compared to other state-of-the-art segmentation techniques.

Global optimization of controlled source audio-frequency magnetotelluric data with an improved artificial bee colony algorithm

The inversion of controlled source audio-frequency magnetotelluric (CSAMT) data is a complex nonlinear problem. A linearization of this problem is easily trapped in local minima and the complexity of the artificial source makes CSAMT data interpretation more difficult than that of magnetotelluric (MT) data. This paper presents an improved artificial bee colony (ABC) algorithm for the 2.5D inversion of CSAMT data. New initialization and generation strategies are proposed to improve the optimization achieved by the original ABC algorithm. The global optimization of CSAMT by the improved ABC algorithm is realized based on 2.5D forward modeling theory and is used in the inversion of a complex model of water-bearing anomalous bodies in sandstone. Results show that the algorithm can accurately recover the resistivity and spatial distribution of strata and anomalous bodies. The survey data for a suspected collapse column in Shandong Province are also processed using the proposed method, and inversion via the algorithm accurately shows the water abundance of the suspected collapse column. Thus, the results of the theoretical modeling and practical data indicate that the improved ABC algorithm is effective for analyzing CSAMT data. Moreover, this algorithm improves the interpretational accuracy and resolution of CSAMT data.

Networked correlation-aware manufacturing service supply chain optimization using an extended artificial bee colony algorithm

Manufacturing service supply chain (MSSC) optimization has been intensively studied to find an optimal service composition solution with the best quality of service (QoS) value. However, traditional MSSC optimization methods usually assume that candidate services are independent of one another. Therefore, potentially better MSSC solutions may have been neglected by not considering the positive influence of correlations between services on the QoS value. This study proposes a novel networked correlation-aware manufacturing service composition (NCMSC) mathematical model to characterize the influence of vertical and horizontal correlations between services on the QoS value of MSSC solution. To solve the NCMSC model, an extended artificial bee colony (ABC) algorithm is proposed to find a near-optimal solution with the best QoS value. The specific improvements to the original ABC algorithm include the following: (1) a new matrix-based encoding scheme is proposed to describe the MSSC solution in which each column contains a vertical composite structure and collaborative services for each subtask; (2) the migration operator of a biogeography-based optimization algorithm is combined with the original ABC algorithm to address the discrete MSSC optimization problem and improve the performance of the original ABC algorithm. The results of the experiments illustrate the importance of networked correlations between services, better practicality, effectiveness, and efficiency of the extended ABC algorithm in solving the optimization problem of MSSC.

Interval Type-2 Neural Fuzzy Controller-Based Navigation of Cooperative Load-Carrying Mobile Robots in Unknown Environments.

In this paper, a navigation method is proposed for cooperative load-carrying mobile robots. The behavior mode manager is used efficaciously in the navigation control method to switch between two behavior modes, wall-following mode (WFM) and goal-oriented mode (GOM), according to various environmental conditions. Additionally, an interval type-2 neural fuzzy controller based on dynamic group artificial bee colony (DGABC) is proposed in this paper. Reinforcement learning was used to develop the WFM adaptively. First, a single robot is trained to learn the WFM. Then, this control method is implemented for cooperative load-carrying mobile robots. In WFM learning, the proposed DGABC performs better than the original artificial bee colony algorithm and other improved algorithms. Furthermore, the results of cooperative load-carrying navigation control tests demonstrate that the proposed cooperative load-carrying method and the navigation method can enable the robots to carry the task item to the goal and complete the navigation mission efficiently.

A Modified Artificial Bee Colony for the Non-Smooth Dynamic Economic/Environmental Dispatch

This paper presents an improved artificial bee colony (ABC) technique for solving the dynamic economic emission dispatch (DEED) problem. Ramp rate limits, valve-point loading effects and prohibited operating zones (POZs) have been considered. The proposed technique integrates the grenade explosion method and Cauchy operator in the original ABC algorithm, to avoid random search mechanism. However, the DEED is a multi-objective optimization problem with two conflicting criteria which need to be minimized simultaneously. Thus, it is recommended to provide the best solution for the decision-makers. Shannon’s entropy-based method is used for the first time within the context of the on-line planning of generator outputs to extract the best compromise solution among the Pareto set. The robustness of the proposed technique is verified on six-unit and ten-unit system tests. Results proved that the proposed algorithm gives better optimum solutions in comparison with more than ten metaheuristic techniques.

Artificial bee colony algorithm: A component-wise analysis using diversity measurement

A swarm-based metaheuristic algorithm, like artificial bee colony (ABC), embraces four key elements of collective intelligence: positive feedback, negative feedback, multiple interactions, and fluctuation. Fluctuation refers to population diversity which can be measured using dimension-wise diversity. This paper performed component-wise analysis of ABC algorithm using diversity measurement. The analysis revealed scout bees component as counterproductive and onlooker bees component with poor global search ability. Subsequently, an ABC algorithm without scout bees component and modified onlooker bees component is proposed in this paper. The effectiveness and efficiency of the proposed ScoutlessABC is validated on test suite of a dozen of benchmark functions. To further evaluate the performance, ScoutlessABC is employed on the parameter training problem of fuzzy neural network for solving eight classification problems. The experimental results show that ScoutlessABC maintains strong convergence ability than the original ABC algorithm. Overall, this study has two major contributions: (a) an effective component-wise analysis approach using diversity measurement and (b) a simplified and modified ABC variant with enhanced search efficiency.

An artificial bee colony algorithm search guided by scale-free networks

Many optimization algorithms have adopted scale-free networks to improve the search ability. However, most methods have merely changed their population topologies into those of scale-free networks; their experimental results cannot verify that these algorithms have superior performance. In this paper, we propose a scale-free artificial bee colony algorithm (SFABC) in which the search is guided by a scale-free network. The mechanism enables the SFABC search to follow two rules. First, the bad food sources can learn more information from the good sources of their neighbors. Second, the information exchange among good food sources is relatively rare. To verify the effectiveness of SFABC, the algorithm is compared with the original artificial bee colony algorithm (ABC), several advanced ABC variants, and other metaheuristic algorithms on a wide range of benchmark functions. Experimental results and statistical analyses indicate that SFABC obtains a better balance between exploration and exploitation during the optimization process and that, in most cases, it can provide a competitive performance of the benchmark functions. We also verify that scale-free networks can not only improve the optimization performance of ABC but also enhance the search ability of other metaheuristic algorithms, such as differential evolution (DE) and the flower pollination algorithm (FPA).

A Novel Artificial Bee Colony Optimization Algorithm with SVM for Bio-inspired Software-Defined Networking

In recent years, artificial intelligence and bio-inspired computing methodologies have risen rapidly and have been successfully applied to many fields. Bio-inspired network systems are a field of biology and computer science, it has the high relation to the bio-inspired computing and bio-inspired system. It has the self-organizing and self-healing characteristics that help them in achieving complex tasks with much ease in the network environment. Software-defined networking provides a breakthrough in network transformation. However, increasing network requirement and focus on the controller for determining the network functionality and resources allocations aims at self-management capabilities. More recently, the artificial bee colony (ABC) algorithm has been used to solve the issues of parameter optimization. In this paper, a discretized food source for an artificial bee colony (DfABC) optimization algorithm is proposed and applied to optimize the kernel parameters of a support vector machine (SVM) model, creating a new hybrid. In order to further improve prediction accuracy, the proposed DfABC algorithm is applied to six popular UCI datasets. We also compare the DfABC algorithm to particle swarm optimization (PSO), the genetic algorithm (GA), and the original ABC algorithm. The experimental results show that the proposed DfABC-SVM model achieves better classification accuracy with a shorter convergence time, outperforming the other hybrid artificial intelligence models.

RETRACTED ARTICLE: Analysis of image processing algorithm based on bionic intelligent optimization

In order to improve the speed and the accuracy of image segmentation, the nectar source fitness and defect nectar source replacement are improved through original artificial bee colony algorithm, in this paper, an optimized algorithm for combined use of artificial bee colony and Ostu is proposed. This method simulates the process of honey bee colony, and the threshold is regarded as nectar source, the fitness is regarded as the content of nectar source, then the segmentation of the image is completed successfully. In order to improve the speed of image segmentation, the long-term use of honey in original artificial bee colony is replaced with new nectar source, which can improve the running speed of the algorithm; In order to increase the accuracy of the algorithm and avoid local optimization, the fitness formula is meticulous through the fitness adjustment on nectar source. The experimental results show that the image segmentation reaches the ideal state, and the speed and precision of the segmentation are improved.

An Artificial Bee Colony Algorithm Based on Dynamic Penalty and Lévy Flight for Constrained Optimization Problems

Artificial bee colony (ABC) algorithm is one of the most popular intelligence algorithms, which has been widely applied to some unconstrained optimization problems. Many improved versions of ABC algorithm are also used for solving constrained optimization problems (COPs). An artificial bee colony algorithm based on dynamic penalty function and Levy flight (DPLABC) is presented for COPs in this paper. Based on the original ABC algorithm, four modifications are put forward in this newly proposed algorithm: The dynamic penalty method is used to handle the constraints; Levy flight with logistic map is applied in the employed bee phase; according to the selection probability, a further search mechanism which is learned from the best solution and two other neighbor food sources is adopted for onlooker bees; different from pulling back to the upper and lower limits, the new boundary handling mechanism inspired by the best solution is also given to repair the invalid solutions. To validate the performance of DPLABC algorithm, it is tested on 13 constrained benchmark functions from 2006 IEEE Congress on Evolution Computation (CEC2006) and four engineering design problems. The experimental results indicate that DPLABC is competitive with the state-of-the-art algorithms including dynamic difference search algorithm and several improved variants of ABC for solving COPs.