Hybrid Artificial Bee Colony Algorithm Research Articles

Retraction notice: Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks

Retraction notice: Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks

Hybrid Artificial Bee Colony Algorithm with Variable Neighborhood Search for Capacitated Vehicle Routing Problem

Aiming at the capacitated vehicle routing problem, a hybrid integer programming model with goal of lowest path cost is constructed, and a hybrid artificial bee colony algorithm with variable neighborhood search based on the model and the characteristics of the CVRP problem is proposed to solve the problem. The hybrid algorithm integrates the artificial bee colony algorithm and the variable neighborhood search algorithm, embeds a multi-variable neighborhood operator in the local search link of the artificial bee colony to carry out iteration. And the operator contains targeted transformation operations on path nodes, strings, and sub paths to ensure the diversity of the bee population. In addition, a variable neighborhood perturbation strategy is used to strengthen the algorithm's ability to escape from local optima. The comparative analysis of the literature study set and its algorithm solution shows that the designed hybrid variable neighborhood artificial bee colony algorithm has strong global search ability and high solution accuracy, especially in solution stability. HABC-VNS can obtain 47 optimal solutions in 74 examples. The average minimum deviation of the optimal solution is 0.34%, and the average deviation of the average CVRP set is 0.57%. The overall performance is better than the algorithm in the comparative literature.

Multi-objective fuzzy partial disassembly line balancing considering preventive maintenance scenarios using enhanced hybrid artificial bee colony algorithm

In the existing researches about partial disassembly line balancing problem (PDLBP), all workstations are assumed always be available. In the real world, however, aging and failure of machines are inevitable, which may result in the shutdown of the whole disassembly line. To solve this problem, this study introduces preventive maintenance scenarios into PDLBP to prevent unexpected breakdowns and promote the productivity and smoothness of the disassembly line. Then, considering the substantial level of uncertainty in the state of end-of-life products, a multi-objective fuzzy mathematical model is established to minimize the cycle time, disassembly profit, and total assignment plan alteration simultaneously. And an enhanced hybrid artificial bee colony algorithm is developed to solve it. The attribute-driven heuristic strategy is applied to improve the quality of initial food sources, and the adaptive neighborhood search is designed to improve the exploitation efficiency of the employed bees and onlooker bees. Additionally, a hybrid global learning strategy is developed to guide the scout bees to raise the diversity of food sources. Finally, three case studies are conducted to validate the proposed algorithm. The results indicate that the proposed algorithm can achieve superior performance.

Hybrid Artificial Bee Colony and JAYA Algorithm for Linear Antenna Array Synthesis

Hybrid Artificial Bee Colony and JAYA Algorithm for Linear Antenna Array Synthesis

Modelling of Subject Scheduling Systems Using Hybrid Artificial Bee Colony Algorithm

A common schedule problem found in colleges is the positioning of courses in a certain space and time. This placement process often encounters barriers that must be met so that there is no imbalance in the school schedule. One of the problems that often arise is the placement of class capacity that does not match the course requirements. In this study, the researchers used the Artificial Bee Colony Hybrid Algorithm (HABC) to construct course schedules efficiently at the college. The objective of the research was to develop a course scheduling system using the HABC algorithm by combining the Engineering of Artificial Bee Colony (ABC) and genetic algoritms, especially on the crossover process to better address the schedule problems. The research procedure used is to design and implement a course scheduling system using the Hybrid ABC algorithm. The results of the research demonstrate that the Hybrid ABC algorithm is effective in generating optimal course schedule schedules, in line with time limits, room needs, and lecturer requirements and can automate course schedule processes, saving time and resources, while ensuring optimal schedules.

Solving distributed low carbon scheduling problem for large complex equipment manufacturing using an improved hybrid artificial bee colony algorithm

Multi-agent collaborative manufacturing, high energy consumption and pollution, and frequent operation outsourcing are the three main characteristics of large complex equipment manufacturing enterprises. Therefore, the production scheduling problem of large complex equipment to be studied is a distributed flexible job shop scheduling problem involving operation outsourcing (Oos-DFJSP). Besides, the influences of each machine on carbon emission and job scheduling at different processing speeds are also involved in this research. Thus the Oos-DFJSP of large complex equipment consists of the following four sub-problems: determining the sequence of operations, assigning jobs to manufactories, assigning operations to machines and determining the processing speed of each machine. In the Oos-DFJSP, if a job is assigned to a manufactory of a group manufacturing enterprise, and the manufactory cannot complete some operations of the workpiece, then these operations will be assigned to other manufactories with related processing capabilities. Aiming at solving the problem, a multi-objective mathematical model including costs, makespan and carbon emission was established, in which energy consumption, power generation of waste heat and treatment capacity of pollutants were considered in the calculation of carbon emission. Then, a multi-objective improved hybrid genetic artificial bee colony algorithm was developed to address the above model. Finally, 45 groups of random comparison experiments were presented. Results indicate that the developed algorithm performs better than other multi-objective algorithms involved in the comparison experiments not only on quality of non-dominated solutions but also on Inverse Generational Distance and Error Ratio. That is, the proposed mathematical model and algorithm were proved to be an excellent method for solving the multi-objective Oos-DFJSP.

Underwater clustering based hybrid routing protocol using fuzzy ELM and hybrid ABC techniques

Underwater wireless sensor networks (UWSNs) are designed to perform cooperative monitoring and data collection tasks by combining several elements, such as automobiles and sensors located in a particular acoustic area. Several studies have been carried out to improve energy efficiency and routing reliability. However, UWSN faces several challenges, such as high ocean interference and noise, long transmission delays, limited bandwidth, and low sensor node battery energy. In this work, a novel underwater clustering-based hybrid routing protocol (UC-HRP) has been proposed to address these issues. The overall process is carried out in three phases. In the first phase, the fuzzy-ELM approach is used to initialize the cluster based on parameters such as Doppler spread, path loss, noise, and multipath. In the second phase, the cluster head is selected using Cluster Centre Cluster Head Selection (C3HS) based on Link quality, distance, node degree, and residual energy. In the third phase, Hybrid Artificial Bee Colony (HABC) algorithm is used for selecting an optimal route based on the parameters such as reliability, bandwidth effectiveness, average path loss, and average transmission latency. The performance of the proposed UC-HRP method is evaluated using a variety of parameters, including the network lifetime, packet delivery ratio, alive nodes, and energy consumption. The proposed technique improves the network lifetime by 14.03%, 16.25%, and 18.34% better than ACUN, ANC-UWSNS, and MERP respectively.

Optimizing the Design of an Interior Permanent Magnet Synchronous Motor for Electric Vehicles with a Hybrid ABC-SVM Algorithm

This paper presents a comprehensive investigation of the optimal design of an interior permanent magnet synchronous motor (IPMSM) for electric vehicles (EVs), utilizing the hybrid artificial bee colony algorithm–support vector machine (HAS) algorithm. The performance of the drive motor is a crucial determinant of the overall vehicle performance, particularly in EVs that rely solely on a motor for propulsion. In this context, interior permanent magnet synchronous motors (IPMSMs) offer a compelling choice due to their high torque density, wide speed range, superior efficiency, and robustness. However, accurate analysis of the nonlinear characteristics of IPMSMs necessitates finite element analysis, which can be time-consuming. Therefore, research into methods for deriving an optimal model with minimal computation is of significant importance. The HAS is a powerful multimodal optimization technique that is capable of exploring several optimal solutions. It enhances the navigation capability by combining the artificial bee colony algorithm (ABC) with the kernel support vector machine (KSVM). Specifically, the algorithm improves the search ability by optimizing the movement of bees in each region generated by the KSVM. Furthermore, hybridization with the Nelder–Mead method ensures accurate and quick convergence at pointers discovered in the ABC. To demonstrate the effectiveness of the proposed algorithm, this study compared its performance with a conventional algorithm in two mathematical test functions, verifying its remarkable performance. Finally, the HAS algorithm was applied to the optimal design of the IPMSM for EVs. Overall, this paper provides a thorough investigation of the application of the HAS algorithm to the design of IPMSMs for electric vehicles, and its application is expected to benefit from the combination of machine-learning techniques with various other optimization algorithms.

Modelling of Photovoltaic Modules Based on Hybrid Artificial Bee Colony Algorithm

In recent decades, the application of photovoltaic (PV) power generation and other new energy begins to attract people’s attention. The modelling of PV modules is significant for accurately estimating the generation power and electrical characteristic of the PV modules or arrays under complicated outdoor conditions. In this paper, a hybrid ABC algorithm is proposed. The model parameters of PV modules can be identified. In the proposed algorithm, three kinds of bees are hybrid, and they can complement each other and change roles dynamically, which makes the performance of the algorithm better than other basic optimization algorithms. Experimental results verify that the proposed hybrid ABC can effectively identify the model parameters and achieves better modelling accuracy of the I-V characteristics.

Application of hybrid artificial bee colony algorithm based on load balancing in aerospace composite material manufacturing

The manufacturing of composite materials for the aerospace industry represents a cornerstone of future development in this advanced field. In this study, a mathematical model of flexible job-shop scheduling problem (FJSP) was established by extracting the key manufacturing information from an aerospace composite manufacturing workshop. Intelligent algorithms can effectively solve FJSP. However, when applied to a multi-objective FJSP (MOFJSP), they suffer from drawbacks such as blind search and low efficiency. Therefore, this study proposes a new artificial bee colony algorithm and implements it as part of a hybrid method (referred to as the HMABC) to solve MOFJSP. The method involves three steps: (1) A multiple-rule initial population is constructed to minimize randomness and improve quality. (2) A greedy decoding method is used to decode each individual to further narrow the search range to replace the conventional decoding method. (3) By considering the machine load index (which has often been neglected), the concept of machine load rate is proposed, and three heuristic strategies are designed (with the influences ranging from small to high) to diverge the solution in all directions instead of just a direction. The performance of the proposed method is compared with those of existing algorithms using a classical FJSP test example, and the effectiveness and efficiency of HMABC is verified. Finally, the method is applied to an actual aerospace composite manufacturing system to realize the scheduling of actual production activities.

A hybrid artificial bee colony algorithm and support vector machine for predicting blast-induced ground vibration

A hybrid artificial bee colony algorithm and support vector machine for predicting blast-induced ground vibration

A New Neural Network Training Algorithm Based on Artificial Bee Colony Algorithm for Nonlinear System Identification

Artificial neural networks (ANNs), one of the most important artificial intelligence techniques, are used extensively in modeling many types of problems. A successful training process is required to create effective models with ANN. An effective training algorithm is essential for a successful training process. In this study, a new neural network training algorithm called the hybrid artificial bee colony algorithm based on effective scout bee stage (HABCES) was proposed. The HABCES algorithm includes four fundamental changes. Arithmetic crossover was used in the solution generation mechanisms of the employed bee and onlooker bee stages. The knowledge of the global best solution was utilized by arithmetic crossover. Again, this solution generation mechanism also has an adaptive step size. Limit is an important control parameter. In the standard ABC algorithm, it is constant throughout the optimization. In the HABCES algorithm, it was determined dynamically depending on the number of generations. Unlike the standard ABC algorithm, the HABCES algorithm used a solution generation mechanism based on the global best solution in the scout bee stage. Through these features, the HABCES algorithm has a strong local and global convergence ability. Firstly, the performance of the HABCES algorithm was analyzed on the solution of global optimization problems. Then, applications on the training of the ANN were carried out. ANN was trained using the HABCES algorithm for the identification of nonlinear static and dynamic systems. The performance of the HABCES algorithm was compared with the standard ABC, aABC and ABCES algorithms. The results showed that the performance of the HABCES algorithm was better in terms of solution quality and convergence speed. A performance increase of up to 69.57% was achieved by using the HABCES algorithm in the identification of static systems. This rate is 46.82% for the identification of dynamic systems.

A customer-oriented method to support multi-task green scheduling with diverse time-of-use prices in Cloud Manufacturing

Green manufacturing has become a hot topic globally, and Cloud Manufacturing (CMfg) provides a novel approach to support green manufacturing through manufacturing services (MS) composition. In this study, we design a customer-oriented method for supporting multi-task green scheduling in CMfg. First, we aim to minimize the total energy consumption during scheduling. Second, because CMfg is a type of customer-oriented manufacturing, the just-in-time objective is used to meet the customers on-time requirements. Subsequently, we study the different time-of-use prices of MS in CMfg. The price of utilizing MS is related not only to cost but also to the supply-demand relationship, which results in diverse time-of-use prices for MS. After establishing the scheduling model, a hybrid artificial bee colony algorithm is designed to solve the scheduling model. For the algorithm, we design a guiding mechanism to enhance the global search ability and an adaptive variable neighborhood search for improving the local search ability. To verify the performance of our algorithm, we compared it with similar algorithms, and the results indicate the high-level performance of our work.

Software Defects Prediction Based on Hybrid Beetle Antennae Search Algorithm and Artificial Bee Colony Algorithm with Comparison

Software defects are problems in software that destroy normal operation ability and reflect the quality of the software. Software fault can be predicted by the software reliability model. In this paper, the hybrid algorithm is applied to parameter estimation in software defect prediction. As a biological heuristic algorithm, BAS (Beetle Antennae Search Algorithm) has fast convergence speed and is easy to implement. ABC (Artificial Bee Colony Algorithm) is better in optimization and has strong robustness. In this paper, the BAS-ABC hybrid algorithm is proposed by mixing the two algorithms and the goal of the proposed method is to improve the convergence and stability of the algorithm. Five datasets were used to carry out the experiments, and the data results showed that the hybrid algorithm was more accurate than the single algorithm, with stronger convergence and stability, so it was more suitable for parameter estimation of the software reliability model. Meanwhile, this paper implemented the comparison between hybrid BAS + ABC and hybrid PSO + SSA, and the result shows that BAS + ABC has better performance both in convergence and stability. The comparison result shows the strong ability in estimation and prediction of software defects hybrid BAS and ABC.

A novel method for customer-oriented scheduling with available manufacturing time windows in Cloud Manufacturing

Cloud Manufacturing is customer-oriented manufacturing, customers’ satisfaction is important for the platform. To enhance the satisfaction of customers, previous studies aimed at maximizing the Quality of Services, but the timeliness which means delivering the products to customers on time has not been paid enough attention. For timeliness, because the customer is not rational, the satisfaction is not linear with tardiness and earliness, we use the prospect theory, a famous theory of psychology, to calculate the satisfaction based on tardiness and earliness in scheduling, not minimize the total tardiness and earliness like previous studies. Then, the manufacturing services may only provide several available manufacturing time windows to the platform, because part of the time was occupied by the previous customers or the manufacturing services’ own production before. To utilize the potential manufacturing capacity, we study the manufacturing services with time windows. Also, the adjusted speed of the manufacturing services is studied. Finally, to solve the scheduling problem, we proposed an improved hybrid artificial bee colony algorithm. For our proposed algorithm, to guide the evolution of the algorithm on the stage of employer bee, we design a new selecting mechanism based on the grey wolf algorithm. On the onlooker bee, we regard the solver as part of the algorithm, the solver is used to find new effective scheduling sequences. We also verify the algorithm's effectiveness through numerical experiments.

Maximum power point tracking of PEMFC based on hybrid artificial bee colony algorithm with fuzzy control

Maximum power point tracking (MPPT) is an effective method to improve the power generation efficiency and power supply quality of a proton exchange membrane fuel cell (PEMFC). Due to the inherent nonlinear characteristics of PEMFC, conventional MPPT methods are often difficult to achieve a satisfactory control effect. Considering this, artificial bee colony algorithm combining fuzzy control (ABC-fuzzy) was proposed to construct a MPPT control scheme for PEMFC. The global optimization ability of ABC algorithm was used to approach the maximum power point of PEMFC and solve the problem of falling into local optimization, and fuzzy control was used to eliminate the problems of large overshoot and slow convergence speed of ABC algorithm. The testing results show that compared with perturb & observe algorithm, conductance increment and ABC methods, ABC-fuzzy method can make PEMFC obtain greater output power, faster regulation speed, smaller steady-state error, less oscillation and stronger anti-interference ability. The MPPT scheme based on ABC-fuzzy can effectively realize the maximum power output of PEMFC, and plays an important role in improving the service life and power supply efficiency of PEMFC.

A Hybrid Artificial Bee Colony Algorithm to Solve a New Minimum Exposure Path Problem with Various Boundary Conditions for Wireless Sensor Networks

The original minimum exposure path (MEP) problem in WSNs (wireless sensor networks) requires the starting point and ending point of a moving target to be fixed, thus limiting the evaluation of coverage quality relative to the locations of these points. To resolve this issue, a minimum exposure path problem with various boundaries (VB-MEP) is proposed in this paper. Because the corresponding graph model cannot be established, the original classical methods (grid method and Voronoi diagram method) used to solve the minimum exposure path problem are no longer effective for the VB-MEP problem. This paper first transforms the problem into a hybrid optimization problem with constraint conditions and then considers the characteristics of the transformed mathematical optimization model with high dimensionality, unfixed dimensionality and high nonlinearity so that the deterministic optimization methods are no longer applicable. A hybrid artificial colony solution algorithm that incorporates the actual characteristics of the problem is designed, and a convergence analysis and a proof of the designed algorithm are given. Through simulation experiments based on large-scale node distribution scenarios, it is found that the designed hybrid optimization model with constraints and hybrid artificial bee colony solution algorithm can effectively solve the proposed VB-MEP problem.

SISTEM PENJADWALAN MATA PELAJARAN SEKOLAH MENGGUNAKAN METODE HYBRID ARTIFICIAL BEE COLONY (HABC)

scheduling is very closely related to teaching and learning activities in a school that aims to support, facilitate, enhance the quality of education and improve discipline in the process of teaching and learning activities. The problem faced in making a schedule of subjects is time, the process of making a schedule takes quite a long time in the process of processing the schedule. Of the algorithms used in scheduling problems, algortima Artificial Bee Colony is an algorithm inspired by the behavior of honeybee colonies that work based on the way bees forage for food. Algortima Artificial Bee Colony is known to have advantages over other optimization algorithms that are very efficient in finding optimal solutions. But the Artificial Bee Colony algorithm has its drawbacks that, if the dimensions of the problem increase, the exchange of information is still limited to one dimension. The weakness of Artificial Bee Colony is what makes the opportunity to develop ABC, namely, Hybrid Artificial Bee Colony (HABC) by adding a crossover operator of genetic algorithms. Genetics algorithm's crossover operator inserted into ABC algorithm to improve exchange of information between bees. It can be concluded that the application of HABC methods can do the optimization process quite well in scheduling issues with fairly low schedule clashing results. Based on the results of trials conducted five times, the results that scheduling applications using the HABC Algorithm can produce a schedule of subjects by clashing to a minimum with an average accuracy percentage rate of 98.03%

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.

Multi-Objective Task Scheduling Optimization for Load Balancing in Cloud Computing Environment Using Hybrid Artificial Bee Colony Algorithm With Reinforcement Learning

Workload balancing in cloud computing is still challenging problem, especially in Infrastructure as a Service (IaaS) in the cloud model. A problem that should not occur during cloud access is a host or server being overloaded or underloaded, which may affect the processing time or may result in a system crash. Therefore, to prevent these problems, an appropriate schedule of access should be considered so that the system can distribute tasks across all available resources, which is called load balancing. The load balancing technique should ensure that all Virtual Machines (VMs) are used appropriately. In this paper, an independent task scheduling approach in cloud computing is proposed using a Multi-objective task scheduling optimization based on the Artificial Bee Colony Algorithm (ABC) with a Q-learning algorithm,which is a reinforcement learning technique that helps the ABC algorithm work faster, called the MOABCQ method. The proposed method aims to optimize scheduling and resource utilization, maximize VM throughput, and create load balancing between VMs based on makespan, cost, and resource utilization, which are limitations of concurrent considerations. Performance analysis of the proposed method was compared using CloudSim with the existing load balancing and scheduling algorithms: Max-Min, FCFS, HABC_LJF, Q-learning, MOPSO, and MOCS algorithms in three datasets: Random, Google Cloud Jobs (GoCJ), and Synthetic workload. The experimental results indicated that the algorithms used MOABCQ approach outperformed the other algorithms in terms of reducing makespan, reducing cost, reducing degree of imbalance, increasing throughput and average resource utilization.