Genetic Artificial Bee Colony Research Articles

Optimizing sustainable reverse logistic networks: a case study of medical waste using the genetic artificial bee colony algorithm

Optimizing sustainable reverse logistic networks: a case study of medical waste using the genetic artificial bee colony algorithm

Solving line balancing and AGV scheduling problems for intelligent decisions using a Genetic-Artificial bee colony algorithm

Due to the rapid advancement of technology, the demand for electronic devices in various sectors such as consumer electronics, automotive, telecommunications, healthcare, and industrial applications, as well as customized printed circuit board (PCB) products, has significantly increased. Balancing the PCB assembly line is crucial for improving productivity to meet market demand, which means balancing the workload distributed among various assembly stations on the assembly line. However, the workload balancing depends on material-handling systems in production environments that facilitate the transportation of raw materials to the buffer storage of the assembly stations. In the PCB assembly system, the material handling is carried out using automated guided vehicles (AGVs). This paper studies the assembly line balancing and AGV scheduling collectively because of their dependency on each other. A mixed integer linear programming (MILP) model is formulated to balance the workload distribution among stations and schedule the AGVs. An existing intelligent platform of the real-life PCB industry is considered to solve this integrated problem with intelligent decisions. The platform includes three layers modules: physical layer, data management layer and application service layer. A novel genetic artificial bee colony (GABC) algorithm is proposed and embedded with an application service layer to optimize the current problem and give optimum solutions for the real-life physical layer. The proposed GABC algorithm incorporates the operators of the genetic algorithm (GA) i.e., crossover and mutation into the search process of the ABC algorithm. Additionally, the greedy selection feature is employed in GABC which significantly enhances the exploitation capabilities, leading to faster convergence, higher-quality solutions, and improved robustness. The performance of the proposed GABC algorithm is tested based on different parameters by comparing the results with GA, ABC and PSO algorithms. Experimental analysis indicates that the proposed GABC algorithm outperforms the compared algorithms. Therefore, the yielded solutions in terms of prompt responses help managers to expedite the production through intelligent decision-making based on customer demands.

A Genetic Artificial Bee Colony Algorithm for Investigating Job Creation and Economic Enhancement in Medical Waste Recycling

A Genetic Artificial Bee Colony Algorithm for Investigating Job Creation and Economic Enhancement in Medical Waste Recycling

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.

A Two-Stage Genetic Artificial Bee Colony Algorithm for Solving Integrated Operating Room Planning and Scheduling Problem With Capacity Constraints of Downstream Wards

A Two-Stage Genetic Artificial Bee Colony Algorithm for Solving Integrated Operating Room Planning and Scheduling Problem With Capacity Constraints of Downstream Wards

Multi-task service composition model considering competitive resource constraint in cloud manufacturing

Service composition optimization is one of the core issues of cloud manufacturing research. At present, relevant researches mostly focus on single-task service composition, and there are few reports on the problem of multi-task service composition considering resource competition. However, in the real cloud manufacturing environment, multi-task service composition is common, so how to formulate a robust multi-task service composition model is a meaningful research direction. This paper first proposes a multi-task service composition optimization model considering resource competition constraints (MTSCOM-RCC) in the cloud manufacturing environment to fill the above-mentioned gap. The MTSCOM-RCC model considers the conflicts of interest and time between parallel subtasks and proposes an important service evaluation indicator. Secondly, a two-layer coding method, including task layer and subtask, is proposed based on studying the MTSCOM-RCC’s competition mechanism and characteristics. An improved hybrid genetic artificial bee colony algorithm model is proposed to solve the multi-task model. The model is combined with the global optimization method and task queue optimization method. Experimental results prove that the two-layer task optimization method model is more feasible and effective than the other two solution models.

A Novel Genetic Artificial Bee Inspired Neural Network Model for Drug Name Recognition

Drug names recognition (DNR) that seeks to discriminate drug mentions within the unstructured biomedical texts and categorize them into drug classes, is an essential task in mining medical information. The neural network structure considerably influences their overall performance. If there were lots of neurons within a network, it might fit the training dataset better, and the network might achieve few training errors, however it could be prone to over-fitting and the poor network generalization. Therefore, this paper proposes a novel network model for DNR based on a hybrid genetic artificial bee colony (GABC) algorithm. The algorithm enhances exploitation process of ABC and avoids the premature convergence problem by combining genetic operators into the ABC. These operators are hybridized in two phases of ABC: (1) exploitation process of onlooker bees phase, to enhance information sharing in between employed and onlooker bees which can find better solutions, and (2) scout bees phase to improve the replacement of any exhausted solution. The proposed network model for DNR was compared to some ether established models using different bio-inspired algorithms. The results reveal that the proposed model outperforms the other models in terms of predictive ability and computational time.

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.

Security in Cognitive Radio Network: Defense against Primary User Emulation attacks using Genetic Artificial Bee Colony (GABC) algorithm

Due to the exponential increase in the number of wireless devices and in data rates demands, the shortage of the free frequency spectrum resources becomes a critical problem. Cognitive Radio Network (CRN) is considered as an emerging technology that eases off the problem of spectrum shortage by optimizing the usage of the available spectrum through giving the unlicensed Secondary Users (SU) the opportunity to exploit the spectrum without causing any interference with the Primary Users’ (PU) usage. Securing the CRN is a crucial problem that must be addressed. Primary User Emulation (PUE) attacks are one of the main threats in deploying CRN, where the Malicious Users mimic the PU signal to confuse the other SUs and corrupt the spectrum sensing process. Many efforts had been made for securing the spectrum sensing process. In this paper, a hybrid Genetic Artificial Bee Colony (GABC) algorithm is proposed to optimize the spectrum utilization by detecting the PUE attacks and enhancing the probability of detection. GABC integrates the Genetic operators with ABC algorithm to reach the balance between exploitation and exploration to find the optimal solution. The simulations results show promising performance of GABC in optimizing spectrum sensing, when compared with recent detection algorithms.

Close loop supply chain network problem with uncertainty in demand and returned products: Genetic artificial bee colony algorithm approach

In recent years, due to environmental concerns, remanufacturing of products is practiced in different companies and closed loop supply chain network in these companies is significant to optimize. Therefore, current study is aimed to determine an optimal closed loop supply chain network, which is composed of multiple producers, remanufacturers, intermediate centers and customer centers. Furthermore, uncertainty in the demand and uncertainty in the quantity of returned products is considered simultaneously in the network to make it significantly useful in the uncertain environment. A novel genetic artificial bee colony (GABC) algorithm is introduced with a new food source representation for the current problem. The proposed GABC algorithm considered neighbor food sources for local search and used crossover and mutation operations of genetic algorithm to enhance the exploration ability of the proposed algorithm. Taguchi method is employed to compute the optimal parameters of GABC for two different size test problems taken from literature and a Case problem which are modified according to the current research problem. The performance of presented GABC algorithm is tested by comparing the results of considered test problems with the results obtained from the original artificial bee colony (ABC) algorithm and genetic algorithm (GA). Moreover, to test the robustness of the proposed GABC algorithm, different scenarios of small and large size problems based on the quantity of demand and variations in demand are made to perform the experiments. Results indicate that proposed GABC outperforms standard ABC and GA in different scenarios to give smaller value of the total cost of network and gives more robust results to give smaller variations in the total cost of network due to uncertain variations in the demand, as compared to original ABC and GA.d

Manufacturing service composition method based on networked collaboration mode

“Cluster Supply Chains” (CSCs) are a new type of business mode, which can help small- and medium-sized enterprises (SMEs) to respond to the changing market demands effectively through networked collaboration. As a large number of manufacturers start to adopt the emerging services paradigm, various cross-enterprise business collaborations in CSC can be deemed as the manufacturing service composition (MSC). However, existing web service composition methods are not appropriate for satisfying the specific demands of manufacturing services composition. The reason lies in two aspects: (1) Current Quality of Service (QoS) model cannot consider the horizontal collaboration between manufacturing services, which may ignore a lot of potential candidate services; (2) current methods are not good at dealing with the rapid increase of candidate service set, which need to be improved further in the searching capability. To solve the above problems, this paper first proposes the new QoS evaluation model of manufacturing service composition according to the networked collaboration pattern in CSC; secondly, the Genetic-Artificial Bee Colony (G-ABC) algorithm is proposed to enhance the performance of searching for the optimal solution based on the available horizontal composite service; finally, the effectiveness of the above solutions is validated through simulation experiments and the results are satisfactory.