scholarly journals Minimizing Cost of Assembly of an Interrelated Dimensional Chain Product Using ABC Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-23
Author(s):  
Mahalingam Sivakumar ◽  
Nagarajan Lenin ◽  
Kandasamy Jayakrishna ◽  
Natarajan Eswara Prasath
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Optimization Techniques ◽  
Manufacturing Cost ◽  
Selective Assembly ◽  
Equal Group ◽  
Bee Colony ◽  
Linear Assembly ◽  
Group Number ◽  
Surplus Parts ◽  
Made In

Selective assembly is a method where components made with wider tolerance are grouped into a number of bins. Based on the best combination of the bin, the corresponding group components are randomly selected and matched together to make an assembly. Existing techniques focused on equal group number partitioning of components, equal probability, equal group width, and equal area methods to minimize either clearance variation or surplus parts using different optimization techniques. Mostly, simple assemblies with two or three components are worked by various authors in the literature without considering their original dimension by considering only their component’s tolerance. In the present work, components are classified into different unequal group numbers based on their tolerance values. The interrelated dimensional assemblies are made in a single stage by matching the parts based on the best bin combination obtained by the artificial bee colony algorithm. A simple linear assembly and a three-armed knuckle joint assembly are considered examples of problems to demonstrate the effectiveness of the proposed method by minimizing the manufacturing cost.

A Communication Model Framework for Electric Transmission Line Monitoring Using Artificial Bee Colony Algorithm

2019 ◽  
Vol 29 (04) ◽  
pp. 2050063
Author(s):  
C. Jeyanthi ◽  
H. Habeebullah Sait ◽  
K. Chandrasekaran ◽  
C. Christopher Columbus
Keyword(s):  
Transmission Line ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Voltage Drop ◽  
Test System ◽  
Optimization Techniques ◽  
Cellular Communication ◽  
Wireless Sensor ◽  
Model Framework ◽  
Bee Colony

The natural calamity and physical malfunction in the overhead transmission line cause bad impact on the networks such as mechanical failures, power losses, reduction of line capacity, and voltage drop. These adverse impacts can be reduced by implementing proper monitoring systems. Wireless sensor network is an apt mechanism to monitor the overhead transmission network because of its physical configuration. This paper portrays the communication between wireless sensor networks and central data processing station. Cellular communication can directly transmit information through an assisted cellular module (CM) based on the probability of cellular coverage. In this paper, one of the modern optimization techniques, i.e., artificial bee colony algorithm, is used to study the problem of CM placement of cellular communication. By using this algorithm, the optimal number and location of the CMs for a test system varied from 10 to 100 are determined. A novel optimal link path scheme is proposed to check the condition of the required quality of services of both cellular/ZigBee users. The attained results show that the methodology is best suited to acquire low cost solution for the cellular module placement problem.

Tolerance allocation of complex assembly with nominal dimension selection using Artificial Bee Colony algorithm

2018 ◽  
Vol 233 (1) ◽  
pp. 18-38 ◽  
Author(s):  
D Vignesh Kumar ◽  
D Ravindran ◽  
N Lenin ◽  
M Siva Kumar
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Vital Role ◽  
Tolerance Allocation ◽  
Manufacturing Cost ◽  
Mechanical Assembly ◽  
Complex Assembly ◽  
Bee Colony ◽  
Discrete Values ◽  
Nominal Dimension

Optimum tolerance allocation plays a vital role in minimizing the direct manufacturing cost of mechanical assembly. It is very sensitive due to the variations in manufacturing processes of the components. Most of the earlier studies are aiming at optimum tolerance allocation for assemblies without considering the selection of nominal dimensions of components and considering them as discrete values. It is proposed to minimize the manufacturing cost of an assembly with tolerance allocation and alternate nominal dimension selections by considering them in closer decimal intervals. The evolutionary algorithms such as Genetic and Artificial Bee Colony algorithms are developed and proposed to achieve the above objectives. The performance of the algorithms has been enhanced with the seed solution obtained using Lagrange Multiplier method. The complex assembly problems proposed by various authors with the required parameters have been considered for investigating the proposed method. The critical dimensions of the assemblies are fixed and the nominal dimension has been varied with its tolerances. The resultant manufacturing cost by various methods is presented and compared with corresponding nominal dimensions and tolerances. Based on the percentage of improvement of manufacturing cost, it is observed that the Artificial Bee Colony algorithm outperforms.

Self-Adaptive and Adaptive Parameter Control in Improved Artificial Bee Colony Algorithm

Informatica ◽  
2017 ◽  
Vol 28 (3) ◽  
pp. 415-438 ◽  
Author(s):  
Bekir Afşar ◽  
Doğan Aydin ◽  
Aybars Uğur ◽  
Serdar Korukoğlu
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Parameter Control ◽  
Adaptive Parameter ◽  
Bee Colony ◽  
Adaptive Parameter Control ◽  
Self Adaptive

A new Artificial Bee Colony algorithm for Set Covering Problems

2014 ◽  
Author(s):  
Broderick Crawford ◽  
Ricardo Soto ◽  
Rodrigo Cuesta Aguilar ◽  
Fernando Paredes
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Set Covering ◽  
Covering Problems ◽  
Set Covering Problems ◽  
Bee Colony

Network intrusion detection by artificial bee colony algorithm-based SVM

2014 ◽  
Author(s):  
Xi Zhen-yuan ◽  
Chen He ◽  
Gao Xiao-yan ◽  
Chen Lu-yi
Keyword(s):  
Intrusion Detection ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Network Intrusion Detection ◽  
Bee Colony ◽  
Network Intrusion

Path Planning of Mobile Robot Using Improved Artificial Bee Colony Algorithm

2020 ◽  
Vol 38 (9A) ◽  
pp. 1384-1395
Author(s):  
Rakaa T. Kamil ◽  
Mohamed J. Mohamed ◽  
Bashra K. Oleiwi
Keyword(s):  
Mobile Robot ◽  
Comparative Research ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Dynamic Control ◽  
Computational Time ◽  
Bee Colony ◽  
Shortest Distance ◽  
Minimum Number ◽  
Cubic Polynomial

A modified version of the artificial Bee Colony Algorithm (ABC) was suggested namely Adaptive Dimension Limit- Artificial Bee Colony Algorithm (ADL-ABC). To determine the optimum global path for mobile robot that satisfies the chosen criteria for shortest distance and collision–free with circular shaped static obstacles on robot environment. The cubic polynomial connects the start point to the end point through three via points used, so the generated paths are smooth and achievable by the robot. Two case studies (or scenarios) are presented in this task and comparative research (or study) is adopted between two algorithm’s results in order to evaluate the performance of the suggested algorithm. The results of the simulation showed that modified parameter (dynamic control limit) is avoiding static number of limit which excludes unnecessary Iteration, so it can find solution with minimum number of iterations and less computational time. From tables of result if there is an equal distance along the path such as in case A (14.490, 14.459) unit, there will be a reduction in time approximately to halve at percentage 5%.

Artificial bee colony algorithm with modified search strategy

2013 ◽  
Vol 32 (12) ◽  
pp. 3326-3330
Author(s):  
Yin-xue ZHANG ◽  
Xue-min TIAN ◽  
Yu-ping CAO
Keyword(s):  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Search Strategy ◽  
Bee Colony

ABC-Gly: identifying protein lysine glycation sites with artificial bee colony algorithm

2019 ◽  
Vol 17 ◽  
Author(s):  
Yanqiu Yao ◽  
Xiaosa Zhao ◽  
Qiao Ning ◽  
Junping Zhou
Keyword(s):  
Support Vector Machine ◽  
Amino Acid ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Training Dataset ◽  
Support Vector ◽  
Supplementary File ◽  
Feature Subset ◽  
Lipid Molecule ◽  
Bee Colony

Background: Glycation is a nonenzymatic post-translational modification process by attaching a sugar molecule to a protein or lipid molecule. It may impair the function and change the characteristic of the proteins which may lead to some metabolic diseases. In order to understand the underlying molecular mechanisms of glycation, computational prediction methods have been developed because of their convenience and high speed. However, a more effective computational tool is still a challenging task in computational biology. Methods: In this study, we showed an accurate identification tool named ABC-Gly for predicting lysine glycation sites. At first, we utilized three informative features, including position-specific amino acid propensity, secondary structure and the composition of k-spaced amino acid pairs to encode the peptides. Moreover, to sufficiently exploit discriminative features thus can improve the prediction and generalization ability of the model, we developed a two-step feature selection, which combined the Fisher score and an improved binary artificial bee colony algorithm based on support vector machine. Finally, based on the optimal feature subset, we constructed the effective model by using Support Vector Machine on the training dataset. Results: The performance of the proposed predictor ABC-Gly was measured with the sensitivity of 76.43%, the specificity of 91.10%, the balanced accuracy of 83.76%, the area under the receiver-operating characteristic curve (AUC) of 0.9313, a Matthew’s Correlation Coefficient (MCC) of 0.6861 by 10-fold cross-validation on training dataset, and a balanced accuracy of 59.05% on independent dataset. Compared to the state-of-the-art predictors on the training dataset, the proposed predictor achieved significant improvement in the AUC of 0.156 and MCC of 0.336. Conclusion: The detailed analysis results indicated that our predictor may serve as a powerful complementary tool to other existing methods for predicting protein lysine glycation. The source code and datasets of the ABC-Gly were provided in the Supplementary File 1.

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