Login Register Cart 0
Generic placeholder image

Recent Advances in Electrical & Electronic Engineering

Editor-in-Chief

ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Design and Development of a Data Structure Visualisation System Using the Ant Colony Algorithm

Author(s): Xiaojuan Li, Mudassir Khan and Mohd Dilshad Ansari*

Volume 16, Issue 2, 2023

Published on: 19 September, 2022

Page: [103 - 109] Pages: 7

DOI: 10.2174/2352096515666220617111139

Price: $65

conference banner
Abstract

Aim: A data structure visualisation system uses object-oriented thinking and COM technology to dynamically and interactively simulate and track data structure algorithms and realize the dynamic synchronisation and visualisation of abstract data structures and algorithms using the data modelling function and self-test function.

Background: Teaching data structures and algorithms is difficult because of their abstraction and dynamics; the use of icons in classroom teaching can be partially abstracted into intuition, but analysing the instantaneous dynamic characteristics of the object and the dynamic execution process of the algorithm is difficult.

Objective: A data structure visualisation system employs a data modelling function, visual data structures, a user-friendly and flexible interface, and multipath features for multilevel users. Such systems can be designed more effectively by using the ant colony algorithm.

Methods: The more the ants pass by a certain path, the higher the concentration of residual pheromone, and the higher the probability of the subsequent ant selecting that path. Therefore, the individuals in an ant colony communicate messages and cooperate with each other for foraging.

Results: The resulting speedup ratio indicates that the speedup is smaller when the number of nodes is 100 or more; the acceleration is higher when the node reaches a certain scale, and the speedup ratio does not change considerably. In this study, the traffic simulation software VISSIM was used to generate road network data; the generated traffic data were analysed and used to design a traffic network data structure.

Conclusion: The traffic network data model oriented to the ant colony algorithm was established through abstraction. Accordingly, a parallel ant colony algorithm based on cloud computing was implemented. Finally, a Hadoop cloud computing platform was established and used to run and test the parallel ant colony algorithm program; several experiments were conducted, and the experimental results were analysed concurrently.

Keywords: Data structure, object-oriented, ant colony algorithm, dynamic, commentary, structure.

Next »
[1]
L. Wu, X. Tian, H. Wang, Q. Liu, and W. Xiao, "Improved ant colony optimization algorithm and its application to solve pipe routing design", Assem. Autom., vol. 39, no. 1, pp. 45-57, 2019.
[2]
X. Linhong, and L. Xiangnan, "Computer aided design technology for convex faceted gem cuts based on the half-edge data structure", J. Beijing Institute of Technol., vol. 28, no. 101, pp. 207-219, 2019.
[3]
L. Barion, E. Cisbani, M. Contalbrigo, A.D. Dotto, and Z.W. Zhao, "Rich detectors development for hadron identification at EIC: Design, prototyping and reconstruction algorithm", J. Instrum., vol. 15, no. 02, 2020.
pp. C02040 [http://dx.doi.org/10.1088/1748-0221/15/02/C02040]
[4]
T. George, and V. Ganesan, "An effective technique for tuning the time delay system with pid controller-ant lion optimizer algorithm with ANN technique", Int. J. COMADEM, vol. 23, no. 1, pp. 39-48, 2020.
[5]
A. Dabbagh, and R. Bagherpour, "Development of a match factor and comparison of its applicability with ant-colony algorithm in a heterogeneous transportation fleet in an open-pit mine", J. Min. Sci., vol. 55, no. 1, pp. 45-56, 2019.
[6]
Y. Liang, and L. Wang, "Applying genetic algorithm and ant colony optimization algorithm into marine investigation path planning model", Soft Comput., vol. 24, no. 11, pp. 8199-8210, 2020.
[http://dx.doi.org/10.1007/s00500-019-04414-4]
[7]
L. Chen, M. Liu, Z. Wang, and Z. Dai, "A structure evolution-based design for stable iir digital filters using AMECoDEs algorithm", Soft Comput., vol. 24, no. 7, pp. 5151-5163, 2020.
[http://dx.doi.org/10.1007/s00500-019-04268-w]
[8]
H. Yilmaz, "Note to: A mathematical model and ant colony algorithm for multi-manned assembly line balancing problem", Int. J. Adv. Manuf. Technol., vol. 89, no. 5/8, pp. 1941-1941, 2017.
[http://dx.doi.org/10.1007/s00170-016-9544-x]
[9]
Y. Zeng, X. Zhou, L. Zhang, N. Zhou, and G.E. Sobelman, "A database-driven ant colony algorithm for PLC networking", IEICE Electron. Express, vol. 11, no. 22, pp. 20140957-20140957, 2015.
[http://dx.doi.org/10.1587/elex.11.20140957]
[10]
M. Saravanan, and P.V. Arulkumar, "An artificial bee colony algorithm for design and optimize the fixed area layout problems", Int. J. Adv. Manuf. Technol., vol. 78, no. 9-12, pp. 2079-2095, 2015.
[http://dx.doi.org/10.1007/s00170-014-6774-7]
[11]
B. Xu, H.Q. Min, and F.X. Xiao, "Ant colony algorithm for solving discrete Minimum Constraint Removal (MCR) problem", J. Shanghai Jiaotong Univ., vol. 49, no. 3, pp. 383-386, 2015.
[12]
K. Sundareswaran, V. Vigneshkumar, P. Sankar, S.P. Simon, R.N.P. Srinivasa, and S. Palani, "Development of an improved P&O algorithm assisted through a colony of foraging ants for MPPT in PV system", IEEE Trans. Industr. Inform., vol. 12, no. 1, pp. 187-200, 2016.
[http://dx.doi.org/10.1109/TII.2015.2502428]
[13]
M.M. Rana, L. Li, S.W. Su, and W. Xiang, "Consensus-based smart grid state estimation algorithm", IEEE Trans. Industr. Inform., vol. 14, no. 8, pp. 3368-3375, 2017.
[http://dx.doi.org/10.1109/TII.2017.2782750]
[14]
R. de Azevedo, M.H. Cintuglu, T. Ma, and O.A. Mohammed, "Multiagent-based optimal microgrid control using fully distributed diffusion strategy", IEEE Trans. Smart Grid, vol. 8, no. 4, pp. 1997-2008, 2017.
[http://dx.doi.org/10.1109/TSG.2016.2587741]
[15]
M.M. Rana, L. Li, and S.W. Su, "An adaptive-then-combine dynamic state estimation considering renewable generations in smart grids", IEEE J. Sel. Areas Comm., vol. 34, no. 12, pp. 3954-3961, 2016.
[http://dx.doi.org/10.1109/JSAC.2016.2611963]
[16]
E. Rashid, M.D. Ansari, V.K. Gunjan, and M. Khan, Enhancement in teaching quality methodology by predicting attendance using machine learning technique. Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough., Springer: Cham, 2020, pp. 227-235.
[http://dx.doi.org/10.1007/978-3-030-38445-6_17]
[17]
K. Harijan, M.A. Uqaili, M. Memon, and U.K. Mirza, "Forecasting the diffusion of wind power in Pakistan", Energy, vol. 36, no. 10, pp. 6068-6073, 2011.
[http://dx.doi.org/10.1016/j.energy.2011.08.009]
[18]
M.M. Rana, L. Li, and S.W. Su, "Cyber attack protection and control of microgrids, IEEE/CAA J", Autom. Sin., vol. 5, no. 2, pp. 602-609, 2017.
[19]
V.H. Bui, A. Hussain, and H.M. Kim, "Diffusion strategy-based distributed operation of microgrids using multiagent system", Energies, vol. 10, no. 7, p. 903, 2017.
[http://dx.doi.org/10.3390/en10070903]

Rights & Permissions Print Cite
Article Metrics
1
© 2024 Bentham Science Publishers | Privacy Policy