Journal of information and communication convergence engineering

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

Maximum Node Interconnection by a Given Sum of Euclidean Edge Lengths in a Cluster Node Distribution

  • Kim, Yeonsoo (School of Computer Science and Information Engineering, The Catholic University of Korea) ;
  • Kim, Minkwon (School of Computer Science and Information Engineering, The Catholic University of Korea) ;
  • Hwang, Byungyeon (School of Computer Science and Information Engineering, The Catholic University of Korea)
  • Received : 2021.07.06
  • Accepted : 2022.05.20
  • Published : 2022.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

This paper proposes a method to find a tree with the maximum number of terminals that can be connected by a given length when numerous terminals distributed in a cluster form are given to the Euclidean plane R2 with several constraints. First constraint is that a given terminal is distributed in a cluster form, second is that a given length cannot connect all terminals in the tree, and third is that there is no curved connection between each terminal. This paper proposes a method to establish more efficient interconnections within terminals distributed in a cluster form by improving a randomly distributed memetic genetic algorithm. The construction of interconnections has been extensively used in design-related fields, from networking to architecture. Additionally, in real life, the construction of interconnections is mostly distributed in the form of clusters. Therefore, the heuristic algorithm proposed in this paper can be effectively utilized in real life and is expected to provide various cost savings.

Keywords

Acknowledgement

This work was supported by the Catholic University of Korea Research Fund (2020).

References

  1. K. Zhou and J. Chen, "Simulation DNA algorithm of set covering problem," Applied Mathematics & Information Sciences, vol. 8, no. 1, pp. 139-144, Jan. 2014. DOI: 10.12785/amis/080117.
  2. D. Hu, P. Dai, K. Zhou, and S. Ge, "Improved particle swarm optimization for minimum spanning tree of length constraint problem," in Proceeding of International Conference on Intelligent Computation Technology and Automation, Nanchang, China, pp. 474-477, 2015. DOI: 10.1109/ICICTA.2015.124.
  3. J. Kim, J. Oh, M. Kim, Y. Kim, J. Lee, S. Han, and B. Hwang, "Maximum node interconnection by a given sum of euclidean edge lengths," Journal of Information and Communication Convergence Engineering, vol. 17, no. 4, pp. 246-254, Dec. 2019. DOI: 10.6109/jicce.2019.17.4.246.
  4. B. R. Moon, Easy to Learn Genetic Algorithm: Evolutionary Approach, Hanbit Media, Seoul, South Korea, 2008.
  5. K. Singh and S. Sundar, "A hybrid steady-state genetic algorithm for the min-degree constrained minimum spanning tree problem," European Journal of Operational Research, vol. 276, no. 1, pp. 88-105, Jul. 2019. DOI: 10.1016/j.ejor.2019.01.002.
  6. K. Singh and S. Sundar, "A hybrid genetic algorithm for the degree constrained minimum spanning tree problem," Soft Computing, vol. 24, pp. 2169-2186, May. 2019. DOI: 10.1007/s00500-019-04051-x.
  7. P. C. Pop, O. Matei, C. Sabo, and A. Petrovan, "A two-level solution approach for solving the generalized minimum spanning tree problem," European Journal of Operational Research, vol. 265, no. 2, pp. 478-487, Mar. 2018. DOI: 10.1016/j.ejor.2017.08.015.
  8. L. Zhang, H. Chang, and R. Xu, "Equal-width partitioning roulette wheel selection in genetic algorithm," in Proceeding of International Conference on Technologies and Applications of Artificial Intelligence, Tainan, Taiwan, pp. 62-67, 2012. DOI: 10.1109/taai.2012.21.
  9. Y. Zou, Z. Mi, and M. Xu, "Dynamic load balancing based on roulette wheel selection," in Proceeding of International Conference on Communications, Circuits and Systems, Guilin, Chin, pp. 1732-1734, 2006. DOI: 10.1109/icccas.2006.285008.
  10. T. N. Bui and B. R. Moon, "Genetic algorithm and graph partitioning," IEEE Transactions on Computers, vol. 45, no. 7, pp. 841-855, Jul. 1996. DOI: 10.1109/icmwi.2010.5648133.
  11. W. Youping, L. Liang, and C. Lin, "An advanced genetic algorithm for traveling salesman problem," in Proceeding of International Conference on Genetic and Evolutionary Computing, Guilin, China, pp. 101-104, 2009. DOI: 10.1109/wgec.2009.127.
  12. G. Wei and X. Xie, "Research of using genetic algorithm of improvement to compute the most short path," in Proceeding of International Conference on Anti-counterfeiting, Security, and Identification in Communication, Hong Kong, China, pp. 516-519, 2009. DOI: 10.1109/icasid.2009.5276990.
  13. J. T. Tou and R. C. Gonzalez, Pattern Recognition Principles, Addison-Wesley Publishing Company, MA: Boston, USA, pp. 75-109, 1974.