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Maximum Node Interconnection by a Given Sum of Euclidean Edge Lengths

  • Kim, Joonmo (Department of Computer Engineering, Dankook University) ;
  • Oh, Jaewon (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) ;
  • Kim, Yeonsoo (School of Computer Science and Information Engineering, the Catholic University of Korea) ;
  • Lee, Jeongeun (School of Computer Science and Information Engineering, the Catholic University of Korea) ;
  • Han, Sohee (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 : 2019.09.11
  • Accepted : 2019.11.16
  • Published : 2019.12.31

Abstract

This paper proposes a solution to the problem of finding a subgraph for a given instance of many terminals on a Euclidean plane. The subgraph is a tree, whose nodes represent the chosen terminals from the problem instance, and whose edges are line segments that connect two corresponding terminals. The tree is required to have the maximum number of nodes while the length is limited and is not sufficient to interconnect all the given terminals. The problem is shown to be NP-hard, and therefore a genetic algorithm is designed as an efficient practical approach. The method is suitable to various probable applications in layout optimization in areas such as communication network construction, industrial construction, and a variety of machine and electronics design problems. The proposed heuristic can be used as a general-purpose practical solver to reduce industrial costs by determining feasible interconnections among many types of components over different types of physical planes.

Keywords

References

  1. K. Zhou and J. Chen, "Simulation DNA algorithm of set covering problem," Applied Mathematics & Information Sciences, vol. 8, pp. 139-144, Jan. 2014. DOI: http://dx.doi.org/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, pp. 474-477, 2015. DOI: https://doi.org/10.1109/icicta.2015.124.
  3. C. Lin, "A heuristic genetic algorithm based on schema replacement for 0-1 knapsack problem," in Proceeding of International Conference on Genetic and Evolutionary Computing, Shenzhen, pp. 301-304, 2010. DOI: https://doi.org/10.1109/icgec.2010.81.
  4. R. P. Singh, "Solving 0-1 knapsack problem using genetic algorithms," in Proceeding of IEEE International Conference on Communication Software and Networks, Xi'an, pp. 591-595, 2011. DOI: https://doi.org/10.1109/iccsn.2011.6013975.
  5. F. B. Ozsoydan and A. Baykasoglu, "A swarm intelligence-based algorithm for the set-union knapsack problem," Future Generation Computer Systems, vol. 93, pp. 560-569, 2019. DOI: https://doi.org/10.1016/j.future.2018.08.002.
  6. 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, 2019. DOI: https://doi.org/10.1016/j.ejor.2019.01.002.
  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, 2018. DOI: https://doi.org/10.1016/j.ejor.2017.08.015.
  8. K. Singh and S. Sundar, "A hybrid genetic algorithm for the degreeconstrained minimum spanning tree problem," Soft Computing, pp. 1-18, 2019. DOI: https://doi.org/10.1007/s00500-019-04051-x.
  9. 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, pp. 62-67, 2012. DOI: https://doi.org/10.1109/taai.2012.21.
  10. 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, pp. 1732-1734, 2006. DOI: https://doi.org/10.1109/icccas.2006.285008.
  11. T. N. Bui and B. R. Moon, "Genetic algorithm and graph partitioning," IEEE Transactions on Computers, vol. 45, no. 7, pp. 841-855, 1996. DOI: https://doi.org/10.1109/icmwi.2010.5648133.
  12. B. R. Moon, Easy To Learn Genetic Algorithm: Evolutionary Approach, Hanbit Media, 2008.
  13. 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, pp. 101-104, 2009. DOI: https://doi.org/10.1109/wgec.2009.127.
  14. 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, pp. 516-519, 2009. DOI: https://doi.org/10.1109/icasid.2009.527691.