Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Development of Fuzzy Logic Ant Colony Optimization Algorithm for Multivariate Traveling Salesman Problem

다변수 순회 판매원 문제를 위한 퍼지 로직 개미집단 최적화 알고리즘

  • Byeong-Gil Lee (Graduate School of Consulting, Kumoh National Institute of Technology) ;
  • Kyubeom Jeon (School of Industrial Engineering, Kumoh National Institute of Technology) ;
  • Jonghwan Lee (School of Industrial Engineering, Kumoh National Institute of Technology)
  • 이병길 (금오공과대학교 컨설팅대학원) ;
  • 전규범 (금오공과대학교 산업공학부) ;
  • 이종환 (금오공과대학교 산업공학부)
  • Received : 2022.11.21
  • Accepted : 2022.12.27
  • Published : 2023.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

An Ant Colony Optimization Algorithm(ACO) is one of the frequently used algorithms to solve the Traveling Salesman Problem(TSP). Since the ACO searches for the optimal value by updating the pheromone, it is difficult to consider the distance between the nodes and other variables other than the amount of the pheromone. In this study, fuzzy logic is added to ACO, which can help in making decision with multiple variables. The improved algorithm improves computation complexity and increases computation time when other variables besides distance and pheromone are added. Therefore, using the algorithm improved by the fuzzy logic, it is possible to solve TSP with many variables accurately and quickly. Existing ACO have been applied only to pheromone as a criterion for decision making, and other variables are excluded. However, when applying the fuzzy logic, it is possible to apply the algorithm to various situations because it is easy to judge which way is safe and fast by not only searching for the road but also adding other variables such as accident risk and road congestion. Adding a variable to an existing algorithm, it takes a long time to calculate each corresponding variable. However, when the improved algorithm is used, the result of calculating the fuzzy logic reduces the computation time to obtain the optimum value.

Keywords

Acknowledgement

This paper was supported by Kumoh National Institute of Technology

References

  1. Choe, I.C., Ha, S.H., Kim, S.J., and Jeon, H.T., A study on intelligent path searching and guide using RFID and fuzzy logic, Journal of Korean Institute of Intelligent Systems, 2009, Vol. 19, No. 1, pp. 139-144. https://doi.org/10.5391/JKIIS.2009.19.1.139
  2. Choi, W.K. and Jeon, H.T., Intelligent path guide system using fuzzy logic, The Institute of Electronics Engineers of Korea - Computer and Information, 2008, Vol. 45, No. 3, pp. 68-74.
  3. Colorni, A., Dorigo, M., and Maniezzo, V., Distributed Optimization by Ant Colonies, actes de la premiere conference europeenne sur la vie artificielle, pp. 134-142, 1991.
  4. Hong, S.M., Lee, Y.A., and Chung, T.C., Efficient path search method using ant colony system in traveling salesman problem, Journal of KISS: Software and Application, 2003, Vol. 30, No. 9.10, pp. 862-866.
  5. Jang, D.S., Cho, S.J., Tahk, M.J., Koo, H.J., and Kim, J.S., Fuzzy logic based collision avoidance for UAVs, Journal of the Korean Society for Aeronautical & Space Sciences, 2006, Vol. 34, No. 7, pp. 55-62. https://doi.org/10.5139/JKSAS.2006.34.7.055
  6. Jin, K.S., Ahn, H.G., and Yoon, T.S., An improved map construction for mobile robot using fuzzy logic and genetic algorithm, Journal of Korean Institute of Intelligent Systems, 2005, Vol. 15, No. 3, pp. 330-336. https://doi.org/10.5391/JKIIS.2005.15.3.330
  7. Kim, J.S., Jeong, J.Y., and Lee, J.H., Optimizing work-in-process parameter using genetic algorithm, Journal of Society of Korea Industrial and Systems Engineering, 2017, Vol. 40, No. 1, pp. 79-86. https://doi.org/10.11627/jkise.2017.40.1.079
  8. Kim, S.J., Lee, G.S., and Choi, H.M., An efficient approach for the solution of traveling salesman problems based on self-organizing feature maps, IEEK Summer Conference, 1992, Vol. 15, No 1, pp. 722-726.
  9. Kim, Y.H., Kim, K.S., and Kwak, S.Y., A pedestrian collision warning system using a fuzzy logic, Journal of Broadcast Engineering, 2015, Vol. 20, No. 3, pp. 440-448. https://doi.org/10.5909/JBE.2015.20.3.440
  10. Lee, H.K. and Suh, S.M., A heuristic method for max(x, y), Journal of the Korean Institute of Industrial Engineers, 1993, Vol. 19, No. 3, pp. 37-49.
  11. Lee, K.K., Han, S.K., and Lee, S.W., A genetic algorithm for the traveling salesman problem, Journal of the Korea Information Science Society, 1995, Vol. 22, No. 4, pp. 559-566.
  12. Lee, S.G. and Choi, J.H., Balance between intensification and diversification in ant colony optimization, Journal of The Korea Contents Association, 2011, Vol. 11, No. 3, pp. 100-107. https://doi.org/10.5392/JKCA.2011.11.3.100
  13. Lee, S.G. and Kang, M.J., Ant colony system for solving the traveling salesman problem considering the overlapping edge of global best path, Journal of the Korea Society of Computer and Information, 2011, Vol. 16, No. 3, pp. 203-210. https://doi.org/10.9708/jksci.2011.16.3.203
  14. Lee, S.H. and Kim, Y.D., Ant colony system for vehicle routing problem with simultaneous delivery and pick-up under time windows, Journal of the Korean Institute of Industrial Engineers, 2009, Vol. 35, No. 2, pp. 160-170.
  15. Lee, S.J. and Han, Y.D., Truss size optimization using ant colony optimization algorithm, Journal of The Architectural Institute of Korea Structure & Construction, 2011, Vol. 27, No. 8, pp. 21-28.
  16. Noh, T.W., Oh, H.Y., and Kim, C.K., An light-weight genetic algorithm based scheme for traveling salesman problem, Journal of Korea Information Science Society, 2014, Vol. 41, No. 1, pp. 1222-1224.
  17. Park, M.C. and Han, S.Y., Shape optimization using the ant colony optimization algorithm, Journal of The Korean Society of Manufacturing Technology Engineers, 2014, pp. 100-100.
  18. Park, S.W., Park, J.W., Kim, D.W., Ohm, W.Y., Lee, C.H., and Ahn, C.H., A study on the synergy effect between neuro-fuzzy and genetic algorithms for non-linear system modeling, The Journal of Korean Institute of Information Technology, 2013, Vol. 11, No. 1, pp. 9-17. https://doi.org/10.14801/kiitr.2013.11.1.9