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Tumbleweed Algorithm and Its Application for Solving Location Problem of Logistics Distribution Center

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Genetic and Evolutionary Computing (ICGEC 2021)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 833))

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Abstract

In this paper, a new swarm intelligence optimization algorithm named Tumbleweed Algorithm (TA) is proposed. The TA algorithm simulates the two processes of tumbleweed from seedling to adulthood and the propagation of tumbleweed seeds after adulthood. And by introducing the concept of growth cycle, the two stages are combined. In order to verify the effectiveness of the new algorithm proposed to solve the problems, this paper uses the CEC2013 function set to test, and compares the 10D, 30D and 50D dimensions with six swarm intelligence optimization algorithms. By comparing the experimental results under different dimensions, the TA algorithm proposed in this paper is generally superior to other intelligent optimization algorithms compared, and has strong optimization ability and competitiveness. Finally, the TA algorithm is applied to the location problem of logistics distribution center to verify the practicability of the algorithm. In solving this problem, the TA algorithm can also obtain better optimization results.

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References

  1. Pan, J.S., Meng, Z., Chu, S.C., Xu, H.R.: Monkey king evolution: an enhanced ebb-tide-fish algorithm for global optimization and its application in vehicle navigation under wireless sensor network environment. Telecommun. Syst. 65(3), 351–364 (2017)

    Article  Google Scholar 

  2. Xue, X., Zhang, J.: Matching large-scale biomedical ontologies with central concept based partitioning algorithm and adaptive compact evolutionary algorithm. Appl. Soft Comput. 106, 107343 (2021)

    Google Scholar 

  3. Wang, X.D., Chen, R.C., Yan, F.: High-dimensional data clustering using k-means subspace feature selection. J. Network Intell. 4(3), 80–87 (2019)

    Google Scholar 

  4. Tu, T.N.: A fuzzy approach of large size remote sensing image clustering. J. Inf. Hiding Multimedia Signal Process. 11(4), 187–198 (2020)

    Google Scholar 

  5. Chai, Q.W., Chu, S.C., Pan, J.S., Zheng, W.M.: Applying adaptive and self assessment fish migration optimization on localization of wireless sensor network on 3-D Terrain. J. Inf. Hiding Multimedia Signal Process. 11(2), 90–102 (2020)

    Google Scholar 

  6. Pan, J.S., Wang, X., Chu, S.C., Nguyen, T.: A multi-group grasshopper optimisation algorithm for application in capacitated vehicle routing problem. Data Sci. Patt. Recogn. 4(1), 41–56 (2020)

    Google Scholar 

  7. Xu, X.W., Pan, T.S., Song, P.C., Hu, C.C., Chu, S.C.: Multi-cluster based equilibrium optimizer algorithm with compact approach for power system network. J. Netw. Intell. 6(1), 117–142 (2021)

    Google Scholar 

  8. Huang, H.C., Chu, S.C., Pan, J.S., Huang, C.Y., Liao, B.Y.: Tabu search based multi-watermarks embedding algorithm with multiple description coding. Inf. Sci. 181(16), 3379–3396 (2011)

    Article  Google Scholar 

  9. Gao, M., Pan, J.S., Li, J.P., Zhang, Z.P., Chai, Q.W.: 3-D Terrains deployment of wireless sensors network by utilizing parallel gases Brownian motion optimization. J. Internet Technol. 22(1), 13–29 (2021)

    Google Scholar 

  10. Pan, J.S., Chu, S.C., Roddick, J., Dao, T.K., et al.: Optimization localization in wireless sensor network based on multi-objective firefly algorithm. J. Netw. Intell. 1(4), 130–138 (2016)

    Google Scholar 

  11. Kou, X., Feng, J.: Matching ontologies through compact monarch butterfly algorithm. J. Netw. Intell. 5(4), 191–197 (2020)

    Google Scholar 

  12. Chu, S.C., Huang, H.C., Roddick, J.F., Pan, J.S.: Overview of algorithms for swarm intelligence. In: International Conference on Computational Collective Intelligence, pp. 28–41. Springer (2011). https://doi.org/10.1007/978-3-642-23935-9_3

  13. Holland, J.H.: Genetic algorithms. Sci. Am. 267(1), 66–73 (1992)

    Article  Google Scholar 

  14. Pan, J.S., Kong, L., Sung, T.W., Tsai, P.W., Snášel, V.: A clustering scheme for wireless sensor networks based on genetic algorithm and dominating set. J. Internet Technol. 19(4), 1111–1118 (2018)

    Google Scholar 

  15. Xue, X., Wu, X., Chen, J.: Optimizing ontology alignment through an interactive compact genetic algorithm. ACM Trans. Manage. Inf. Syst. (TMIS) 12(2), 1–17 (2021)

    Article  Google Scholar 

  16. Wang, J., Pan, B., Wang, Q.R., Ding, Q.: A chaotic key expansion algorithm based on genetic algorithm. J. Inf. Hiding Multimedia Signal Process. 10(2), 289–299 (2019)

    Google Scholar 

  17. Qin, A.K., Huang, V.L., Suganthan, P.N.: Differential evolution algorithm with strategy adaptation for global numerical optimization. IEEE Trans. Evolut. Comput. 13(2), 398–417 (2008)

    Article  Google Scholar 

  18. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983)

    Article  MathSciNet  Google Scholar 

  19. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995-International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE (1995)

    Google Scholar 

  20. Wu, J., Xu, M., Liu, F.F., Huang, M., Ma, L., Lu, Z.M.: Solar wireless sensor network routing algorithm based on multi-objective particle swarm optimization. J. Inf. Hiding Multimedia Signal Process. 12(1), 1–11 (2021)

    Google Scholar 

  21. Mirjalili, S., Mirjalili, S.M., Lewis, A.: Grey wolf optimizer. Adv. Eng. Software 69, 46–61 (2014)

    Article  Google Scholar 

  22. Yang, X.S., Karamanoglu, M., He, X.: Flower pollination algorithm: a novel approach for multiobjective optimization. Eng. Optimization 46(9), 1222–1237 (2014)

    Article  MathSciNet  Google Scholar 

  23. Pan, J.S., Zhuang, J., Luo, H., Chu, S.C.: Multi-group flower pollination algorithm based on novel communication strategies. J. Internet Technol. 22(2), 257–269 (2021)

    Google Scholar 

  24. Meng, Z., Pan, J.S.: QUasi-affine transformation evolution with external archive (QUATRE-EAR): an enhanced structure for differential evolution. Knowl.-Based Syst. 155, 35–53 (2018)

    Article  Google Scholar 

  25. Liu, N., Pan, J.S., Chu, S.C.: A competitive learning quasi affine transformation evolutionary for global optimization and its application in CVRP. J. Internet Technol. 21(7), 1863–1883 (2020)

    Google Scholar 

  26. Karaboga, D., Basturk, B.: On the performance of artificial bee colony (ABC) algorithm. Appl. Soft Comput. 8(1), 687–697 (2008)

    Article  Google Scholar 

  27. Xiao, S., Wang, H., Wang, W., Huang, Z., Zhou, X., Xu, M.: Artificial bee colony algorithm based on adaptive neighborhood search and Gaussian perturbation. Appl. Soft Comput. 100, 106955 (2021)

    Google Scholar 

  28. Wang, H., Wang, W., Xiao, S., Cui, Z., Xu, M., Zhou, X.: Improving artificial bee colony algorithm using a new neighborhood selection mechanism. Inf. Sci. 527, 227–240 (2020)

    Article  MathSciNet  Google Scholar 

  29. Pammel, L.: Botany of Russian thistle. Bulletin 3(26), 3 (2017)

    Google Scholar 

  30. Liang, J., Qu, B., Suganthan, P., Hernández-Díaz, A.G.: Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore, Technical Report, vol. 201212(34), pp. 281–295 (2013)

    Google Scholar 

  31. Mirjalili, S.: SCA: a sine cosine algorithm for solving optimization problems. Knowl.-Based Syst. 96, 120–133 (2016)

    Article  Google Scholar 

  32. Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Software 95, 51–67 (2016)

    Article  Google Scholar 

  33. Chu, S.C., Tsai, P.W., Pan, J.S.: Cat swarm optimization. In: Pacific Rim international Conference on Artificial Intelligence, pp. 854–858. Springer (2006). https://doi.org/10.1007/978-3-540-36668-3_94

  34. Yang, X.S., He, X.: Bat algorithm: literature review and applications. Int. J. Bio-inspired Comput. 5(3), 141–149 (2013)

    Article  Google Scholar 

  35. Arora, S., Singh, S.: Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput. 23(3), 715–734 (2019)

    Article  Google Scholar 

  36. Kayikci, Y.: A conceptual model for intermodal freight logistics centre location decisions. Procedia-Soc. Behav. Sci. 2(3), 6297–6311 (2010)

    Article  Google Scholar 

  37. Nathan, R., et al.: Mechanisms of long-distance dispersal of seeds by wind. Nature 418(6896), 409–413 (2002)

    Article  Google Scholar 

  38. Bensassi, S., Márquez-Ramos, L., Martínez-Zarzoso, I., Suárez-Burguet, C.: Relationship between logistics infrastructure and trade: evidence from Spanish regional exports. Transp. Res. Part A: Policy Pract. 72, 47–61 (2015)

    Google Scholar 

  39. Gammelgaard, B., et al.: The emergence of city logistics: the case of Copenhagen’s Citylogistik-kbh. Int. J. Phys. Distrib. Logist. Manage. 45(4), 333–351 (2015). https://doi.org/10.1108/IJPDLM-12-2014-0291

  40. Hu, W.: An improved flower pollination algorithm for optimization of intelligent logistics distribution center. Adv. Prod. Eng. Manage. 14(2), 177–188 (2019)

    Google Scholar 

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Author information

Authors and Affiliations

  1. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    Qing-yong Yang, Shu-Chuan Chu & Jeng-Shyang Pan

  2. College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    Anhui Liang

Authors
  1. Qing-yong Yang
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  2. Shu-Chuan Chu
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  3. Anhui Liang
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  4. Jeng-Shyang Pan
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Editor information

Editors and Affiliations

  1. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong, China

    Shu-Chuan Chu

  2. Western Norway University of Applied Sciences, Bergen, Norway

    Jerry Chun-Wei Lin

  3. Northeast Electric Power University, Jilin City, Jilin, China

    Jianpo Li

  4. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, Shandong, China

    Jeng-Shyang Pan

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Yang, Qy., Chu, SC., Liang, A., Pan, JS. (2022). Tumbleweed Algorithm and Its Application for Solving Location Problem of Logistics Distribution Center. In: Chu, SC., Lin, J.CW., Li, J., Pan, JS. (eds) Genetic and Evolutionary Computing. ICGEC 2021. Lecture Notes in Electrical Engineering, vol 833. Springer, Singapore. https://doi.org/10.1007/978-981-16-8430-2_58

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