COMPARISON OF INTELLIGENT ALGORITHMS OF NEW RETAIL DISTRIBUTION CENTER

Authors

  • Shihua Luo School of Business, Guangdong University of Foreign Studies, Guangzhou 510420, China
  • Zuchang Zhong (Corresponding Author) School of Business, Guangdong University of Foreign Studies, Guangzhou 510420, China

Keywords:

New Retail concept, New Retail distribution center, Chaotic-enhanced Fruit Fly Optimization Algorithm, Particle Swarm Optimization, intelligent algorithms.

Abstract

The relationship between the number and size of retail stores and the optimization ability of intelligent algorithms is analyzed by experiments. The location coordinates of retail stores are randomly generated by intelligent algorithm, and the location model with revenue as objective function is constructed. The numerical examples of chaotic-enhanced fruit fly optimization algorithm and particle swarm optimization algorithm are designed. The results show that under the same constraints, with the increase of the number of retail stores, chaotic-enhanced fruit fly optimization algorithm and particle swarm optimization algorithm show different optimization capabilities. That is to say, the relationship between the number and size of retail stores and the optimization effect of intelligent algorithms is not stable: when the number of retail stores is small or medium-sized, theparticle swarm optimization algorithmhas strong optimization ability; but when the number of retail stores continues to increase sharply, the chaotic-enhanced fruit fly optimization algorithm displays its prominent optimization ability.

References

[1] 1.Ya, P. (2017, July 13). Yang Lixiang: The way for Zhang He Tian Xia brand collection store. The China National Radio. Retrieved from https://baijiahao.baidu.com/s?id=1572741581971115&wfr=spider&for=pc,2017-07-13. 2.Han.C.Z., & Wang. B.Y. (2018).The theoretical situation of“New Retail”and its extension[J].China Business and Market, 32(12), 20-30. 3.Pan,W.T.(2012). A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowledge-Based Systems, 26(none), 69-74. 4.Zhang, Q., & Liu L.J. (2018).Dynamic grouping and multi-strategy fruit fly optimization algorithm[J].Information and Control, 4(47), 470-485. 5.Wan, X.F., Xi, R.X., Hu, H.L., & Wan,X.L. (2018). Research on multi- objective optimization control of grid-connected inverter under unbalanced grid voltage based on fruit fly algorithm[J].Power System Technology, 42(05), 1628-1635. 6.Duan, Y.M., Xiao, H.H., & Tan, Q.L. (2017). Fruit fly optimization algorithm based on simulated annealing mechanism[J].Control Engineering of China, 24(05), 938-946. 7.Yang, F., Wang, X.B., & Shao, Y. (2018). Deformation prediction of grey neural network based on modified fruit fly algorithm[J].Science of Surveying and Mapping, 43(02), 63-69. 8.Gui, L., Ai, P., & Ding, G.S. (2018). Improved fruit fly optimization algorithm with changing step and strategy[J].Computer Engineering and Applications,54(04),148-153+184. 9.Zhang, X.P., Chen, Y., & Ding, X.J. (2018). Dynamic search and cooperative learning for fruit fly optimization algorithm[J].Journal of Chinese Computer Systems, 39(01), 48-52. 10.Wang, Y.W., Feng, L.Z., Zhu, J.M., Chai, Y.M., & Wu, Y. (2018).An improved fruit fly optimization algorithm based on global-local bidirectional driving[J].Journal of Harbin Institute of Technology, 50(05), 93-101. 11.Han, J.Y., & Liu, C.Z. (2013). Adaptive chaos fruit fly optimization algorithm[J].Journal of Computer Applications,33(05), 1313-1316,1333. 12.Zhang, Y.W., Yang, Y.P., & Wu, Z. (2015). An optimization

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Published

2020-09-27

Issue

Section

Articles

How to Cite

Luo Shihua, Zhong Zuchang. Comparison of intelligent algorithms of new retail distribution center. Eurasia Journal of Science and Technology. 2020, 2(1): 11-19.