Title: An improved genetic algorithm in shared bicycle parking point allocation

Authors: Guanlin Chen; Jiawei Shi; Huang Xu; Tian Li; Wujian Yang

Addresses: School of Computer and Computing Science, Zhejiang University City College, Hangzhou, 310015, China; College of Computer Science and Technology, Zhejiang University, Hangzhou, 310027, China ' School of Computer and Computing Science, Zhejiang University City College, Hangzhou, 310015, China; School of Computer Science and Engineering, Anhui University of Science and Technology, Huainan, 232000, China ' School of Computer and Computing Science, Zhejiang University City College, Hangzhou, 310015, China ' School of Computer and Computing Science, Zhejiang University City College, Hangzhou, 310015, China ' School of Computer and Computing Science, Zhejiang University City College, Hangzhou, 310015, China

Abstract: Aiming to solve the problem of inadequate parking places for shared bicycles especially during peak hours, an improved genetic algorithm for parking point allocation is proposed in this paper. We integrate linear regression algorithm with the genetic algorithm to increase the direct of individual mutation, which leads to avoiding falling into local optimum. Meanwhile, we use linear regression to haste the convergence speed of genetic algorithm which ensures the new method can improve efficiency while allocating parking point. For the sake of carrying out the experiment accurately and conveniently, we use geohash to encode the locations of parking points and bicycles into short letters and numbers. According to the analysis of experimental results, it proves the improved algorithm is superior to the conventional method for parking point allocation.

Keywords: genetic algorithm; linear regression; shared bicycle; parking point allocation; geohash.

DOI: 10.1504/IJSN.2020.109710

International Journal of Security and Networks, 2020 Vol.15 No.3, pp.141 - 147

Received: 17 Dec 2019
Accepted: 18 Dec 2019
Published online: 21 Sep 2020 *

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