计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 106-110.doi: 10.11896/jsjkx.210700096

• 智能计算 • 上一篇    下一篇

基于改进樽海鞘算法的共享单车分布密度优化

周川   

  1. 江苏理工学院商学院 江苏 常州213001
  • 出版日期:2021-11-10 发布日期:2021-11-12
  • 通讯作者: 周川(nufe_zc@163.com)
  • 基金资助:
    江苏省自然科学基金(BK20170315)

Optimization of Sharing Bicycle Density Distribution Based on Improved Salp Swarm Algorithm

ZHOU Chuan   

  1. School of Business,Jiangsu University of Technology,Changzhou,Jiangsu 213001,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:ZHOU Chuan,born in 1990,master,associate researcher.Her main research interests include intelligent algorithm and data mining.
  • Supported by:
    Natural Science Fundation of Jiangsu Province,China(BK20170315).

摘要: 针对城市共享单车分布密度优化问题,提出了一种改进樽海鞘算法。首先,将共享单车分布密度优化问题转换成函数优化问题,以等待时间、花费时间、费用及安全代价为评价指标,建立目标函数。其次,引入一维正态云模型和非线性递减控制策略来改进樽海鞘算法中引领者的搜索机制,增强对局部数据的挖掘能力;引入自适应策略来改进原算法跟随者搜索机制,避免算法陷入局部最优值。最后,通过标准测试函数以及共享单车分布密度优化仿真对所提优化算法的有效性进行了验证,结果表明:相比原樽海鞘算法、萤火虫算法及人工蜂群算法,改进的樽海鞘算法具有更好的稳定性和全局搜索能力,能够更好地实现对共享单车分布密度的优化,提升共享单车的区域利用率,对智慧交通的发展有一定的参考价值。

关键词: 分布密度优化, 共享单车, 云模型, 自适应策略, 樽海鞘算法

Abstract: In this article,an improved sea-squirt algorithm is proposed for the urban bike-sharing distribution density optimization problem.First,the sharing bicycle distribution density optimization problem is converted into a functional optimization problem,and the objective function of optimization is established with waiting time,time spent,cost and safety cost as evaluation indexes.Secondly,a one-dimensional normal cloud model and a nonlinear decreasing control strategy are introduced to improve the leader search mechanism in the Bottleneck algorithm to enhance the mining ability of local data;an adaptive strategy is introduced to improve the follower search mechanism of the original algorithm to avoid the algorithm falling into the local optimum.Finally,the effectiveness of the proposed optimization algorithm is verified by the standard test function and the simulation of shared bicycle distribution density.The results show that the improved Bottlenose sheath algorithm has better stability and global search capability than the original algorithm,firefly algorithm and artificial bee colony algorithm,and can better optimize the distribution density of shared bicycles and improve the regional utilization rate of shared bicycles,which is a reference value for the development of intelligent transportation.It has certain reference value for the development of intelligent transportation.

Key words: Adaptive strategy, Cloud model, Distribution density optimization, Public bicycle, Salp swarm algorithm

中图分类号: 

  • TN911.1-34
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