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基于改进被囊群算法的露天矿无人驾驶卡车运输调度

李在友 孙艳斌 王晓光 陈永 刘光伟 郭直清

李在友,孙艳斌,王晓光,等. 基于改进被囊群算法的露天矿无人驾驶卡车运输调度[J]. 工矿自动化,2022,48(6):87-94, 127.  doi: 10.13272/j.issn.1671-251x.17929
引用本文: 李在友,孙艳斌,王晓光,等. 基于改进被囊群算法的露天矿无人驾驶卡车运输调度[J]. 工矿自动化,2022,48(6):87-94, 127.  doi: 10.13272/j.issn.1671-251x.17929
LI Zaiyou, SUN Yanbin, WANG Xiaoguang, et al. Unmanned truck transportation scheduling in open-pit mines based on improved tunicate swarm algorithm[J]. Journal of Mine Automation,2022,48(6):87-94, 127.  doi: 10.13272/j.issn.1671-251x.17929
Citation: LI Zaiyou, SUN Yanbin, WANG Xiaoguang, et al. Unmanned truck transportation scheduling in open-pit mines based on improved tunicate swarm algorithm[J]. Journal of Mine Automation,2022,48(6):87-94, 127.  doi: 10.13272/j.issn.1671-251x.17929

基于改进被囊群算法的露天矿无人驾驶卡车运输调度

doi: 10.13272/j.issn.1671-251x.17929
基金项目: 国家自然科学基金资助项目(51974144);辽宁省“揭榜挂帅”科技攻关项目(2021JH1/10400011);辽宁省高等学校基本科研项目(LJKZ0340)。
详细信息
    作者简介:

    李在友(1981—),男,山东潍坊人,高级工程师,硕士,主要从事机电供电设备管理工作,E-mail:11610553@chnenergy.com.cn

    通讯作者:

    刘光伟(1981—),男,辽宁沈阳人,教授,博士,博士研究生导师,主要研究方向为露天矿开采设计理论、矿业系统工程,E-mail:liu_guangwei@yeah.net

  • 中图分类号: TD57

Unmanned truck transportation scheduling in open-pit mines based on improved tunicate swarm algorithm

  • 摘要: 针对露天矿无人驾驶卡车运输调度问题,以无人驾驶卡车燃油费用、固定启用费用、故障维修费用及网络基站建设与维护费用之和最小为目标函数,并以采矿场开采量、破碎场破碎量、卡车数量、卡车运输工作量为约束条件,建立了露天矿无人驾驶卡车运输调度优化模型。针对被囊群算法存在全局勘探和局部开采能力不平衡的问题,提出了一种基于Singer映射和参数位置自适应更新机制的改进被囊群算法(ITSA),并将其用于求解露天矿无人驾驶卡车运输调度优化模型。该算法引入Singer映射用于增强初始被囊种群在解空间中的分布性,加快压缩解空间大小,从而提高算法收敛速度;通过参数位置自适应更新机制调节被囊个体与最优被囊个体位置,以增大解空间的搜索范围,从而使算法跳出局部最优。仿真结果表明:与灰狼优化算法(GWO)、鲸鱼优化算法(WOA)、原子搜索优化算法(ASO)及被囊群算法(TSA)4种群智能优化算法相比,ITSA具有更好的收敛精度、收敛速度和稳定性能;在单峰基准函数上,ITSA的各项评价指标远优于其他4种算法,表明ITSA具有更好的局部开采能力;在多峰基准函数上,ITSA的各项评价指标表现出更好的寻优性能,表明ITSA具有更好的全局勘探性能。实际应用场景表明,ITSA用于求解无人驾驶卡车运输调度优化模型时具有更快的收敛速度和更高的收敛精度,且减少了卡车运输费用和运输距离。

     

  • 图  1  不同方法生成的初始被囊群位置

    Figure  1.  Initial tunicate swarm location generated by different methods

    图  2  基于随机参数和自适应权重因子生成的函数值

    Figure  2.  Function values generated by random parameters and adaptive weighting factors

    图  3  ITSA流程

    Figure  3.  Flow of improved tunicate swarm algorithm

    图  4  不同群智能优化算法对基准函数的寻优收敛曲线

    Figure  4.  Optimal convergence curves of different swarm intelligence optimization algorithms for benchmark functions

    图  5  不同群智能优化算法的迭代收敛曲线

    Figure  5.  Iterative convergence curves of different swarm intelligence optimization algorithms

    表  1  不同群智能优化算法的基准函数寻优结果

    Table  1.   Benchmark function optimization results of different swarm intelligence optimization algorithms

    函数评价指标GWOWOAASOTSAITSA
    F1(sphere)平均值3.62×10−704.20×10−1662.02×10−231.72×10−513.64×10−250
    标准差1.04×10−6901.57×10−238.53×10−510
    F2(schwefel 2.22)平均值6.08×10−411.81×10−1074.89×10−111.29×10−316.04×10−131
    标准差7.22×10−419.56×10−1074.71×10−111.16×10−312.43×10−130
    F3(schwefel 1.2)平均值5.90×10−199.93×1032.21×1027.66×10−166.28×10−213
    标准差2.17×10−185.85×1031.33×1023.58×10−150
    F4(griewank)平均值3.36×10−38.10×10−33.36×10−38.24×10−30
    标准差7.20×10−32.62×10−27.15×10−31.36×10−20
    F5(ackley)平均值1.28×10−143.85×10−153.28×10−122.044.44×10−15
    标准差2.72×10−152.30×10−151.83×10−121.480
    F6(weierstrass)平均值003.26×10−32.13×10−150
    标准差001.30×10−24.63×10−150
    下载: 导出CSV

    表  2  采矿场至破碎站不同路线距离

    Table  2.   Route distance between mining station and crushing station km

    采矿场采矿场至破碎站距离
    路线1路线2路线3
    M13.5933.6123.485
    M22.2172.3222.107
    M31.6761.6021.701
    M43.4573.5523.388
    M53.4373.3063.521
    M63.3253.2323.476
    M73.0722.9983.102
    M83.5853.6553.475
    下载: 导出CSV

    表  3  不同群智能优化算法下模型求解结果

    Table  3.   Model solution results under different swarm intelligence optimization algorithms

    算法最小运输费用/元卡车运输距离/km
    GWO8 360.31742.356
    WOA8 586.49045.751
    ASO8 806.12646.861
    TSA8 535.57443.362
    ITSA8 248.04241.936
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-04-12
  • 修回日期:  2022-06-20
  • 网络出版日期:  2022-06-28

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