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《山东大学学报(理学版)》 ›› 2026, Vol. 61 ›› Issue (1): 94-102.doi: 10.6040/j.issn.1671-9352.1.2024.752

• • 上一篇    

基于混合变异灰狼优化算法的泊位-岸桥调度

杨玉,孙圣博,徐子瑞,蒋效伟,宋强,戴红伟*   

  1. 江苏海洋大学计算机工程学院, 江苏 连云港 222005
  • 发布日期:2026-01-15
  • 通讯作者: 戴红伟(1975— ),男,教授,硕士生导师,博士,研究方向为智能计算、最优化问题、复杂网络等. E-mail:hwdai@jou.edu.cn
  • 作者简介:杨玉(1979— ),女,副教授,硕士生导师,博士,研究方向为智能计算与最优化. E-mail:yangyu@jou.edu.cn*通信作者:戴红伟(1975— ),男,教授,硕士生导师,博士,研究方向为智能计算、最优化问题、复杂网络等. E-mail:hwdai@jou.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(62373171);全国高等院校计算机基础教育研究会教学研究项目(2023-AFCEC-307);江苏省计算机学会教学类项目(JSCS2022028);江苏省大学生创新创业项目(202211641027Z,202311641003Z)

Hybrid mutation based gray wolf optimization algorithm for berth-quay crane scheduling

YANG Yu, SUN Shengbo, XU Zirui, JIANG Xiaowei, SONG Qiang, DAI Hongwei*   

  1. School of Computer Engineering, Jiangsu Ocean University, Lianyungang 222005, Jiangsu, China
  • Published:2026-01-15

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摘要： 为解决灰狼优化(grey wolf optimizer, GWO)算法收敛速度慢、易陷入局部最优等问题,提出一种基于混合变异的灰狼优化(hybrid mutation grey wolf optimizer, HMGWO)算法。采用Tent混沌映射策略初始化种群,融入自适应收敛因子策略平衡搜索多样性,引入高斯-柯西混合变异策略提高算法性能。利用6个基准测试函数进行仿真实验,从寻优能力与收敛性等方面对HMGWO算法进行综合分析。将HMGWO算法应用于离散泊位-岸桥调度问题,1 000次迭代实验后,HMGWO算法的船舶在港时间最短。

关键词: 灰狼优化算法, 混合变异, 混沌映射, 自适应收敛因子, 泊位-岸桥调度

Abstract: In order to address the issues of slow convergence speed and susceptibility to local optimality in the gray wolf optimizer(GWO)algorithm, a hybrid mutation gray wolf optimizer(HMGWO)algorithm is proposed. This new algorithm is based on hybrid mutation and utilizes the Tent chaotic mapping strategy. The population is initialized, and an adaptive convergence factor strategy is incorporated to maintain search diversity. Additionally, the algorithm introduces the Gaussian-Cauchy hybrid mutation strategy to enhance performance. Six benchmark test functions are utilized for simulation experiments, evaluating the HMGWO algorithms optimization capability and convergence. The HMGWO algorithm was applied to the discrete berth-quay crane scheduling problem. After one thousand iterations in experiments, the HMGWO algorithm spent the shortest time for ships in port.

Key words: grey wolf optimization algorithm, hybrid mutation, chaotic mapping, adaptive convergence factor, berth-quay crane scheduling

中图分类号: 

  • TP181
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