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主管单位 中华人民共和国
工业和信息化部
主办单位 哈尔滨工业大学 主编 冷劲松 国际刊号ISSN 0367-6234 国内刊号CN 23-1235/T

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引用本文:李长安,谢宗奎,吴忠强,张立杰.改进灰狼算法及其在港口泊位调度中的应用[J].哈尔滨工业大学学报,2021,53(1):101.DOI:10.11918/201911117
LI Chang′an,XIE Zongkui,WU Zhongqiang,ZHANG Lijie.Improved grey wolf algorithm and its application in port berth scheduling[J].Journal of Harbin Institute of Technology,2021,53(1):101.DOI:10.11918/201911117
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改进灰狼算法及其在港口泊位调度中的应用
李长安1,3,4,谢宗奎2,吴忠强2,张立杰1,3
(1. 先进锻压成形技术与科学教育部重点实验室(燕山大学),河北 秦皇岛 066004;2 .燕山大学 电气工程学院, 河北 秦皇岛 066004;3. 河北省重型机械流体动力传输与控制重点实验室(燕山大学),河北 秦皇岛 066004; 4.神华天津煤炭码头有限责任公司,天津 300457)
摘要:
为提升港口泊位调度的效率,提出一种基于改进灰狼算法的船舶调度优化方法. 针对灰狼算法收敛速度慢、寻优精度不高等不足,引入Sin混沌初始化,增强初始种群的均匀性和遍历性;引入头狼引领策略,加快算法收敛,提高算法效率;引入合作竞争机制,增强算法局部搜索的能力;在灰狼种群位置更新时引入自适应权值,以满足不同时期的寻优要求. 为验证改进灰狼算法的有效性,将该算法与其他6种不同算法进行对比实验. 结果表明:改进灰狼算法的收敛速度明显快于其他6种算法,在不同测试函数的仿真中均能得到所求函数的最优值,且该算法独立运行20次取得解的标准差均为0,表明该算法对不同维度的求解问题均具有很好的抗扰性;在港口泊位调度的应用中,经过该算法优化后,所有船舶停留总时间较优化前缩短了14.7%,大幅度缩短了船舶的在港时间. 该算法在船舶调度优化中取得了满意的应用效果,能够得出相对较佳的调度方案,实现泊位停靠最优化,为港口泊位调度优化提供了新方法.
关键词:  灰狼算法(GWO)  群体智能  函数优化  全局搜索  局部开发
DOI:10.11918/201911117
分类号:TP18
文献标识码:A
基金项目:国家自然科学基金(51875499)
Improved grey wolf algorithm and its application in port berth scheduling
LI Chang′an1,3,4,XIE Zongkui2,WU Zhongqiang2,ZHANG Lijie 1,3
(1. Key Laboratory of Advanced Forging & Stamping Technology and Science of Ministry of Education of China(Yanshan University), Qinhuangdao 066004, Hebei, China; 2. College of electric engineering(Yanshan University), Qinhuangdao 066004, Hebei, China; 3. Hebei Key Laboratory of Heavy Machinery Fluid Power Transmission and Control(Yanshan University), Qinhuangdao 066004, Hebei, China; 4.Shenhua Tianjin Coal Terminal Co., Ltd, Tianjin 300457,China.)
Abstract:
To improve the efficiency of port berth scheduling, and aiming at the shortages of grey wolf optimization with slow convergence speed and easy to fall into local optimum, an improved grey wolf optimization is proposed. The improved grey wolf optimization allocates the initial positions of individuals by sin chaotic sequence, enhancing the population uniformity and ergodicity. The head wolf leading strategy is introduced to accelerate the convergence of the algorithm and improve the efficiency of the algorithm. The cooperative competition mechanism is introduced to enhance the local search ability of the algorithm. When the gray wolf population is updated, the adaptive weight is introduced to meet the optimization requirements of different periods. Finally, the performance of the algorithm is analyzed and compared with six algorithms. Experiments show that the algorithm has obvious advantages in convergence speed and convergence accuracy. And the standard deviation of the solution obtained by running the algorithm 20 times independently is 0, which shows that the algorithm has good immunity to solving problems of different dimensions. Besides, satisfactory results have been achieved in the application of port berth scheduling, after the optimization of the algorithm, the total stay time of all ships was reduced by 14.7% compared with before. So the algorithm can obtain relatively better scheduling schemes and provides a new strategy for port berth scheduling optimization.
Key words:  grey wolf optimization  swarm intelligence  function optimization  global exploration  local optimization

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