[1]战彦君,张玲华.基于改进灰狼算法的充电桩供电线路规划研究[J].计算机技术与发展,2023,33(08):186-191.[doi:10. 3969 / j. issn. 1673-629X. 2023. 08. 027]
 ZHAN Yan-jun,ZHANG Ling-hua.Research on Power Supply Line Planning of Charging Pile Based on Improved Gray Wolf Algorithm[J].,2023,33(08):186-191.[doi:10. 3969 / j. issn. 1673-629X. 2023. 08. 027]
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基于改进灰狼算法的充电桩供电线路规划研究()
分享到:

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
33
期数:
2023年08期
页码:
186-191
栏目:
新型计算应用系统
出版日期:
2023-08-10

文章信息/Info

Title:
Research on Power Supply Line Planning of Charging Pile Based on Improved Gray Wolf Algorithm
文章编号:
1673-629X(2023)08-0186-06
作者:
战彦君1 张玲华12
1. 南京邮电大学 通信与信息工程学院,江苏 南京 210003;
2. 江苏省通信与网络技术工程研究中心,江苏 南京 210003
Author(s):
ZHAN Yan-jun1 ZHANG Ling-hua12
1. School of Communication and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China;
2. Jiangsu Communication and Network Technology Engineering Research Center,Nanjing 210003,China
关键词:
充电桩灰狼优化算法Tent 混沌映射非线性收敛因子线路规划
Keywords:
charge pilegray wolf optimization algorithmTent chaotic mapnonlinear convergence factorline planning
分类号:
TP181
DOI:
10. 3969 / j. issn. 1673-629X. 2023. 08. 027
摘要:
针对城市电动汽车共享充电桩分布的随机性和不均匀性,为了以最高效率和最低成本对区域中充电桩供电,将灰狼算法应用于充电桩供电线路规划中。 针对传统灰狼算法易陷入局部最优解、初始种群分布不均匀和后期收敛速度慢等问题,提出了一种基于 Tent 映射和非线性收敛因子的改进灰狼算法。 通过 Tent 混沌映射产生种群初始解以丰富种群多样性,采用非线性收敛因子和加入随机扰动的位置更新公式来避免陷入局部最优和加快算法后期收敛速度。 对城市充电桩进行供电线路实例仿真,并将改进算法与传统灰狼算法、粒子群算法、遗传算法、免疫算法、模拟退火算法、布谷鸟算法、教与学算法进行比较。 实验结果表明,改进算法收敛速度快,稳定性好,可以很好地应用于充电桩供电线路规划。
Abstract:
Aiming at the randomness and unevenness of the distribution of shared charging piles for urban electric vehicles,gray wolfalgorithm is applied to the?
power supply line planning of charging piles in order to achieve the lowest cost and highest efficiency of powersupply. Aiming at the problems such as?
local optimal solution,uneven initial population distribution and slow convergence,an improvedgray wolf algorithm based on Tent mapping and nonlinear convergence factor is proposed. The Tent chaotic map is used to generate theinitial population solution to enrich the diversity of the population. The nonlinear convergence factor and the position update formula withrandom perturbation are used to avoid falling into the local optimum and speed up the?
later convergence of the algorithm. The improvedalgorithm is compared with the traditional gray wolf algorithm, particle swarm optimization algorithm, genetic algorithm, immunealgorithm,simulated annealing algorithm,cuckoo algorithm and teaching and learning algorithm. The experimental results show that theimproved algorithm has fast convergence speed and good stability, which can be well applied to the problem of power supply lineplanning of charging pile.

相似文献/References:

[1]郭玉纯,曹小鹏,胡元娇.禁忌搜索灰狼优化算法研究[J].计算机技术与发展,2019,29(12):55.[doi:10. 3969 / j. issn. 1673-629X. 2019. 12. 010]
 GUO Yu-chun,CAO Xiao-peng,HU Yuan-jiao.Research on Tabu Search-grey Wolf Optimization Algorithm[J].,2019,29(08):55.[doi:10. 3969 / j. issn. 1673-629X. 2019. 12. 010]

更新日期/Last Update: 2023-08-10