|Table of Contents|

Research on Route Planning of Generic CablingBased on Genetic Algorithm(PDF)

南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2020年04期
Page:
51-56
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Research on Route Planning of Generic CablingBased on Genetic Algorithm
Author(s):
Chen Chao1Chen Zhenzhong2
(1.Jiangsu Eazytec Co.,Ltd.,Yixing 214200,China)(2.College of Mechanical Engineering,Donghua University,Shanghai 201620,China)
Keywords:
genetic algorithmgeneric cablingbuilding intelligencepath planning
PACS:
TM715
DOI:
10.3969/j.issn.1672-1292.2020.04.008
Abstract:
In this paper,the path planning of generic cabling system is taken as the research object,and the dual objectives of the public path and the shortest path are planned as a whole to meet the different construction needs in different situations. For this reason,this paper first gives the integer programming model of PDS decision,and uses genetic algorithm to build a new PDS optimization algorithm for the design and research of PDS path planning. Based on genetic algorithm,we realize the coordination of public path and shortest path by weighted combination,in which the longer the public path is,the shorter the construction period is,and the longer the shortest path is,the more cost is saved. Finally,through the simulation,the validity of the proposed model and method is verified.

References:

[1] 许海峰.“互联网+”在智能建筑弱电系统及综合布线中的应用分析[J]. 现代建筑电气,2020,11(1):37-40.
[2]戴蓉. 浅谈软件开发项目的成本管控[J]. 现代商业,2020(2):37-39.
[3]杜学美,赵文林,雷玮. 基于粒子群算法的项目工期-质量-成本-安全的综合优化[J]. 系统工程,2019,37(4):23-25.
[4]王玫婷,张建坤. 基于改进遗传算法的工程项目多目标优化研究[J]. 建筑经济,2018,38(11):5-6.
[5]邱幸运. 基于量子粒子群算法的工程项目多目标优化研究[D]. 邯郸:河北工程大学,2019.
[6]金力仙,李金刚. 基于遗传算法的多目标路径优化算法的研究[J]. 计算机技术与发展,2018,28(2):54-58.
[7]WANG Y L,LO K M. Generic cabling of intelligent buildings based on ant colony algorithm[J]. International Journal of Software Science and Computational Intelligence(IJSSCI),2011,3(2):49-61.
[8]杜海遥. 基于斯坦纳树和粒子群算法的机电产品布线优化技术研究[D]. 南京:南京航空航天大学,2017.
[9]王树玉. 基于DNA算法的智能建筑综合布线辅助系统设计[D]. 北京:北京电子科技大学,2015.
[10]SRINIVAS N,KALYANMOY D. Multiobjective function optimization using nondominated sorting genetic algorithms[J]. IEEE Transactions on Evloutionary Computation,1994,2(3):221-248.
[11]SABYASACHI M,TSOURDOS A. Optimal topology for consensus using genetic algorithm[J]. Neurocomputing,2020,40(4):15-18.
[12]胡良剑,孙晓君. MATLAB数学实验[M]. 2版. 北京:北京高等教育出版社,2014.
[13]陈成. 基于改进遗传算法的带时间窗的多目标配送路径优化[J]. 信息技术与信息化,2018,30(11):31-32.
[14]党宏社,孙心妍. 基于遗传算法的工厂AGV路径优化研究[J]. 电子产品世界,2020(1):26-27.

Memo

Memo:
-
Last Update: 2020-12-15