Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154401
Title: An optimization framework of K-means clustering and metaheuristic for traveling salesman problem
Authors: Wang, Benquan
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Wang, B. (2021). An optimization framework of K-means clustering and metaheuristic for traveling salesman problem. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/154401
Abstract: In this dissertation, we first studied the optimization framework of K-means clustering genetic algorithm. By comparing with traditional genetic algorithm, we verified that the optimization framework can effectively save computing time when solving large-scale traveling salesman problems and the final path length also meets the requirements. Based on the randomness of the genetic algorithm, we make the improvement of this optimization framework. By adjusting the sequence of operations about the framework, we compute the path length on each iteration during cluster process and select the optimal results through comparison. The improved framework has a better path length than before. In addition, we selected the combination of ant colony algorithm and K-means clustering to form another optimization framework, which also verified the optimization effect on the running time when solving large-scale traveling salesman problems and could decrease calculation error rate. At the same time, we conduct related research on the parameter analysis of ant colony algorithm and summarize properties of each parameter of the ant colony algorithm and their influence on the final optimization result.
URI: https://hdl.handle.net/10356/154401
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
Msc_Dissertation_ Wang Benquan.pdf
  Restricted Access
1.16 MBAdobe PDFView/Open

Page view(s)

26
Updated on Jan 24, 2022

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.