The User-Controlled K Shortest Paths with Diversity

Yongqing Wang, Wei Huang, Honghao Zhang*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The user-controlled K shortest path problem with diversity (UKSPD) is a general form of the K shortest path (KSP) problem in graphs. Instead of finding the K shortest from point to point, the UKSPD determines the level of similarity of the K shortest paths through user input parameters. In this paper, we formally describe the UKSPD problem, which acts as a multi-objective optimization problem. Considering the application of genetic algorithm in multi-objective optimization, we propose an improved genetic algorithm to solve the UKSPD problem. The basic mechanism of the whole algorithm is as follows: chromosomes are directly represented as paths, crossover and mutation operations are performed to ensure the connectivity of the paths, and the user input parameter in each iteration determines the similarity of the selected paths. The proposed algorithm is tested on the New York City Map and compared with the improved Dijkstra algorithm, and the experimental results illustrate the effectiveness of the proposed genetic algorithm.

Original languageEnglish
Title of host publication6th International Workshop on Advanced Algorithms and Control Engineering, IWAACE 2022
EditorsDaowen Qiu, Ning Sun
PublisherSPIE
ISBN (Electronic)9781510657755
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event6th International Workshop on Advanced Algorithms and Control Engineering, IWAACE 2022 - Qingdao, China
Duration: 8 Jul 202210 Jul 2022

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume12350
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference6th International Workshop on Advanced Algorithms and Control Engineering, IWAACE 2022
Country/TerritoryChina
CityQingdao
Period8/07/2210/07/22

Keywords

  • K shortest path
  • genetic algorithm

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