By Topic

Multi-path acquiring methods based on multi-population parallel genetic algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Wang Zhengwu ; School of Traffic & transportation Engineering, Changsha University of Science &Technology, Hunan 410076, China ; Li Chunyuan

To avoid the negative effect of guidance and to decrease congestion shifting caused because the drivers choose the optimum path simultaneously, more reasonable paths should be recommended, but there is no quick and effective algorithm to seek the k-shortest path. Genetic algorithm, characterized by overall optimization and potential parallel, is suitable for k-shortest path seeking. When the ordered nodes are applied, premature convergence and low searching efficiency at later stage of evolutionary often appear because a large number of invalid paths are produced during the genetic operation, and the path is more blindly generated. This paper will improve the algorithm in two ways: firstly, convergence speed is improved by the introduction of multi-population parallel algorithm which can maintain the population diversities, prevent premature convergence and improve later searching efficiency; secondly, a new code method is used to reduce algorithm complexity. The code method based on the turn actions is adopted to decrease the invalid path number during the genetic operation while the loop punishment factor is introduced in the fitness calculation to avoid the operations of eliminating loops. Numerical research shows that the improved algorithm has the advantages on fast convergence and high searching efficiency.

Published in:

2008 27th Chinese Control Conference

Date of Conference:

16-18 July 2008