By Topic

An Agent-based Evolutionary Search for Dynamic Travelling Salesman Problem

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 Dazhi ; Sch. of Inf. Sci. & Eng., Northeastern Univ., Shenyang, China ; Liu Shixin

This paper presents an agent-based evolutionary search algorithm (AES) for solving dynamic travelling salesman problem (DTSP). The proposed algorithm uses the principal of collaborative endeavor learning mechanism in which all the agents within the current population co-evolve to track dynamic optima. Moreover, a local updating rule which is much the same of permutation enforcement learning scheme is induced for diversity maintaining in dynamic environments. The developed search algorithm and benchmark generator are then built to test the evolutionary model for dynamic versions of travelling salesman problem. Experimental results demonstrate that the proposed method is effective on dynamic problems and have a great potential for other dynamic combinatorial optimization problems as well.

Published in:

Information Engineering (ICIE), 2010 WASE International Conference on  (Volume:1 )

Date of Conference:

14-15 Aug. 2010