By Topic

An Efficient Approach for Solving TSP: The Rapidly Convergent Ant Colony 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
$31 $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)
Lingling Wang ; Sch. of Math. & Comput. Sci., Nanjing Normal Univ., Nanjing ; Qingbao Zhu

Although many significant achievements have been made on using ant colony optimization (ACO) algorithm to solve traveling salesman problem (TSP) and similar large-scale computational problems, the long convergent time required in the large-scale optimization still remains a computing bottle neck of ACO algorithm. In this paper, we present a rapidly convergent ant colony optimization (rcACO) algorithm to solve the TSP. In this algorithm, adaptive pheromone update is carried out according to the distance ants have moved, meanwhile, the inversion operator is used to enhance local search, etc. Our huge numerical experimental results demonstrate that the convergence speed of rcACO is tens to hundreds times faster than the recently improved ACO algorithms, meanwhile the global optimal solution can be achieved.

Published in:

Natural Computation, 2008. ICNC '08. Fourth International Conference on  (Volume:4 )

Date of Conference:

18-20 Oct. 2008