By Topic

Solving NP-complete problem using ACO 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)
Asif, M. ; Dept. of Comput. Sci., NUCES, Islamabad, Pakistan ; Baig, R.

Recent studies have shown that Evolutionary Algorithms have had reasonable success at providing solutions to those problems that fall in NP-Complete class of algorithms. Ant Colony Optimization (ACO) algorithm is one of the promising field of evolutionary algorithms that gave acceptable solutions to Travelling Salesperson Problem and various Network Routing Optimization problems in polynomial time. These classic computer science problems belong to a NP-Complete class of problems that is amongst some of the most interesting in mathematics, including the Sudoku Puzzle Problem. People have tried to automate solving Sudoku Puzzle Problem using brute force, tabu search. Given the success of ACO algorithm with problems within NP-Complete class of problems, it would be interesting to see how it handles this puzzle. A novel technique is presented as modification to standard ACO algorithm. Moreover, we will compare performance matrix (quality of solution and time complexity) of ACO algorithm with other techniques presented in the past to solve the Sudoku puzzle.

Published in:

Emerging Technologies, 2009. ICET 2009. International Conference on

Date of Conference:

19-20 Oct. 2009