Scheduled System Maintenance:
Some services will be unavailable Sunday, March 29th through Monday, March 30th. We apologize for the inconvenience.
By Topic

Solving Problems with Hidden Dynamics - Comparison of Extremal Optimisation and Ant Colony System

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)
Moser, I. ; Swinburne Univ., Melbourne ; Hendtlass, T.

Solving dynamic combinatorial problems poses a particular challenge to optimisation algorithms. Optimising a dynamic problem that does not notify the solver when a change has been made is very difficult for most well-known algorithms. Extremal optimisation is a recent addition to the group of biologically inspired optimisation algorithms, while ant colony system has been used to solve a large variety of problem types in static and dynamic contexts. Both algorithms seem well suited to solving problems with hidden dynamics. We present a performance comparison of the two algorithms and endeavour to highlight particular strengths and weaknesses observed with different types of dynamic problem changes.

Published in:

Evolutionary Computation, 2006. CEC 2006. IEEE Congress on

Date of Conference:

16-21 July 2006