DocumentCode
2462665
Title
Solving Problems with Hidden Dynamics - Comparison of Extremal Optimisation and Ant Colony System
Author
Moser, I. ; Hendtlass, T.
Author_Institution
Swinburne Univ., Melbourne
fYear
2006
fDate
16-21 July 2006
Firstpage
1248
Lastpage
1255
Abstract
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.
Keywords
combinatorial mathematics; optimisation; ant colony system; biologically inspired optimisation algorithms; dynamic combinatorial problems; extremal optimisation; hidden dynamics; Ant colony optimization; Communications technology; Design engineering; Design optimization; Genetic mutations; Glass; Heat engines; Partitioning algorithms; Physics; Stability;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation, 2006. CEC 2006. IEEE Congress on
Conference_Location
Vancouver, BC
Print_ISBN
0-7803-9487-9
Type
conf
DOI
10.1109/CEC.2006.1688452
Filename
1688452
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