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
Link To Document :
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