DocumentCode :
3400229
Title :
Heuristics for ant colony optimisation using the generalised assignment problem
Author :
Randall, Marcus
Author_Institution :
Meta-heuristic Search Group, Bond Univ., Gold Coast, Qld., Australia
Volume :
2
fYear :
2004
fDate :
19-23 June 2004
Firstpage :
1916
Abstract :
The use of embedded heuristics within meta-heuristic search algorithms has a large effect on their performance. One of the more recent classes of meta-heuristics, ant colony optimisation, is examined in terms of both the heuristic used to select solution components and the local search heuristics used to improve solutions. Static and adaptive heuristic control strategies are developed, as well as neighbourhood oriented local search transition operators, that are able to obtain good solutions to large and tightly constrained generalised assignment problem instances.
Keywords :
heuristic programming; optimisation; search problems; self-adjusting systems; state-space methods; ant colony optimisation; embedded heuristics; generalised assignment problem; local search transition operators; meta-heuristic search algorithms; Adaptive control; Ant colony optimization; Ash; Bonding; Cost function; Heuristic algorithms; Nominations and elections; Programmable control; Search methods; System testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2004. CEC2004. Congress on
Print_ISBN :
0-7803-8515-2
Type :
conf
DOI :
10.1109/CEC.2004.1331130
Filename :
1331130
Link To Document :
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