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|Abstract=We propose to modify a type of ant algorithm called Population based Ant Colony Optimization (P-ACO) to allow implementation on an FPGA architecture. Ant algorithms are adapted from the natural behavior of ants and used to find good solutions to combinatorial optimization problems. General layout on the FPGA and algorithmic description are covered. The most notable achievements featured in this paper are a runtime reduction and including the approximation of the heuristic function by a small set of favored decisions which changes over time.
 
|Abstract=We propose to modify a type of ant algorithm called Population based Ant Colony Optimization (P-ACO) to allow implementation on an FPGA architecture. Ant algorithms are adapted from the natural behavior of ants and used to find good solutions to combinatorial optimization problems. General layout on the FPGA and algorithmic description are covered. The most notable achievements featured in this paper are a runtime reduction and including the approximation of the heuristic function by a small set of favored decisions which changes over time.
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{{Forschungsgebiet Auswahl

Aktuelle Version vom 24. September 2009, 20:20 Uhr


Population based Ant Colony Optimization on FPGA


Population based Ant Colony Optimization on FPGA



Published: 2002

Buchtitel: Proceedings of the IEEE International Conference on Field-Programmable Technology (FPT), Hong Kong, 2002
Seiten: 125-133

Referierte Veröffentlichung

BibTeX

Kurzfassung
We propose to modify a type of ant algorithm called Population based Ant Colony Optimization (P-ACO) to allow implementation on an FPGA architecture. Ant algorithms are adapted from the natural behavior of ants and used to find good solutions to combinatorial optimization problems. General layout on the FPGA and algorithmic description are covered. The most notable achievements featured in this paper are a runtime reduction and including the approximation of the heuristic function by a small set of favored decisions which changes over time.


Projekt

AntAlg



Forschungsgruppe

Effiziente Algorithmen


Forschungsgebiet

Ameisenalgorithmen, Rekonfigurierbarkeit, Rechnerarchitektur