6533b837fe1ef96bd12a263f

RESEARCH PRODUCT

Optimizing PolyACO Training with GPU-Based Parallelization

Torry TuftelandGuro ØDesneltvedtMorten Goodwin

subject

Computer scienceMathematicsofComputing_NUMERICALANALYSISSignificant part02 engineering and technologyParallel computingFunction (mathematics)Ant colonyComputingMethodologies_ARTIFICIALINTELLIGENCEBottle neck030218 nuclear medicine & medical imaging03 medical and health sciencesAutomatic parallelization0302 clinical medicineConvergence (routing)0202 electrical engineering electronic engineering information engineeringCode (cryptography)020201 artificial intelligence & image processing

description

A central part of Ant Colony Optimisation (ACO) is the function calculating the quality and cost of solutions, such as the distance of a potential ant route. This cost function is used to deposit an opportune amount of pheromones to achieve an apt convergence, and in an active ACO implementation a significant part of the runtime is spent in this part of the code. In some cases, the cost function accumulates up towards 94 % in its run time making it a performance bottle neck.

yearjournalcountryeditionlanguage
2016-01-01
https://doi.org/10.1007/978-3-319-44427-7_20