3829

A Parallel Immune Algorithm Based on Fine-Grained Model with GPU-Acceleration

Jianming Li, Lihua Zhang, Linlin Liu
Sch. of Electron. & Inf. Eng., Dalian Univ. of Technol., Dalian, China
Fourth International Conference on Innovative Computing, Information and Control (ICICIC), 2009

@article{li1899parallel,

   title={A parallel immune algorithm based on fine-grained model with GPU-acceleration},

   author={Li, J. and Zhang, L. and Liu, L.},

   journal={icicic},

   pages={683–686},

   year={2009},

   publisher={IEEE Computer Society}

}

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Fine-grained parallel immune algorithm (FGIA), though a popular and robust strategy for solving complicated optimization problems, is sometimes inconvenient to use as its population size is restricted by heavy data communication and the parallel computers are relatively difficult to use, manage, maintain and may not be accessible to most researchers. In this paper, we propose a FGIA method based on GPU-acceleration, which maps parallel IA algorithm to GPU through the CUDA. We implement the IA on the base of the framework of genetic algorithm (GA), the analytical results demonstrate that the proposed method increases the population size, speeds up its execution and provides ordinary users with a feasible FGIA solution.
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