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An efficient fine-grained parallel particle swarm optimization method based on gpu-acceleration

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Indexed by:期刊论文

Date of Publication:2007-12-01

Journal:INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL

Included Journals:SCIE、Scopus

Volume:3

Issue:6B

Page Number:1707-1714

ISSN No.:1349-4198

Key Words:particle swarm optimization; fine-grained; parallel process; GPU

Abstract:Fine-grained parallel particle swarm optimization (FGPSO), 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 FGPSO method based on GPU-acceleration, which maps a parallel PSO algorithm to texture-rendering on consumer-level graphics cards. The analytical results demonstrate that the proposed method increases the population size, speeds up its execution and provides ordinary users with a feasible FGPSO solution.

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