Accelerating the performance of particle swarm optimization for embedded applications

Authors:
Girma S. Tewolde;Darrin M. Hanna;Richard E. Haskell
Affiliations:
Department of Electrical and Computer Engineering, Kettering University, Flint, MI;Electrical & Computer Engineering Dept., Oakland University, Rochester, MI;Electrical & Computer Engineering Dept., Oakland University, Rochester, MI
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

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Abstract

The ever increasing popularity of particle swarm optimization (PSO) algorithm is recently attracting attention to the embedded computing world. Although PSO is in general considered to be computationally efficient algorithm, its direct software implementation on complex problems, targeted on low capacity embedded processors could however suffer from poor execution performance. This paper first evaluates the performance of the standard PSO algorithm on a typical embedded platform (using a 16-bit microcontroller). Then, a modular, flexible and reusable architecture for a hardware PSO engine, for accelerating the algorithm's performance, will be presented. Finally, implementation test results of the proposed architecture targeted on Field Programmable Gate Array (FPGA) technology will be presented and its performance compared against software executions on benchmark test functions.