A hardware accelerator for Particle Swarm Optimization

The Particle Swarm Optimization or PSO is a heuristic based on a population of individuals, in which the candidates for a solution of the problem at hand evolve through a simulation process of a social adaptation simplified model. Combining robustness, efficiency and simplicity, PSO has gained great popularity as many successful applications are reported. The algorithm has proven to have several advantages over other algorithms that based on swarm intelligence principles. The use of PSO solving problems that involve computationally demanding functions often results in low performance. In order to accelerate the process, one can proceed with the parallelization of the algorithm and/or map it directly onto hardware. This paper presents a novel massively parallel coprocessor for PSO implemented using reconfigurable hardware. The implementation results show that the proposed architecture is up to 135× and not less than 20× faster in terms of optimization time when compared to the direct software execution of the algorithm. Both the accelerator and the processor used to run the software version are mapped into FPGA reconfigurable hardware.

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► A novel massively parallel coprocessor for PSO.
► Implementation using reconfigurable hardware.
► Results show that the proposed architecture is up to 135× and not less than 20× faster in than the direct software execution of the algorithm.