Abstract: In this paper, a digital implementation of Particle Swarm Optimization algorithm (PSO) is developed for implementation on Field Programmable Gate Array (FPGA). PSO is a recent intelligent heuristic search method in which the mechanism of algorithm is inspired by the swarming of biological populations. PSO is similar to the Genetic Algorithm (GA). In fact, both of them use a combination of deterministic and probabilistic rules. The experimental results of this algorithm are effective to evaluate the performance of the PSO compared to GA and other PSO algorithm. New digital solutions are available to generate a hardware implementation of PSO Algorithms. Thus, we developed a hardware architecture based on Finite state machine (FSM) and implemented into FPGA to solve some dispatch computing problems over other circuits based on swarm intelligence. Moreover, the inherent parallelism of these new hardware solutions with a large computational capacity makes the running time negligible regardless the complexity of the processing.

BEN AMEUR Mohamed sadek and SAKLY Anis, “FPGA Implementation of Parallel Particle Swarm Optimization Algorithm and Compared with Genetic Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 7(8), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070809

@article{sadek2016,
title = {FPGA Implementation of Parallel Particle Swarm Optimization Algorithm and Compared with Genetic Algorithm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070809},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070809},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {8},
author = {BEN AMEUR Mohamed sadek and SAKLY Anis}
}