@Article{JCST-32-2,
author = {Yan, Xiao-Hu and He, Fa-Zhi and Yi-Lin, Chen},
title = {A Novel Hardware/Software Partitioning Method Based on Position Disturbed Particle Swarm Optimization with Invasive Weed Optimization},
journal = {Journal of Computer Science & Technology},
year = {2017},
volume = {32},
number = {2},
pages = {340--355},
abstract = {<p style="text-align: justify;">With the development of the design complexity in embedded systems, hardware/software (HW/SW) partitioning becomes a challenging optimization problem in HW/SW co-design. A novel HW/SW partitioning method based on
position disturbed particle swarm optimization with invasive weed optimization (PDPSO-IWO) is presented in this paper.
It is found by biologists that the ground squirrels produce alarm calls which warn their peers to move away when there is
potential predatory threat. Here, we present PDPSO algorithm, in each iteration of which the squirrel behavior of escaping
from the global worst particle can be simulated to increase population diversity and avoid local optimum. We also present
new initialization and reproduction strategies to improve IWO algorithm for searching a better position, with which the
global best position can be updated. Then the search accuracy and the solution quality can be enhanced. PDPSO and
improved IWO are synthesized into one single PDPSO-IWO algorithm, which can keep both searching diversification and
searching intensification. Furthermore, a hybrid NodeRank (HNodeRank) algorithm is proposed to initialize the population
of PDPSO-IWO, and the solution quality can be enhanced further. Since the HW/SW communication cost computing is
the most time-consuming process for HW/SW partitioning algorithm, we adopt the GPU parallel technique to accelerate
the computing. In this way, the runtime of PDPSO-IWO for large-scale HW/SW partitioning problem can be reduced efficiently. Finally, multiple experiments on benchmarks from state-of-the-art publications and large-scale HW/SW partitioning
demonstrate that the proposed algorithm can achieve higher performance than other algorithms.</p>},
issn = {1860-4749},
doi = {https://doi.org/10.1007/s11390-017-1714-2},
url = {https://global-sci.com/article/86343/a-novel-hardwaresoftware-partitioning-method-based-on-position-disturbed-particle-swarm-optimization-with-invasive-weed-optimization}
}