Two Simple Tricks for Fast Cache-Aware Parallel Particle Swarm Optimization

Hajewski, Jeff; Oliveira, Suely

doi:10.1109/cec.2019.8790219

Cited by 3 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some studies [26][27][28][29][30] applied GPU to parallelize PSO, putting forward diverse parallel strategies. Hajewski and Oliveira [31] developed fast cache-aware parallel PSO relying on OpenMP. Ant colony optimization (ACO) [32] and artificial bee colony (ABC) [33] were also parallelized by GPU.…”

Section: Related Workmentioning

confidence: 99%

Improving the Performance of Whale Optimization Algorithm through OpenCL-Based FPGA Accelerator

et al. 2020

View full text Add to dashboard Cite

Whale optimization algorithm (WOA), known as a novel nature-inspired swarm optimization algorithm, demonstrates superiority in handling global continuous optimization problems. However, its performance deteriorates when applied to large-scale complex problems due to rapidly increasing execution time required for huge computational tasks. Based on interactions within the population, WOA is naturally amenable to parallelism, prompting an effective approach to mitigate the drawbacks of sequential WOA. In this paper, field programmable gate array (FPGA) is used as an accelerator, of which the high-level synthesis utilizes open computing language (OpenCL) as a general programming paradigm for heterogeneous System-on-Chip. With above platform, a novel parallel framework of WOA named PWOA is presented. The proposed framework comprises two feasible parallel models called partial parallel and all-FPGA parallel, respectively. Experiments are conducted by performing WOA on CPU and PWOA on OpenCL-based FPGA heterogeneous platform, to solve ten well-known benchmark functions. Meanwhile, other two classic algorithms including particle swarm optimization (PSO) and competitive swarm optimizer (CSO) are adopted for comparison. Numerical results show that the proposed approach achieves a promising computational performance coupled with efficient optimization on relatively large-scale complex problems.

show abstract