A Fast Parallel Genetic Algorithm for Graph Coloring Problem Based on CUDA

Chen, Buhua; Chen, Bo; Liu, Hongwei; Zhang, Xuefeng

doi:10.1109/cyberc.2015.33

Cited by 6 publications

(2 citation statements)

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“…Concerning brain storm optimization (BSO), Jin and Qin [34] presented GPU-based manner whilst Ma et al [35] proposed parallelized BSO algorithm based on Spark framework for association rule mining. Similar works in [36,37] used GPU and FPGA to accelerate genetic algorithm (GA). What deserves attention is that Garcia et al [38] achieved parallel implementation and comparison of teaching-learning based optimization (TLBO) and Jaya on many-core GPU.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Related Workmentioning

confidence: 99%

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

et al. 2020

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show abstract

“…These calculations are actualized on both SIMD and MIMD parallel models and tried for speed, effectiveness, and for shading arbitrary triangulated networks and diagrams from sparse matrix. Buhua Chen et al [2] introduced a new parallel genetic algorithm to take care of the Graph coloring problem (GCP) in view of Computer Unified Device Architecture (CUDA). All the operators such as initialization, crossover, mutation and selection are designed to be parallel in threads.…”

Section: Related Workmentioning

confidence: 99%