多类数据集并行分类的粒子群优化实现

2017 International Conference on Computer, Communication and Signal Processing (ICCCSP) Pub Date : 1900-01-01 DOI:10.1109/ICCCSP.2017.7944088

M. Balasaraswathi, B. Kalpana

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引用次数: 0

摘要

本研究概念是结合粒子群优化(PSO)和模拟退火(SA)的并行PSO-SA模型。并行PSO- sa通过并行化每个粒子的操作来运行，Multistart PSO并行运行嵌入模拟退火的几个正常版本的PSO。在基准数据集上进行的实验结果表明，该方法可以降低时间复杂度，提高分类精度。

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Particle swarm optimization parallelism implementation to classify multiclass datasets

This research concept deals with Parallelizes PSO-SA model which combines the particle swarm optimization (PSO) and Simulated Annealing (SA). Parallel PSO-SA operates by parallelizing the operation of each of the particles and Multistart PSO runs parallel several normal versions of PSO embedded with Simulated Annealing in parallel. The experimental results were conducted on benchmark data sets and the proposed approach can reduce the time complexity and also to increase classification accuracy.

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2017 International Conference on Computer, Communication and Signal Processing (ICCCSP)

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