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首页> 外文期刊>IEEE Transactions on Reliability >A Novel Hybrid Method of Parameters Tuning in Support Vector Regression for Reliability Prediction: Particle Swarm Optimization Combined With Analytical Selection
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A Novel Hybrid Method of Parameters Tuning in Support Vector Regression for Reliability Prediction: Particle Swarm Optimization Combined With Analytical Selection

机译：支持向量回归中用于参数预测的混合参数调整新方法：粒子群算法与分析选择相结合

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摘要

Support vector regression (SVR) is a widely used technique for reliability prediction. The key issue for high prediction accuracy is the selection of SVR parameters, which is essentially an optimization problem. As one of the most effective evolutionary optimization methods, particle swarm optimization (PSO) has been successfully applied to tune SVR parameters and is shown to perform well. However, the inherent drawbacks of PSO, including slow convergence and local optima, have hindered its further application in practical reliability prediction problems. To overcome these drawbacks, many improvement strategies are being developed on the mechanisms of PSO, whereas there is little research exploring a priori information about historical data to improve the PSO performance in the SVR parameter selection task. In this paper, a novel method controlling the inertial weight of PSO is proposed to accelerate its convergence and guide the evolution out of local optima, by utilizing the analytical selection (AS) method based on a priori knowledge about SVR parameters. Experimental results show that the proposed ASPSO method is almost as accurate as the traditional PSO and outperforms it in convergence speed and ability in tuning SVR parameters. Therefore, the proposed ASPSO-SVR shows promising results for practical reliability prediction tasks.
机译：支持向量回归（SVR）是一种广泛用于可靠性预测的技术。高预测精度的关键问题是SVR参数的选择，这本质上是一个优化问题。作为最有效的进化优化方法之一，粒子群优化（PSO）已成功应用于调整SVR参数并显示出良好的性能。但是，PSO的固有缺点（包括收敛速度慢和局部最优）阻碍了其在实际可靠性预测问题中的进一步应用。为了克服这些缺点，正在针对PSO的机制开发许多改进策略，而很少有研究探索有关历史数据的先验信息以改善SVR参数选择任务中的PSO性能的研究。本文提出了一种新的控制PSO惯性权重的方法，该方法通过利用基于SVR参数先验知识的分析选择（AS）方法来加速PSO的收敛并指导局部最优解的演化。实验结果表明，提出的ASPSO方法几乎与传统PSO一样准确，并且在收敛速度和SVR参数调整能力方面均优于传统PSO。因此，提出的ASPSO-SVR在实际的可靠性预测任务中显示出可喜的结果。

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