为了解决传统高斯混合模型（GMM）对初值敏感，在实际训练中极易得到局部最优参数的问题，提出了一种采用微粒群算法优化GMM参数的新方法。
The traditional training methods of Gaussian Mixture Model(GMM) are sensitive to the initial model parameters, which often leads to a local optimal parameter in practice.
为了保证获得最优水轮机PID调节器参数，本文研究了利用微粒群优化（PSO）算法进行参数优化设计的新方法。
To gain optimization parameters of hydro turbine PID governor, this paper interprets the approach of optimization designing that uses the Particle Swarm Optimization (PSO) algorithm.
提出了一种基于微粒群算法(PSO)的图像增强方法，把图像增强看作最优化问题。
In this study, a Particle Swarm optimization (PSO) approach to image enhancement is proposed, in which image enhancement is formulated as an optimization problem.
为了解决多重模态最优化问题，我们运用了一维离散优化方法、遗传算法和蚁群算法。
To solve the multimodal optimization problem the 1d discrete optimization methods, the genetic and Ant Colony algorithms are applied.
为了解决多重模态最优化问题，我们运用了一维离散优化方法、遗传算法和蚁群算法。
To solve the multimodal optimization problem the 1d discrete optimization methods, the genetic and Ant Colony algorithms are applied.
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