论文标题
基于估计收敛点，使用高斯采样加速差分进化算法
Accelerating differential evolution algorithm with Gaussian sampling based on estimating the convergence points
论文作者
论文摘要
在本文中，我们提出了一个简单的策略，用于通过平均估计精英子群来估算收敛点。基于这个想法，我们得出了两种方法，它们是普通的平均策略，并加权平均策略。我们还设计了一个具有估计收敛点的平均值的高斯采样操作员，并具有一定的标准偏差。该操作员与传统的差分进化算法（DE）结合使用，以加速收敛。数值实验表明，我们的建议可以在CEC2013套件上的28个低维测试功能的大多数功能上加速DE，并且我们的建议很容易扩展，以与其他基于人群的进化算法结合使用，并具有简单的修改。
In this paper, we propose a simple strategy for estimating the convergence point approximately by averaging the elite sub-population. Based on this idea, we derive two methods, which are ordinary averaging strategy, and weighted averaging strategy. We also design a Gaussian sampling operator with the mean of the estimated convergence point with a certain standard deviation. This operator is combined with the traditional differential evolution algorithm (DE) to accelerate the convergence. Numerical experiments show that our proposal can accelerate the DE on most functions of 28 low-dimensional test functions on the CEC2013 Suite, and our proposal can easily be extended to combine with other population-based evolutionary algorithms with a simple modification.
