二阶卡尔曼滤波分布估计算法

分布估计算法由于其较强的理论基础已成为进化计算研究的新热点．从卡尔曼滤波的角度来看，它的作甩实际上是一个递归滤波器，但作用在一个种群上的分布估计算法相当于只有一个信息源．因此，该文利用信息融合的思想，将种群分成若干子种群，各子种群独立地使用二阶分布估计算法来估计其状态，这样就可从多个信息源获得信息．然后用卡尔曼滤波器将这多个信息源的信息相融合，以产生更准确的估计，并将估计信息反馈到各子种群中．实验结果表明，相对于已有的二阶分布估计算法，该文算法的稳定性和全局搜索能力都得到了很大提高，从而说明了该文算法的有效性．

Second Order Estimation of Distribution Algorithms Based on Kalman Filter

Estimation of Distribution Algorithms (EDAs) are new evolutionary algorithms based on probabilistic model and have become a new focus in the field of evolutionary computation. From the view point of Kalman filter, EDA actually is a filter with single sensor, so its stability is poor and it is prone to be trapped in the local optima of the objective functions. To overcome these disadvantages, authors enhance its performance with Kalman filtering technique and propose a new algorithm, second order estimation of distribution algorithm based on Kalman filter. In this method, population is divided into several sub-populations, and a second order EDA for each sub-population is used to estimate the information of its state. Then, a Kalman filter is used to fuse the information so that more accurate state can be obtained. Finally, the information fused is fed back to each sub-population. Experimental results demonstrate that the algorithm outperforms available second order algorithm greatly both in the stability and the global search ability.