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首页> 外文会议>ICCCI 2010;International conference on computer and computational intelligence >Cooperative Estimation of Distribution Algorithm for Continuous Problems Solving
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Cooperative Estimation of Distribution Algorithm for Continuous Problems Solving

机译：连续问题求解的分布算法协同估计

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摘要

Since the estimation of distribution algorithm (EDA) was introduced, different approaches for continuous problems solving have been developed. Through the efforts of many scholars, the EDAs brought some success in continuous domain, but there are still some deficiencies: Gaussian-based EDA didn't perform well in problems with many optima, such as Schwefel function; histogram-based EDA didn't perform well in problems with singular global optimum, such as Three-Peak function. Because of this, based on improved histogram-based EDA and referred to the idea of k-Means clustering, we built cooperative EDA. Experimental results showed that the improved algorithm in this paper can give comparable with or better performance than those improved algorithms.
机译：由于引入了分布估计算法（EDA），因此已经开发了解决连续问题的不同方法。在许多学者的努力下，EDA在连续领域取得了一些成功，但是仍然存在一些不足：基于高斯的EDA在诸如Schwefel函数之类的许多最优问题上表现不佳。基于直方图的EDA在具有奇异全局最优的问题（例如三峰函数）中表现不佳。因此，基于改进的基于直方图的EDA并参考k-Means聚类的思想，我们构建了协作式EDA。实验结果表明，本文提出的改进算法与改进算法相比具有更好的性能。

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