利用存档进化方向增强高斯分布估计算法
Enhancing Gaussian Estimation of Distribution Algorithm by Exploiting Evolution Direction with Archive
摘要：
分布估计算法（EDA）作为一种典型的基于模型的进化算法（EA），具有独特的特点，在全局优化中得到了广泛的应用。然而，常用的高斯EDA（GEDA）经常遭受早熟收敛，严重限制了其搜索效率。本研究首先系统分析了传统GEDA的不足之处，然后尝试利用其进化方向来提高其性能，最后开发了一种新的GEDA变体EDA 2。在估计高斯模型时，EDA 2不是只利用当前生成的一些好的解，而是将前几代生成的一定数量的高质量解保存到档案中，并利用这些历史解来辅助估计高斯模型的协方差矩阵。通过这种方式，隐藏在档案中的进化方向信息被自然地集成到估计的模型中，这反过来又可以引导EDA 2朝向更有希望的解区域。此外，新的估计方法显着减少了EDA 2的人口规模，因为它需要更少的个人在当前的人口模型估计。结果，可以实现快速收敛。为了验证EDA 2的效率，我们在各种基准函数上对其进行了测试，并将其与几种最先进的EA进行了比较，包括IPOP-CMAES，AMaLGaM，三种高性能DE算法和一种新的PSO算法。实验结果表明，EDA 2是有效的和竞争力。
英文摘要：
As a typical model-based evolutionary algorithm (EA), estimation of distribution algorithm (EDA) possesses unique characteristics and has been widely applied to global optimization. However, the common-used Gaussian EDA (GEDA) usually suffers from premature convergence which severely limits its search efficiency. This study first systematically analyses the reasons for the deficiency of the traditional GEDA, then tries to enhance its performance by exploiting its evolution direction, and finally develops a new GEDA variant named EDA2. Instead of only utilizing some good solutions produced in the current generation when estimating the Gaussian model, EDA2 preserves a certain number of high-quality solutions generated in previous generations into an archive and takes advantage of these historical solutions to assist estimating the covariance matrix of Gaussian model. By this means, the evolution direction information hidden in the archive is naturally integrated into the estimated model which in turn can guide EDA2 towards more promising solution regions. Moreover, the new estimation method significantly reduces the population size of EDA2 since it needs fewer individuals in the current population for model estimation. As a result, a fast convergence can be achieved. To verify the efficiency of EDA2, we tested it on a variety of benchmark functions and compared it with several state-of-the-art EAs, including IPOP-CMAES, AMaLGaM, three high-powered DE algorithms, and a new PSO algorithm. The experimental results demonstrate that EDA2 is efficient and competitive.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
