论文标题
通过筛查机制改善LSHADE
Improving LSHADE by means of a pre-screening mechanism
论文作者
论文摘要
事实证明，进化算法在连续优化方面非常有效，尤其是在可能进行大量适应性函数评估（FFE）时。但是，在某些情况下，必须采取一种昂贵的优化方法（即使用相对较少的FFE），并且在这项工作中考虑了这种设置。本文以预筛选机构（PSLShade）的形式引入了众所周知的Lshade算法的扩展。提出的预筛选依赖于以下三个组件：特定的初始采样过程，样品档案和适应性函数的全局线性元模型，该函数由6个独立变量组成。预筛选机制初步评估了试验向量，并指定了其中最好的一种，以进一步评估适应性功能。在昂贵的方案中，使用CEC2021基准评估PSLShade的性能，优化预算为10^2-10^4 FFE，每个维度为10^2-10^4 ffes。我们将PSLShade与基线Lshade方法和MADDE算法进行了比较。结果表明，有限制的优化预算，PSLShade明显胜过这两个竞争算法。此外，与Lshade相比，使用筛查机制的使用导致PSLShade的种群收敛更快。
Evolutionary algorithms have proven to be highly effective in continuous optimization, especially when numerous fitness function evaluations (FFEs) are possible. In certain cases, however, an expensive optimization approach (i.e. with relatively low number of FFEs) must be taken, and such a setting is considered in this work. The paper introduces an extension to the well-known LSHADE algorithm in the form of a pre-screening mechanism (psLSHADE). The proposed pre-screening relies on the three following components: a specific initial sampling procedure, an archive of samples, and a global linear meta-model of a fitness function that consists of 6 independent transformations of variables. The pre-screening mechanism preliminary assesses the trial vectors and designates the best one of them for further evaluation with the fitness function. The performance of psLSHADE is evaluated using the CEC2021 benchmark in an expensive scenario with an optimization budget of 10^2-10^4 FFEs per dimension. We compare psLSHADE with the baseline LSHADE method and the MadDE algorithm. The results indicate that with restricted optimization budgets psLSHADE visibly outperforms both competitive algorithms. In addition, the use of the pre-screening mechanism results in faster population convergence of psLSHADE compared to LSHADE.
