论文标题
TPAM：用于定量分析参数适应方法的基于仿真的模型
TPAM: A Simulation-Based Model for Quantitatively Analyzing Parameter Adaptation Methods
论文作者
论文摘要
尽管已经提出了大量的自适应差异进化（DE）算法，但其参数适应方法（PAM）尚不清楚。我们提出了一个基于目标函数的PAM模拟（TPAM）框架，用于评估PAM的跟踪性能。所提出的TPAM仿真框架测量了PAM跟踪预定目标参数的能力，从而对PAM的适应性行为进行定量分析。我们在拟议的TPAM上评估了广泛使用的五个自适应DES（JDE，EPSDE，JADE，MDE和SHADE）的PAM的跟踪性能，并表明TPAM可以对PAM提供重要的见解，例如，为什么Shade的PAM比Jade的PAM比Jade的表现更好，并且在什么情况下表现出了epsde pamertation parameTers parameTation parameTation parameTation parameTation parameTation。
While a large number of adaptive Differential Evolution (DE) algorithms have been proposed, their Parameter Adaptation Methods (PAMs) are not well understood. We propose a Target function-based PAM simulation (TPAM) framework for evaluating the tracking performance of PAMs. The proposed TPAM simulation framework measures the ability of PAMs to track predefined target parameters, thus enabling quantitative analysis of the adaptive behavior of PAMs. We evaluate the tracking performance of PAMs of widely used five adaptive DEs (jDE, EPSDE, JADE, MDE, and SHADE) on the proposed TPAM, and show that TPAM can provide important insights on PAMs, e.g., why the PAM of SHADE performs better than that of JADE, and under what conditions the PAM of EPSDE fails at parameter adaptation.
