TPAM: A Simulation-Based Model for Quantitatively Analyzing Parameter Adaptation Methods
This work addresses a specific problem for researchers in evolutionary computation by providing a tool for incremental analysis of parameter adaptation methods, though it is domain-specific and does not claim broad SOTA impact.
The paper tackles the lack of understanding in Parameter Adaptation Methods (PAMs) for adaptive Differential Evolution algorithms by proposing the TPAM simulation framework to quantitatively evaluate their tracking performance against predefined target parameters. It demonstrates TPAM's utility by analyzing five adaptive DEs, revealing insights such as why SHADE's PAM outperforms JADE's and conditions under which EPSDE's PAM fails.
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.