TPAM: A Simulation-Based Model for Quantitatively Analyzing Parameter Adaptation Methods

TL;DR

This paper introduces TPAM, a simulation framework for quantitatively analyzing parameter adaptation methods in differential evolution algorithms, providing insights into their performance and behavior.

Contribution

The paper presents a novel TPAM simulation framework for evaluating and understanding parameter adaptation methods in differential evolution algorithms.

Findings

TPAM effectively measures PAM tracking performance.

SHADE's PAM outperforms JADE's in the simulation.

EPSDE's PAM fails under certain conditions.

Abstract

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.