High-dimensional normalized data profiles for testing derivative-free optimization algorithms.

ABSTRACT: This article provides a new tool for examining the efficiency and robustness of derivative-free optimization algorithms based on high-dimensional normalized data profiles that test a variety of performance metrics. Unlike the traditional data profiles that examine a single dimension, the proposed data profiles require several dimensions in order to analyze the relative performance of different optimization solutions. To design a use case, we utilize five sequences (solvers) of trigonometric simplex designs that extract different features of non-isometric reflections, as an example to show how various metrics (dimensions) are essential to provide a comprehensive evaluation about a particular solver relative to others. In addition, each designed sequence can rotate the starting simplex throu

SUBMITTER: Musafer H 

PROVIDER: S-EPMC9454945 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature