A Comparative Study of STA on Large Scale Global Optimization

TL;DR

This paper evaluates the performance of the standard continuous State Transition Algorithm (STA) on large-scale global optimization problems, demonstrating its superior search ability compared to other evolutionary algorithms.

Contribution

It provides a comprehensive comparison of STA with other algorithms for large-scale problems, highlighting its effectiveness and robustness.

Findings

STA performs well on large-scale problems

STA's global search ability surpasses competitors

Standard continuous STA is effective for problems up to 100 dimensions

Abstract

State transition algorithm has been emerging as a new intelligent global optimization method in recent few years. The standard continuous STA has demonstrated powerful global search ability for global optimization problems whose dimension is no more than 100. In this study, we give a test report to present the performance of standard continuous STA for large scale global optimization when compared with other state-of-the-art evolutionary algorithms. From the experimental results, it is shown that the standard continuous STA still works well for almost all of the test problems, and its global search ability is much superior to its competitors.