A new transformation into State Transition Algorithm for finding the global minimum

TL;DR

This paper introduces an axesion operator to improve the state transition algorithm's ability to find global minima, demonstrating enhanced performance on benchmark problems through numerical experiments.

Contribution

The paper proposes a novel axesion operator for the state transition algorithm, improving its global search capability and reliability.

Findings

Enhanced performance on benchmark minimization problems

Effective and reliable new transformation strategy

Improved ability to find global minima

Abstract

To promote the global search ability of the original state transition algorithm, a new operator called axesion is suggested, which aims to search along the axes and strengthen single dimensional search. Several benchmark minimization problems are used to illustrate the advantages of the improved algorithm over other random search methods. The results of numerical experiments show that the new transformation can enhance the performance of the state transition algorithm and the new strategy is effective and reliable.