# A Statistical Study on Parameter Selection of Operators in Continuous State Transition Algorithm

## TL;DR

This paper investigates optimal parameter selection for the continuous State Transition Algorithm (STA) using a statistical study on benchmark functions, leading to an improved STA that accelerates search and outperforms other metaheuristics.

## Contribution

It introduces a new parameter strategy for continuous STA based on statistical analysis, enhancing its efficiency and effectiveness in high-dimensional optimization.

## Key findings

- Optimal parameters significantly improve STA performance.
- The proposed STA outperforms other metaheuristics on benchmarks.
- Effective in high-dimensional spaces (20, 30, 50 dimensions).

## Abstract

State transition algorithm (STA) has been emerging as a novel metaheuristic method for global optimization in recent few years. In our previous study, the parameter of transformation operator in continuous STA is kept constant or decreasing itself in a periodical way. In this paper, the optimal parameter selection of the STA is taken in consideration. Firstly, a statistical study with four benchmark two-dimensional functions is conducted to show how these parameters affect the search ability of the STA. Based on the experience gained from the statistical study, then, a new continuous STA with optimal parameters strategy is proposed to accelerate its search process. The proposed STA is successfully applied to twelve benchmarks with 20, 30 and 50 dimensional space. Comparison with other metaheuristics has also demonstrated the effectiveness of the proposed method.