An enhanced POSTA based on Nelder-Mead simplex search and quadratic interpolation

TL;DR

This paper enhances the parameter optimal state transition algorithm (POSTA) by integrating Nelder-Mead simplex search and quadratic interpolation to improve convergence speed and solution accuracy in high-dimensional global optimization tasks.

Contribution

The paper introduces a novel enhancement to POSTA by incorporating Nelder-Mead and quadratic interpolation, effectively utilizing historical information for better optimization performance.

Findings

Enhanced POSTA outperforms original POSTA on benchmark functions.

The integrated method achieves faster convergence in high-dimensional spaces.

Experimental results show improved solution accuracy.

Abstract

State transition algorithm (STA) is a metaheuristic method for global optimization. Recently, a modified STA named parameter optimal state transition algorithm (POSTA) is proposed. In POSTA, the performance of expansion operator, rotation operator and axesion operator is optimized through a parameter selection mechanism. But due to the insufficient utilization of historical information, POSTA still suffers from slow convergence speed and low solution accuracy on specific problems. To make better use of the historical information, Nelder-Mead (NM) simplex search and quadratic interpolation (QI) are integrated into POSTA. The enhanced POSTA is tested against 14 benchmark functions with 20-D, 30-D and 50-D space. An experimental comparison with several competitive metaheuristic methods demonstrates the effectiveness of the proposed method.