Improved Polar Lights Optimizer Algorithm using Chaotic Maps for Medical Image Segmentation

Abstract

This paper presents an improvement of the Polar Lights Optimizer (PLO) algorithm by integrating three chaotic maps. A dynamic diversification method based on chaotic systems enables the enhanced algorithm, known as Chaotic Polar Light Optimizer (CPLO), to better escape local minima and converge to globally optimal solutions. To evaluate the effectiveness of the proposed CPLO algorithm, we selected four test functions and a real problem, namely the segmentation of medical images. The performance of CPLO was compared with that of the PLO algorithm and three other well-known algorithms. Experimental findings validate the suggested algorithm’s robustness and efficacy in real-world situations while demonstrating improved convergence time and solution quality.