Artemisinin optimization based on malaria therapy: Algorithm and applications to medical image segmentation

This study proposes an efficient metaheuristic algorithm called the Artemisinin Optimization (AO) algorithm. This algorithm draws inspiration from the process of artemisinin medicine therapy for malaria, which involves the comprehensive eradication of malarial parasites within the human body. AO comprises three optimization stages: a comprehensive eliminations phase simulating global exploration, a local clearance phase for local exploitation, and a post-consolidation phase to enhance the algorithm's ability to escape local optima. In the experimental, this paper conducts qualitative analysis experiments on the AO, explaining its characteristics in searching for the optimal solution. Subsequently, AO is then tested on the classical IEEE CEC 2014, and the latest IEEE CEC 2022 benchmark function sets to assess its adaptability to various function types. Comparative analyses are conducted against eight well-established algorithms and eight high-performance improved algorithms. Statistical analyses of convergence curves and qualitative metrics revealed AO's robust competitiveness. Lastly, the AO is incorporated into breast cancer pathology image segmentation applications. Using fifteen authentic medical images at six threshold levels, AO's segmentation performance is compared against eight distinguished algorithms. Experimental results demonstrated AO's superiority in terms of image segmentation accuracy, Feature Similarity Index (FSIM), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM) over the contrast algorithms. These comparative findings emphasize AO's efficacy and its potential in real-world optimization applications. The source codes of this paper will be available in https://aliasgharheidari.com/AO.html and other public websites.