Artemisinin Optimization based on Malaria Therapy: Algorithm and Applications to Medical Image Segmentation

Abstract: 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 a qualitative analysis experiment 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. 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 15 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 results emphasize AO's efficiency and its potential in real-world optimization applications. .

Artemisinin Optimization (AO): The Artemisinin Optimization (AO) algorithm draws inspiration from the treatment process of malaria with artemisinin. This algorithm models the phases of malaria treatment to address complex optimization problems. Below is an overview of the core components and mechanisms of AO.

Conceptual Foundation: AO emulates the malaria treatment process, including initial infection, symptom onset, medical consultation, treatment phases, and recovery. The algorithm mimics these stages to balance exploration and exploitation in the search for optimal solutions.

Algorithm Structure: AO operates through several distinct phases:

Comprehensive Elimination Phase: Models the initial high-dose treatment where search agents disperse to explore the solution space, simulating drug diffusion in the body.

Local Clearance Phase: Focuses on refining solutions locally, similar to the maintenance phase of malaria treatment.

Post-Consolidation Phase: Addresses potential recurrence of malaria by simulating the dormant phase of parasites, enhancing the algorithm's escape from local optima.

Summary of AO Stages: AO incorporates:

The AO algorithm, inspired by malaria treatment, balances global exploration and local exploitation across different treatment phases. It shows promise as an effective tool for optimization tasks, including medical image segmentation.