Immune Plasma Algorithm: A Novel Meta-Heuristic for Optimization Problems.

Area of Science:

• Computational Biology
• Bioinformatics
• Optimization Algorithms

Background:

• The COVID-19 pandemic renewed interest in convalescent plasma therapy, a treatment utilizing antibodies from recovered patients.
• Convalescent plasma has a history of efficacy against various viral outbreaks, including influenza, MERS, SARS, and Ebola.
• The principle involves transfusing antibody-rich plasma to critically ill individuals to aid recovery.

Purpose of the Study:

• To model the immune plasma treatment approach using computational methods.
• To introduce and evaluate a novel meta-heuristic algorithm named the Immune Plasma (IP) algorithm.
• To assess the IP algorithm's performance on numerical benchmark and engineering optimization problems.

Main Methods:

• Development of the Immune Plasma (IP) meta-heuristic algorithm.
• Performance evaluation of the IP algorithm on a suite of numerical benchmark problems.
• Application of the IP algorithm to engineering optimization tasks, specifically electro-encephalography (EEG) signal noise minimization.

Main Results:

• The IP algorithm demonstrated strong performance across various numerical benchmark problems.
• Comparative analysis showed the IP algorithm outperformed several classical and state-of-the-art meta-heuristics.
• The algorithm proved effective in complex engineering optimization, notably in reducing noise in EEG measurements.

Conclusions:

• The newly developed Immune Plasma (IP) algorithm is a promising meta-heuristic optimization technique.
• The IP algorithm offers a robust approach for solving diverse computational and engineering challenges.
• Its effectiveness in EEG noise reduction highlights its potential in biomedical signal processing applications.