Abstract
The recent global health crisis also known as the COVID-19 or coronavirus pandemic has attracted the researchers’ attentions to a treatment approach called immune plasma or convalescent plasma once more again. The main idea lying behind the immune plasma treatment is transferring the antibody rich part of the blood taken from the patients who are recovered previously to the critical individuals and its efficiency has been proven by successfully using against great influenza of 1918, H1N1 flu, MERS, SARS and Ebola. In this study, we modeled the mentioned treatment approach and introduced a new meta-heuristic called Immune Plasma (IP) algorithm. The performance of the IP algorithm was investigated in detail and then compared with some of the classical and state-of-art meta-heuristics by solving a set of numerical benchmark problems. Moreover, the capabilities of the IP algorithm were also analyzed over complex engineering optimization problems related with the noise minimization of the electro-encephalography signal measurements. The results of the experimental studies showed that the IP algorithm is capable of obtaining better solutions for the vast majority of the test problems compared to other commonly used meta-heuristic algorithms.
Highlights
Real world problems or complex engineering designs usually require finding optimum values for hundreds or thousands of decision parameters
Even though the results found by the Immune Plasma (IP) algorithm are better or equal to the results of the AMO for eleven of thirteen problems, they are not enough to generate a statistically significant difference
The convalescent plasma or immune plasma is one of the well-known treatment methods based on transferring the antibodies of an individual who has recovered previously to another patient or patients of the same infection
Summary
Real world problems or complex engineering designs usually require finding optimum values for hundreds or thousands of decision parameters. One of the most popular swarm-intelligence based meta-heuristics is Ant Colony Optimization (ACO) algorithm [10]. ACO algorithm is related with the ants and uses source finding and communication capabilities of them Another important swarm-intelligence based algorithm was proposed by Eberhart and Kennedy and named as Particle Swarm Optimization (PSO) algorithm [11]. Yang introduced a new swarm-intelligence based meta-heuristic called Firefly. As an expected result of this situation, designing new meta-heuristic algorithm after analyzing work-flow of an intelligent organizations of nature still protects its importance for further advances in computer, information and other engineering disciplines. Experimental studies carried out with a set of different optimization problems showed that IP algorithm is capable of obtaining better solutions for most of the problems compared to the other meta-heuristics.
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