COVIDOA: a novel evolutionary optimization algorithm based on coronavirus disease replication lifecycle
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Hosny, Khalid M
Mirjalili, Seyedali
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This paper presents a novel bio-inspired optimization algorithm called Coronavirus Optimization Algorithm (COVIDOA). COVIDOA is an evolutionary search strategy that mimics the mechanism of coronavirus when hijacking human cells. COVIDOA is inspired by the frameshifting technique used by the coronavirus for replication. The proposed algorithm is tested using 20 standard benchmark optimization functions with different parameter values. Besides, we utilized five IEEE Congress of Evolutionary Computation (CEC) benchmark test functions (CECC06, 2019 Competition) and five CEC 2011 real-world problems to prove the proposed algorithm's efficiency. The proposed algorithm is compared to eight of the most popular and recent metaheuristic algorithms from the state-of-the-art in terms of best cost, average cost (AVG), corresponding standard deviation (STD), and convergence speed. The results demonstrate that COVIDOA is superior to most existing metaheuristics.
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Neural Computing and Applications
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The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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Epidemiology
Artificial intelligence
Computer vision and multimedia computation
Machine learning
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science
Coronavirus
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Khalid, AM; Hosny, KM; Mirjalili, S, COVIDOA: a novel evolutionary optimization algorithm based on coronavirus disease replication lifecycle, Neural Computing and Applications, 2022