COVIDOA: a novel evolutionary optimization algorithm based on coronavirus disease replication lifecycle

Asmaa M. Khalid, Khalid M. Hosny, Seyedali Mirjalili

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
JournalNeural Computing and Applications
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • Best cost
  • Convergence
  • Coronavirus
  • Evolutionary algorithm
  • Frameshifting
  • Optimization

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