MOCOVIDOA: a novel multi-objective coronavirus disease optimization algorithm for solving multi-objective optimization problems

Asmaa M Khalid; Hanaa M Hamza; Seyedali Mirjalili; Khaid M Hosny

doi:10.1007/s00521-023-08587-w

MOCOVIDOA: a novel multi-objective coronavirus disease optimization algorithm for solving multi-objective optimization problems

Neural Comput Appl. 2023 May 2:1-29. doi: 10.1007/s00521-023-08587-w. Online ahead of print.

Authors

Asmaa M Khalid¹, Hanaa M Hamza¹, Seyedali Mirjalili², Khaid M Hosny¹

Affiliations

¹ Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig, 44519 Egypt.
² Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, Brisbane, QLD 4006 Australia.

Abstract

A novel multi-objective Coronavirus disease optimization algorithm (MOCOVIDOA) is presented to solve global optimization problems with up to three objective functions. This algorithm used an archive to store non-dominated POSs during the optimization process. Then, a roulette wheel selection mechanism selects the effective archived solutions by simulating the frameshifting technique Coronavirus particles use for replication. We evaluated the efficiency by solving twenty-seven multi-objective (21 benchmarks & 6 real-world engineering design) problems, where the results are compared against five common multi-objective metaheuristics. The comparison uses six evaluation metrics, including IGD, GD, MS, SP, HV, and delta p ( $Δ P$ ). The obtained results and the Wilcoxon rank-sum test show the superiority of this novel algorithm over the existing algorithms and reveal its applicability in solving multi-objective problems.

Keywords: Convergence; Coronavirus; Coverage; Dominance; Frameshifting; Multi-objective.