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

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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.

Original languageEnglish
JournalNeural Computing and Applications
DOIs
Publication statusPublished - 2023

Keywords

  • Convergence
  • Coronavirus
  • Coverage
  • Dominance
  • Frameshifting
  • Multi-objective

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