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

Asmaa M. Khalid, Hanaa M. Hamza, Seyedali Mirjalili, Khaid M. Hosny

Artificial Intelligence Research and Optimization

Research output: Contribution to journal › Article › peer-review

9 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 language	English
Journal	Neural Computing and Applications
DOIs	https://doi.org/10.1007/s00521-023-08587-w
Publication status	Published - 2023

Keywords

Convergence
Coronavirus
Coverage
Dominance
Frameshifting
Multi-objective

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/s00521-023-08587-w

Cite this

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title = "MOCOVIDOA: a novel multi-objective coronavirus disease optimization algorithm for solving multi-objective optimization problems",

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",

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doi = "10.1007/s00521-023-08587-w",

language = "English",

journal = "Neural Computing and Applications",

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T2 - a novel multi-objective coronavirus disease optimization algorithm for solving multi-objective optimization problems

AU - Khalid, Asmaa M.

AU - Hamza, Hanaa M.

AU - Mirjalili, Seyedali

AU - Hosny, Khaid M.

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Convergence

KW - Coronavirus

KW - Coverage

KW - Dominance

KW - Frameshifting

KW - Multi-objective

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

Abstract

Keywords

UN SDGs

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