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

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 mechani...

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Vydáno v:Neural computing & applications Ročník 35; číslo 23; s. 17319 - 17347
Hlavní autoři: Khalid, Asmaa M., Hamza, Hanaa M., Mirjalili, Seyedali, Hosny, Khaid M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.08.2023
Springer Nature B.V
Témata:
Algorithms
Archives & records
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Coronaviruses
Data Mining and Knowledge Discovery
Decision making
Design engineering
Global optimization
Heuristic methods
Image Processing and Computer Vision
Multiple objective analysis
Objectives
Optimization algorithms
Optimization techniques
Original
Original Article
Probability and Statistics in Computer Science
Viral diseases
Dominance
Coverage
Coronavirus
Multi-objective
Frameshifting
Convergence
ISSN:0941-0643, 1433-3058
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Shrnutí: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.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-023-08587-w