ArticleThe Journal of supercomputing2022
Corona virus optimization (CVO): a novel optimization algorithm inspired from the Corona virus pandemic.
Article in The Journal of supercomputing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Hybrid arithmetic optimization algorithm for telecom network anomaly detection.Scientific reports · 2025Article
- A novel meta-heuristic algorithm based on candidate cooperation and competition.Scientific reports · 2025Article
- Optimal location of PMUs for full observability of power system using coronavirus herd immunity optimizer.Heliyon · 2024Article
- Greater cane rat algorithm (GCRA): A nature-inspired metaheuristic for optimization problems.Heliyon · 2024Article
- An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges.Artificial intelligence review · 2023Article
- Immunity-based Ebola optimization search algorithm for minimization of feature extraction with reduction in digital mammography using CNN models.Scientific reports · 2022Article
- A Systematic Review on Particle Swarm Optimization Towards Target Search in The Swarm Robotics Domain.Archives of computational methods in engineering : state of the art reviews · 2022Review
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2 authors.
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Abstract
This research introduces a new probabilistic and meta-heuristic optimization approach inspired by the Corona virus pandemic. Corona is an infection that originates from an unknown animal virus, which is of three known types and COVID-19 has been rapidly spreading since late 2019. Based on the SIR model, the virus can easily transmit from one person to several, causing an epidemic over time. Considering the characteristics and behavior of this virus, the current paper presents an optimization algorithm called Corona virus optimization (CVO) which is feasible, effective, and applicable. A set of benchmark functions evaluates the performance of this algorithm for discrete and continuous problems by comparing the results with those of other well-known optimization algorithms. The CVO algorithm aims to find suitable solutions to application problems by solving several continuous mathematical functions as well as three continuous and discrete applications. Experimental results denote that the proposed optimization method has a credible, reasonable, and acceptable performance.
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