Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm

Nature computing has evolved with exciting performance to solve complex real-world combinatorial optimization problems. These problems span across engineering, medical sciences, and sciences generally. The Ebola virus has a propagation strategy that allows individuals in a population to move among s...

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Vydané v:IEEE access Ročník 10; s. 16150 - 16177
Hlavní autori: Oyelade, Olaide Nathaniel, Ezugwu, Absalom El-Shamir, Mohamed, Tehnan I. A., Abualigah, Laith
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Artificial neural networks
Benchmarks
Combinatorial analysis
constrained benchmark functions
convolutional neural network
Differential equations
Digital imaging
Diseases
Ebola virus
Genetic algorithms
Heuristic algorithms
Heuristic methods
Image classification
Mammography
Mathematical analysis
Mathematical models
Medical imaging
metaheuristic algorithm
Metaheuristics
Optimization
Optimization algorithms
optimization problems
Particle swarm optimization
Performance evaluation
Propagation
Search algorithms
Source code
Statistics
Strategy
Viral diseases
Viruses (medical)
ISSN:2169-3536, 2169-3536
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Shrnutí:Nature computing has evolved with exciting performance to solve complex real-world combinatorial optimization problems. These problems span across engineering, medical sciences, and sciences generally. The Ebola virus has a propagation strategy that allows individuals in a population to move among susceptible, infected, quarantined, hospitalized, recovered, and dead sub-population groups. Motivated by the effectiveness of this strategy of propagation of the disease, a new bio-inspired and population-based optimization algorithm is proposed. This study presents a novel metaheuristic algorithm named Ebola Optimization Search Algorithm (EOSA) based on the propagation mechanism of the Ebola virus disease. First, we designed an improved SIR model of the disease, namely SEIR-HVQD: Susceptible (S), Exposed (E), Infected (I), Recovered (R), Hospitalized (H), Vaccinated (V), Quarantine (Q), and Death or Dead (D). Secondly, we represented the new model using a mathematical model based on a system of first-order differential equations. A combination of the propagation and mathematical models was adapted for developing the new metaheuristic algorithm. To evaluate the performance and capability of the proposed method in comparison with other optimization methods, two sets of benchmark functions consisting of forty-seven (47) classical and thirty (30) constrained IEEE-CEC benchmark functions were investigated. The results indicate that the performance of the proposed algorithm is competitive with other state-of-the-art optimization methods based on scalability, convergence, and sensitivity analyses. Extensive simulation results show that the EOSA outperforms popular metaheuristic algorithms such as the Particle Swarm Optimization Algorithm (PSO), Genetic Algorithm (GA), and Artificial Bee Colony Algorithm (ABC). Also, the algorithm was applied to address the complex problem of selecting the best combination of convolutional neural network (CNN) hyperparameters in the image classification of digital mammography. Results obtained showed the optimized CNN architecture successfully detected breast cancer from digital images at an accuracy of 96.0%. The source code of EOSA is publicly available at https://github.com/NathanielOy/EOSA_Metaheuristic .
Bibliografia:ObjectType-Article-1
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3147821