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 a propagation strategy that allows individuals in population move among susceptible, infected, quarantined, hospitalized, recovered, dead sub-population groups. Motivated by the effectiveness of this disease, new bio-inspired population-based algorithm is proposed. This study presents novel metaheuristic named Optimization Search Algorithm (EOSA) based on mechanism disease. First, we designed an improved SIR model namely SEIR-HVQD: Susceptible (S), Exposed (E), Infected (I), Recovered (R), Hospitalized (H), Vaccinated (V), Quarantine (Q), Death or Dead (D). Secondly, represented using mathematical system first-order differential equations. A combination models was adapted for developing algorithm. To evaluate capability proposed method comparison other methods, two sets benchmark functions consisting forty-seven (47) classical thirty (30) constrained IEEE-CEC were investigated. results indicate competitive state-of-the-art methods scalability, convergence, sensitivity analyses. Extensive simulation show EOSA outperforms popular algorithms such as Particle Swarm (PSO), Genetic (GA), Artificial Bee Colony (ABC). Also, applied address problem selecting best convolutional neural network (CNN) hyperparameters image classification digital mammography. Results obtained showed optimized CNN architecture successfully detected breast cancer from images at accuracy 96.0%. source code publicly available https://github.com/NathanielOy/EOSA_Metaheuristic .