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The Ebola virus and the disease in effect tend to randomly move individualsin the population around susceptible, infected, quarantined, hospitalized,recovered, and dead sub-population. Motivated by the effectiveness inpropagating the disease through the virus, a new bio-inspired andpopulation-based optimization algorithm is proposed. This paper presents anovel metaheuristic algorithm named Ebola optimization algorithm (EOSA). Tocorrectly achieve this, this study models the propagation mechanism of theEbola virus disease, emphasising all consistent states of the propagation. Themodel was further represented using a mathematical model based on first-orderdifferential equations. After that, the combined propagation and mathematicalmodels were adapted for developing the new metaheuristic algorithm. To evaluatethe proposed method's performance and capability compared with otheroptimization methods, the underlying propagation and mathematical models werefirst investigated to determine how they successfully simulate the EVD.Furthermore, two sets of benchmark functions consisting of forty-seven (47)classical and over thirty (30) constrained IEEE CEC-2017 benchmark functionsare investigated numerically. The results indicate that the performance of theproposed algorithm is competitive with other state-of-the-art optimizationmethods based on scalability analysis, convergence analysis, and sensitivityanalysis. Extensive simulation results indicate that the EOSA outperforms otherstate-of-the-art popular metaheuristic optimization algorithms such as theParticle Swarm Optimization Algorithm (PSO), Genetic Algorithm (GA), andArtificial Bee Colony Algorithm (ABC) on some shifted, high dimensional andlarge search range problems.

Ebola Optimization Search Algorithm (EOSA): A new metaheuristic algorithm based on the propagation model of Ebola virus disease