  The Ebola virus and the disease in effect tend to randomly move individuals
in the population around susceptible, infected, quarantined, hospitalized,
recovered, and dead sub-population. Motivated by the effectiveness in
propagating the disease through the virus, a new bio-inspired and
population-based optimization algorithm is proposed. This paper presents a
novel metaheuristic algorithm named Ebola optimization algorithm (EOSA). To
correctly achieve this, this study models the propagation mechanism of the
Ebola virus disease, emphasising all consistent states of the propagation. The
model was further represented using a mathematical model based on first-order
differential equations. After that, the combined propagation and mathematical
models were adapted for developing the new metaheuristic algorithm. To evaluate
the proposed method's performance and capability compared with other
optimization methods, the underlying propagation and mathematical models were
first 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 functions
are investigated numerically. The results indicate that the performance of the
proposed algorithm is competitive with other state-of-the-art optimization
methods based on scalability analysis, convergence analysis, and sensitivity
analysis. Extensive simulation results indicate that the EOSA outperforms other
state-of-the-art popular metaheuristic optimization algorithms such as the
Particle Swarm Optimization Algorithm (PSO), Genetic Algorithm (GA), and
Artificial Bee Colony Algorithm (ABC) on some shifted, high dimensional and
large search range problems.
