Ebola Optimization Search Algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems

Deriving computational solutions to address complex life problems is now a gaining research focus. This is because such a computational approach is capable of modeling algorithms after natural phenomenon to find an optimal solution. In this paper, a nature-inspired and biology-based algorithm was inspired by the natural and biological process of Ebola. We formulated the propagation of the disease using a mathematical and SIR-model. Thereafter, an algorithmic design describing the procedure for the optimization process was done. The resulting Ebola optimization search algorithm (EOSA) was evaluated using the popular IEEE CEC functions' benchmark functions. The outcome of exhaustive experimentation with EOSA showed that the metaheuristic algorithm achieved a state-of-the-art performance compared with similar algorithms. Results confirmed that the scalability analysis, convergence analysis, and sensitivity analysis were competitive when compared with evolutionary-based and swarm-based algorithms.