Coronavirus Optimization Algorithm: A bioinspired metaheuristic based on the COVID-19 propagation model

Mart'inez-'Alvarez, F.; Asencio-Cort'es, G.; Torres, J. F.; Guti'errez-Avil'es, D.; Melgar-Garc'ia, L.; P'erez-Chac'on, R.; Rubio-Escudero, C.; Riquelme, J. C.; Troncoso, A.

A novel bioinspired metaheuristic is proposed in this work, simulating how the Coronavirus spreads and infects healthy people. From an initial individual (the patient zero), the coronavirus infects new patients at known rates, creating new populations of infected people. Every individual can either die or infect and, afterwards, be sent to the recovered population. Relevant terms such as re-infection probability, super-spreading rate or traveling rate are introduced in the model in order to simulate as accurately as possible the coronavirus activity. The Coronavirus Optimization Algorithm has two major advantages compared to other similar strategies. First, the input parameters are already set according to the disease statistics, preventing researchers from initializing them with arbitrary values. Second, the approach has the ability of ending after several iterations, without setting this value either. Infected population initially grows at an exponential rate but after some iterations, the high number recovered and dead people starts decreasing the number of infected people in new iterations. As application case, it has been used to train a deep learning model for electricity load forecasting, showing quite remarkable results after few iterations.