  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, when considering social isolation measures and the high number
recovered and dead people, the number of infected people starts decreasing in
subsequent iterations. Furthermore, a parallel multi-virus version is proposed
in which several coronavirus strains evolve over time and explore wider search
space areas in less iterations. Finally, the metaheuristic has been combined
with deep learning models, in order to find optimal hyperparameters during the
training phase. As application case, the problem of electricity load time
series forecasting has been addressed, showing quite remarkable performance.
