Frequently, metaheuristics algorithms are used in many scientific
and engineering problems optimization. However, these metaheuristics
come across an impossibility to escape from optimal solutions.
To equilibrate the balancing between exploitation and exploration,
several proposals, inspired in the behavior of animal
groups, have been developed. A recent metaheuristic is Crow Search
Algorithm (CSA). It is inspired in the intelligent behavior of
crows. This paper presents a novel CSA variant combined with
two strategies opposed to based-learning called Elite Opposition-
Based Learning and Generalized Opposition-Based Learning. To
evaluate the performance of the proposed algorithm we executed
two experiment groups that utilized twelve reference functions. In
the first group, the proposal was compared to a CSA variant and
other three algorithms. For the second experiment group, we compared
to other four algorithms and more one variant. After the execution
of experiments, we observed that the obtained numerical results
showed a superiority when compared to the other algorithms.