  Butterfly Optimization Algorithm (BOA) is a recent metaheuristic that has
been used in several optimization problems. In this paper, we propose a new
version of the algorithm (xBOA) based on the crossover operator and compare its
results to the original BOA and 3 other variants recently introduced in the
literature. We also proposed a framework for solving the unknown area
exploration problem with energy constraints using metaheuristics in both
single- and multi-robot scenarios. This framework allowed us to benchmark the
performances of different metaheuristics for the robotics exploration problem.
We conducted several experiments to validate this framework and used it to
compare the effectiveness of xBOA with wellknown metaheuristics used in the
literature through 5 evaluation criteria. Although BOA and xBOA are not optimal
in all these criteria, we found that BOA can be a good alternative to many
metaheuristics in terms of the exploration time, while xBOA is more robust to
local optima; has better fitness convergence; and achieves better exploration
rates than the original BOA and its other variants.
