  This thesis studies the domain of collective robotics, and more particularly
the optimization problems of multirobot systems in the context of exploration,
path planning and coordination. It includes two contributions. The first one is
the use of the Butterfly Optimization Algorithm (BOA) to solve the Unknown Area
Exploration problem with energy constraints in dynamic environments. This
algorithm was never used for solving robotics problems before, as far as we
know. We proposed a new version of this algorithm called xBOA based on the
crossover operator to improve the diversity of the candidate solutions and
speed up the convergence of the algorithm. The second contribution is the
development of a new simulation framework for benchmarking dynamic incremental
problems in robotics such as exploration tasks. The framework is made in such a
manner to be generic to quickly compare different metaheuristics with minimum
modifications, and to adapt easily to single and multi-robot scenarios. Also,
it provides researchers with tools to automate their experiments and generate
visuals, which will allow them to focus on more important tasks such as
modeling new algorithms. We conducted a series of experiments that showed
promising results and allowed us to validate our approach and model.
