A robust metaheuristic combining clonal colony optimization and population-based incremental learning methods

To provide a fast robust optimizer for numerical solutions of inverse problems, a metaheuristic combining a clonal colony optimization methodology and a population based incremental learning method is proposed. In the proposed algorithm, a real-valued probability vector is introduced for the extension of each colony; a tournament-based mechanism is employed in a colony to destruct/discard plants to evolve the colony toward a promising space; and a new reallocation operator is designed. The numerical results on two case studies are reported to positively showcase the feasibilities and merits of the proposed metaheuristic.