A Robust Metaheuristic Based on Clonal Colony Optimization and Population Based Incremental Learning Methods

摘要

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