A novel hybrid biogeography-based optimization with differential mutation

Biogeography-based optimization (BBO) is a new biogeography inspired algorithm. As a novel evolutionary computing technique, BBO is simple and effective, which is paid wide attention in both academic and industry fields and achieves many successful applications. Meanwhile, Differential Evolution (DE) is a fast and robust evolutionary algorithm for global optimization. It has been widely used in many areas. In this paper, we propose a hybrid BBO with DE, namely Differential BBO (DBBO), which employs the mutation operator of DE. DBBO combines the exploration of DE with the exploitation of BBO effectively, and hence it can generate the promising candidate solutions. To verify the performance of our proposed DBBO, 10 benchmark functions with 30 dimensions are employed. Experimental results indicate that DBBO is most effective when compared with BBO and DE.