An Improved Hybridizing Biogeography-Based Optimization with Differential Evolution for Global Numerical Optimization

Biogeography-based optimization (BBO) is a new biogeography inspired algorithm. It mainly uses the biogeography-based migration operator to share the information among solution. Differential evolution (DE) is a fast and robust evolutionary algorithm for global optimization. In this paper, we applied an improved hybridization of BBO with DE approach, namely BBO/DE/GEN, for the global numerical optimization problems. BBO/DE/GEN combines the exploitation of BBO with the exploration of DE effectively and the migration operators of BBO were modified based on number of iteration to improve performance. And hence it can generate the promising candidate solutions. To verify the performance of our proposed BBO/DE/GEN, 6 benchmark functions with a wide range of dimensions and diverse complexities are employed. Experimental results indicate that our approach is effective and efficient. Compared with BBO and BBO/DE approaches, BBO/DE/GEN performs better, or at least comparably, in terms of the quality of the final solutions and the convergence rate.