Biogeography-based optimization with ensemble of migration models for global numerical optimization

Biogeography-based optimization (BBO) is a new evolutionary algorithm inspired by biogeography. BBO has demonstrated good performance on various benchmark functions and real-world optimization problems. However, the performance of BBO is sensitive to the migration model which provides the most important control parameters, immigration rate and emigration rate. According to no free lunch theorem, it is impossible for BBO with a single migration model to obtain always good performance. In this paper, BBO with an ensemble of migration models (BBO-EMM) is introduced and is realized using in three parallel populations. The performance is tested on a set of 25 benchmark functions of CEC 2005 and compared with variant versions of BBO with a single migration model with respect to optimization ability and running time. Results show that the proposed BBO-EMM is better than other BBO algorithms for the problems that we studied in this paper.