A novel hybrid algorithm based on Biogeography-Based Optimization and Grey Wolf Optimizer

In order to obtain a Biogeography-Based Optimization (BBO) algorithm with strong universal applicability, this paper presents a novel hybrid algorithm based on BBO and Grey Wolf Optimizer (GWO), named HBBOG. Firstly, BBO and GWO are improved respectively. For BBO, the mutation operator is got rid of and a differential mutation operation is merged into the migration operator to enhance the global search ability. The original migration operation is replaced by a multi-migration operation to enhance the local search ability. For GWO, the opposition-based learning approach is merged to prevent the algorithm from falling into the local optima to some degree. Then, the improved BBO and the opposition learning based GWO are hybridized by a new strategy, named single-dimensional and all-dimensional alternating strategy, to formulate HBBOG. HBBOG can effectively maximize the two algorithms' advantages and overall balance exploration and exploitation, therefore, it can obtain strong universal applicability. We make a large number of experiments on a set of various kinds of benchmark functions and CEC2014 test set and apply HBBOG to clustering optimization. The experimental results show that HBBOG outperforms quite a few state-of-the-art algorithms. (C) 2018 Elsevier B.V. All rights reserved.