An Improved Differential Evolution Biogeography-Based Optimization Algorithm

In order to further improve the performance of biogeography-Based optimization algorithm (BBO), an improved differential evolution biogeography optimization algorithm is proposed. By combining the search ability of differential evolution algorithm with the utilization of biogeography optimization algorithm, the elite retention mechanism is adopted to retain the individuals with high fitness, and the inertia weight strategy is introduced to adjust the proportion of mutation operation in the hybrid migration operation to improve the global search ability of the algorithm, and then the small probability perturbation is added to prevent the algorithm from changing with the iteration It is found that the optimal solution is local. Finally, six test functions are used for experiments, and the results show that the improved algorithm is better than other algorithms in optimization results, convergence speed and stability.