Biogeography-Based Optimization (BBO) algorithm updates habitats through migration and mutation continuously to find the optimal solution, and the migration model affects the performance of the algorithm significantly. In view of the problem of insufficient adaptability of the linear migration model used in the original BBO algorithm, three nonlinear migration models were proposed. These models are based on Logistic function, cubic polynomial function and hyperbolic tangent function respectively. Optimization experiments were carried out on 17 typical benchmark functions, and results show that the migration model based on hyperbolic tangent function performs better than the linear migration model of original BBO algorithm and cosine migration model with good performance of improved algorithm. Stability test shows that the migration model based on hyperbolic tangent function performs better than the original linear migration model with different mutation rates on most test functions. The model satisfies the diversity of the solutions, and better adapts to the nonlinear migration problem with improved search ability.
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2019, Vol. 39 
