基于改进的迁移率模型的生物地理学优化算法

Biogeography-based optimization algorithms based on improved migration rate models

生物地理学优化(BBO)算法通过迁移和变异不断更新栖息地,以寻找最优解,其中迁移率模型的优劣会直接影响算法的优化性能。针对原始BBO算法采用线性迁移率模型适应性不足的问题,基于Logistic函数、三次多项式函数以及双曲正切函数提出了三种新的非线性迁移率模型,并应用于原始BBO算法中。对17个典型的基准函数进行优化性能测试,结果表明,基于双曲正切函数的迁移率模型所得解更接近函数的全局最小值,总体表现优于原始线性迁移率模型的BBO算法以及相关改进算法中表现优异的余弦迁移率模型。稳定性测试结果表明,在不同的变异率下,基于双曲正切函数的迁移率模型在多数测试函数上表现优于原始线性迁移率模型。在满足解多样性的基础上,该模型能够较好地适应非线性迁移问题,提高寻优能力。

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.