引入生态扩张主义的改进生物地理学优化算法

针对生物地理学优化算法(biogeography-based optimization,BBO)前期搜寻范围不足、后期易陷入局部最优等问题,提出一种引入生态扩张主义(ecological imperialism,EI)的改进生物地理学优化算法(EI-BBO).首先,该算法通过在原始栖息地的周围寻找新栖息地,增强了初始化群体的多样性;其次,通过对栖息地进行改良式扩张,提高了算法后期的收敛效率;最后,通过梯度下降对最优解领域进行二次收敛,提高了算法的收敛精度.在CEC2014常用的12个优化测试函数上进行50次蒙特卡罗实验,结果表明无论是最优适应度值、平均适应度值还是标准差值EI-BBO,该算法总体表现均优于其他三种智能优化算法,说明EI-BBO能够提高寻找最优解的能力并提升搜索稳定性.

Improved BBO algorithms based on ecological imperialism

In order to solve the problems of BBO such as insufficient search scope in the early stage and easy to fall into local optimization in the later stage, this paper proposed an improved EI-BBO which based on EI. Firstly, the algorithm searched for new habitat around the original habitat, it enhanced the diversity of the initialization population. Secondly, the algorithm made improved Habitat expansion, it improved the convergence efficiency of the algorithm. Finally, the algorithm used gradient descent to make quadratic convergence in the field of optimal solution, which improved the convergence accuracy of the algorithm. This paper carried out 50 Monte Carlo experiments on 12 optimized test functions commonly used in CEC2014, the experimental results show that the overall performance of EI-BBO is better than the other three intelligent optimization algorithms in terms of optimal fitness value, average fitness value and standard deviation. It shows that EI-BBO can improve the ability to find the optimal solution and enhance the search stability.