基于混合凸迁移和趋优柯西变异的对偶生物地理学优化算法

Dual biogeography-based optimization based on hybrid convex migration and optimal Cauchy mutation

通过分析生物地理学优化算法(BBO)性能的不足,提出了一种基于混合凸迁移和趋优柯西变异的对偶生物地理学优化算法(DuBBO)。在迁移算子中,采用动态的混合凸迁移算子,使算法能够快速地向最优解方向收敛;在变异机制中,采用趋优变异策略,并加入了柯西分布随机数帮助算法跳出局部最优解;最后将对偶学习策略集成到算法中,加快了算法收敛速度并提升了搜索能力。在23个benchmark函数上的实验结果证明了提出的三种改进策略的有效性和必要性。最后将DuBBO与BBO以及另外六种优秀的改进算法进行对比。实验结果表明,DuBBO在整体性能上最好、收敛速度更快、收敛精度更高。

By analyzing the performance of biogeography-based optimization(BBO),this paper proposed a dual biogeography-based optimization based on hybrid convex migration and optimal Cauchy mutation(DuBBO).The algorithm used the dynamic mixed convex migration operator made it converge to the optimal solution quickly.In the mutation mechanism,the algorithm used optimal mutation and Cauchy distribution random number to help the algorithm jump out of the local optimal solution.Finally,the algorithm added the dual learning strategy to speed up the convergence speed and improve ability.The experimental results on 23 benchmark functions show the effectiveness and necessity of the three strategies.Compared DuBBOwith BBO and other six excellent improved algorithms.The experimental results show that DuBBO has the best overall performance,faster convergence speed and higher convergence accuracy.