差分迁移和趋优变异的生物地理学优化算法

Biogeography-based Optimization with Differential Migration and Global-best Mutation

为增强生物地理学优化(Biogeography-based optimization,BBO)算法的优化性能,降低其运行时间,提出了一种差分迁移和趋优变异的生物地理学优化算法(DGBBO).首先,将两种差分扰动操作与BBO算法的迁移操作有机融合,形成差分迁移算子,提升全局搜索能力并平衡探索和开采;其次,将趋优操作融入到BBO算法的变异算子中,替换原变异操作,形成趋优变异算子,克服了原变异算子存在的缺陷,加快收敛速度;此外,还从多个角度降低算法的计算复杂度.在一组常用的基准函数上进行了仿真实验,实验结果表明,相较于其它state-of-the-art算法,DGBBO算法寻优能力显著,稳定性强,收敛速度快,运行时间少,验证了其优秀的优化性能.

In order to enhance the optimization performance and reduce the runtime of the biogeography-based optimization（ BBO） algorithm,an improved biogeography-based optimization algorithm with differential migration and global-best mutation（ DGBBO） is presented. Firstly,the two differential disturbance operations are blended with BBO＇s migration operation to generate the differential migration operator. The differential migration operator can improve the global searching ability and balance the exploration and exploitation.Secondly,the global-best operation is merged into BBO＇s mutation operator instead of the original mutation operation to generate the global-best mutation operator. The global-best operator can overcome the defects of the original mutation operator and accelerate convergence speed. In addition,the computation complexity of the algorithm is reduced from several aspects. A large number of simulation experiments are made on a set of common benchmark functions. The experiment results show that,compared with the other state-of-the-art algorithms,DGBBO performs more significant optimization ability,stronger stability,faster convergence speed and less runtime. So DGBBO＇s excellent optimization performance is verified.