生物地理信息优化算法中迁移算子的改进

原生物地理信息优化算法主要通过迁移算子与变异算子实现群体的进化， 常被应用于求解单目标优化问题。 如果将原有的进化算子直接用于求解连续多目标优化问题，会严重影响群体的多样性。文中将原迁移算子进行改进， 引入扰动因子， 增强群体的多样性。并以此为基础，提出基于生物地理信息的多目标进化算法(BBMOEA)。 通过与原有迁移算子下的算法比较及各类型测试函数的实验， 结果验证改进迁移算子对于求解多目标优化问题是有效可行的。同时将BBMOEA与经典算法SPEA2和NSGA-Ⅱ进行比较， 结果表明BBMOEA所得Pareto解集在收敛的同时，具有较均匀的分布性。

Improvement for Migration Operator in Biogeography-Based Optimization Algorithm

In original biogeography-based optimization ( BBO ) ,the migration and mutation operators are applied to evolve the population. BBO is often used to solve single-objective optimization problems. When the original migration operator of BBO is applied to solve continuous multi-objective optimization problems,the diversity of the population is decreased sharply. In this paper,the migration operator of BBO is developed and the perturbation factor is introduced to increase the diversity of the population. Thus,a biogeography-based multi-objective evolutionary algorithm ( BBMOEA) is proposed. Compared with the algorithm under the action of the original migration operator on benchmark test problems,the simulation results illustrate the effectiveness and efficiency of the developed migration operator. Meanwhile,compared with SPEA 2 and NSGA-Ⅱ,the experimental results show that the solution set gained by algorithm BBMOEA has good convergence and even distribution.