基于高斯变异的生物地理学优化模型

Biogeography-Based Optimization Model Based on Gaussian Mutation

生物地理学优化是一种新型群体智能算法,具有较好的应用前景。针对算法中两大基本算子之一的变异算子进行研究,为了进一步提高优化模型的精度,给出关于高斯变异的生物地理学优化模型。同时介绍了算法的基本原理,重点分析了算法中的变异策略,采用多个测试函数进行仿真。仿真结果表明,在相同的迁移模型下,不同的变异策略对算法优化性能有较大影响,高斯变异策略的优化性能优于随机变异策略。实验还表明栖息地数量对于算法的优化能力也有较大的影响。

The Biogeography-Based Optimization（BBO） is a new swarm intelligence algorithm which has shown impressive performance and been applicated in many fields.To improve the accuracy of the BBO,the mutation strategy which is one of the two basic operators was researched,and the BBO based on Gaussian mutation strategy was given. The basic principles and processes of BBO were described,and the mutation strategy of BBO was analyzed.Several test functions were used for simulation experiments,the experimental results indicate that different mutation strategies with the same migration rate models in BBO have a greater impact on the algorithm to optimize performance.The algorithm with Gaussian mutation strategy optimizes the performance better than the algorithm with random mutation strategy.The experimental results also show that the number of habitats results in significant changes in performance.