t-分布扰动策略和变异策略的花授粉算法

为了解决传统花授粉算法(FPA)收敛速度慢、易陷入局部最优、寻优精度低等缺陷,提出了一种t-分布扰动策略和变异策略的花授粉算法(t MFPA).首先利用混沌映射初始化花朵个体的位置,然后在全局授粉过程中,利用t-分布扰动的随机个体和莱维飞行共同实现个体位置更新,加快收敛速度的同时提高搜索空间的多样性;在局部授粉过程中,加入具有两个差分向量的变异策略和小概率策略,结合两种策略使算法能够跳出局部最优.实验结果表明,t MFPA相比于FPA和其他启发式智能算法具有更好的寻优精度和收敛速度,相对于其他改进算法具有更好的收敛性能.

Flower Pollination Algorithm Based on t-distribution Perturbation Strategy and Mutation Strategy

In order to solve the shortcomings of flower pollination algorithm(FPA)such as slow convergence,easy to fall into local optimum,and low optimization accuracy,a flower pollination algorithm based on t-distribution perturbation strategy and mutation strategy(t MFPA)was proposed.Firstly,chaotic mapping was used to initialize the position of individual flowers.Then in the global pollination process,random individuals with t-distribution perturbations and Levy flight were used to jointly update the individual position,which speeded up the convergence rate and increased the diversity of search space;during the local pollination process,the mutation strategy with two difference vectors and the small probability strategy were added to make the algorithm jump out of local optimization.The experimental results show that tMFPA has better optimization accuracy and convergence speed than FPA and other heuristic intelligent algorithms,and has better convergence performance than other improved FPA algorithms.