融合模拟退火机制的自适应花朵授粉算法

针对基本花朵授粉算法存在的不足,为提高其收敛速度与寻优精度,提出一种融合模拟退火机制的并且根据迭代进化来动态调整全局步长和局部繁衍概率的自适应花朵授粉算法。首先,在基本算法的全局授粉莱维飞行中使用变形指数函数的缩放因子来控制步长,使得花朵个体随迭代次数的增加自适应地进行位置更新；然后,通过瑞利分布函数结合迭代次数对繁衍概率影响因子进行改进,使得在避免早熟收敛的同时能够在后期向着最优解靠近；最后,在已改进的花朵授粉算法中融入模拟退火降温操作,这不仅增加了种群的多样性,而且改善了算法的整体寻优性能。仿真结果表明,改进后的算法 具有较快的收敛速度和较高的收敛精度,寻优性能得到了显著提高。

Adaptive Flower Pollination Algorithm with Simulated Annealing Mechanism

Aiming at the shortages of basic flower pollination algorithm,in order to improve the convergence rate and optimization accuracy of the algorithm,this paper proposed an adaptive flower pollination algorithm fusing simulated annealing mechanism and dynamically adjusting the global step length and local reproduction probability according to the iterative evolution.Firstly,the scaling factor of the deformed exponential function is used to control step length in the global pollination of the basic algorithm,so that the individual of flower can be adaptively updated with the number of iterations.Then,through combining Rayleigh distribution function and the number of iterations,the factors of multiplication probability are improved,thus avoiding the precocious convergence and making the solution close to the optimal solution in the later stage.Finally,a simulated annealing cooling operation is incorporated into the improved flower pollination algorithm,which not only increases the diversity of population,but also improves the overall performance of algorithm.The simulation results show that the algorithm has faster convergence speed and higher convergence precision,and the optimization performance of the proposed algorithm is improved.