带有选择和自适应变异机制的混合蛙跳算法

混合蛙跳算法易陷入局部最优，且收敛速度较慢。为此，提出一种带有选择和自适应变异机制的蛙跳算法。引入线性递减的动态惯性权重修正最差青蛙，按照一定的概率选择适应度值较优的青蛙代替较差青蛙，并对每只青蛙个体以不同概率进行自适应变异。仿真结果表明，该算法可以平衡全局搜索和局部搜索，寻优能力强、迭代次数少，解的精度较高，更适合高维复杂函数的优化。

Shuffled Frog Leaping Algorithm with Selection and Adaptive Mutation Mechanism

Because of the problems of Shuffled Frog Leaping Algorithm(SFLA) such as local optimality and slow convergence rate, a leapfrog algorithm with selection and adaptive mutation mechanism is presented. This algorithm introduces the linear decreasing adaptive inertia weight to correct the poor frog update strategy. It selects the frog with better fitness value to substitute the poor, and makes very frog adaptively mutate with different probability. Simulation results show that this algorithm can balance the global search and local search, and its optimization ability is stronger, the number of iterations is less, the solution is better, and the suitable for high-dimensional optimization of complex functions is more.