一种自适应惯性权重的混合蛙跳算法

摘要

Because of the problems of Shuffled Frog Leaping Algorithm(SFLA) such as local optimality and slow convergence rate, an improved SFLA is presented. In this algorithm, frog population is initialized with opposition base learning to improve the quality of initial solution. Then the adaptive inertia weight is introduced to correct frog update strategy which can balance the global search and local search. Simulation results of experiments on the six classical function show that compared with the SFLA and ISFLA1, the new algorithm optimization ability can be stronger, the number of iterations less, the solution better, the suitable for high-dimensional optimization of complex functions more.%针对混合蛙跳算法(SFLA)易陷入局部最优、收敛速度慢的问题,提出一种改进的混合蛙跳算法.该算法用相对基学习法初始化青蛙群体,从而提高初始解的质量.通过引入自适应惯性权重修正青蛙的更新策略,可以平衡算法的全局搜索和局部搜索.对6个经典函数的仿真测试结果表明,该算法与SFLA和ISFLAl算法相比寻优能力强、迭代次数少、解的精度高,更适合高维复杂函数的优化.