一种改进的混合蛙跳算法及其收敛性分析

为了提高混合蛙跳算法（SFLA）求解函数优化问题的能力，借鉴PSO与DE的进化算子提出了一种改进的混合蛙跳算法（ESFLA），分析了ESFLA的时间复杂性，并基于有限Markov链证明了ESFLA的全局收敛性。对ESFLA、SFLA与ISFLA2的仿真计算结果表明，ESFLA比SFLA和ISFLA2更适用于求解复杂的函数优化问题。

Improved shuffled frog-leaping algorithm and its convergent analysis

To improve the ability of shuffled frog-leaping algorithm（SFLA）for solving function optimization problems,an improved efficiently shuffled frog-leaping algorithm（ESFLA） is proposed,which adopts the evolutionary methods of particle swarm optimization and differential evolution,and its time complexity is analysed.The global convergence of ESFLA is proved by using limit Markov chain.In order to test and verify the ability of ESFLA for solving the function optimization problems,the performance of ESFLA is compared with that of SFLA and ISFLA2.The experimental results indicate that the performance of ESFLA is superior to SFLA and ISFLA2,and it is more suitable for solving complex function optimization problems.