中文摘要：

针对基本蛙跳算法在处理复杂函数优化问题时求解精度低且易陷入局部最优的缺点,将共轭梯度法引入基本蛙跳算法,对排名靠前的p个模因组中的精英个体和排名靠后的q个模因组中的落后个体同时使用共轭梯度法进行更新,一方面增强对较差青蛙的指导能力,另一方面使最差的青蛙直接更新,提高了算法的收敛精度.所得混合蛙跳算法有效结合了基本蛙跳算法较强的全局搜索能力和共轭梯度法快速精确的局部搜索能力.将所得的混合蛙跳算法与其他智能优化算法进行对比,数值试验结果表明,无论从收敛精度还是进化代数而言,所得混合蛙跳算法较其他算法均有较大的改进,具有更高的收敛精度,能有效避免陷入局部最优,且优化结果更加稳定.

英文摘要：

A kind of hybrid Shuffled Frog Leaping Algorithm （SFLA） coupling with conjugate gradient method is proposed to solve the problem of low solution precision and easy to fall into local optimal solutions under basic SFLA. The elite individuals in the frontpmeme groups and the behind individuals in the lastqmeme groups are selected to search the solution with conjugate gradient method, which enhance the ability to guide the poor frogs in one hand, and in another hand update the worst frogs directly, and improve the convergence accuracy. The proposed hybrid SFLA algorithm combines the strongly global searing ability of SFLA with the fast local searching ability of conjugate gradient method. Comparisons among the hybrid SFLA and other well-known intelligent algorithms are made and the numerical experiment results show that, in terms of convergence or evolutionary generation, the improvements have been achieved greatly in the hybrid SFLA. The improved algorithm has a higher convergent precision, more stable optimal results, and better ability to jump out local optimal solution than other well-known intelligent algorithms.