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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中,对排名靠前的几个模因组中的精英个体使用共轭梯度法进行更新,增强对较差青蛙的指导能力.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.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智能优化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.
Aiming at the shortcomings of the basic frog leaping algorithm in solving complex function optimization problems with low precision and easy falling into local optimum, the conjugate gradient method is introduced into the basic frog leaping algorithm, and the elitist individuals The conjugate gradient method is used to update and improve the ability to guide the poorer frogs.The obtained mixed frog leaping algorithm effectively combines the strong global search ability of the basic frog leaping algorithm with the fast and accurate local search ability of the conjugate gradient method.The numerical results The results show that the proposed hybrid frog leapfrog algorithm has better performance than other intelligent optimization algorithms in terms of convergence precision and evolutionary algebra, and has higher convergence precision, which can effectively avoid falling into local optimum and stabilize the optimization results.