一种利用动态搜索策略的混合蛙跳算法

doi:10.3969/j.issn.1001-2400.2014.04.010

Abstract

Abstract:

Based on the principle of the shuffled frog leaping algorithm (SFLA), the algorithm's optimization mechanism is studied. A novel shuffled frog leaping algorithm is proposed to solve the problems of the original SFLA, such as non-uniform initial population, slow convergence speed in later iterations, and being easy to fall into local optimum. The improved algorithm generates the initial population with the random uniform design method, changes the influence of the subgroup's current worst value on the subsgroup's evolution behavior dynamically by using the influence factor. Besides, the variance of the population's fitness is calculated to judge whether the population falls into local optimum, and then the improved algorithm makes the population jump out of local optimal state by the perturbation of the current global optimal value. Experimental results show that the improved algorithm can lead to a higher convergence accuracy and a better convergence result.

Key words: shuffled frog leaping algorithm, random uniform design, influence factor, the variance of the population's fitness, perturbation

CLC Number:

TP301.6

JIANG Jianguo;ZHANG Liyuan;SU Qian;DENG Lingjuan;LIU Mengnan. Shuffled frog leaping algorithm using dynamic searching strategy[J].J4, 2014, 41(4): 51-57.

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Shuffled frog leaping algorithm using dynamic searching strategy

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