新型随机分形搜索算法

Novel stochastic fractal search algorithm

针对随机分形搜索算法在更新阶段中存在收敛速度慢、求解精度不高和易陷入局部最优等缺陷,提出一种新型随机分形搜索算法。通过将差分进化算法的变异操作引入到随机分形搜索算法的更新阶段,进一步增加生成群体的多样性并提高算法的求解精度,有效提高算法的搜索性能。采用12个标准测试函数进行数值实验,将新型随机分形算法与随机分形搜索算法和引力搜索算法进行比较。实验结果表明,新型随机分形搜索算法具有良好的优化性能。

To solve the problem of slow convergence speed,low accuracy and easily falling into local optimum in the updating process of stochastic fractal search algorithm(SFS),a kind of novel stochastic fractal search algorithm(NSFS)was proposed.By introducing the mutation operator of the differential evolution algorithm into the updating process of SFS,the diversity of the generated population and the accuracy of the algorithm were increased,thus the search performance of the algorithm was effectively improved.Series of computational experiments on 12 benchmark instances were tested and the comparisons with that of SFS and gravitational search algorithm show the NSFS has better performance.