新型果蝇优化算法的研究
发布时间：2018-12-12 02:10
【摘要】：由于传统果蝇优化算法(FOA)具有寻优精度低和易陷入局部极小点的缺点,提出了一种具有不同飞行半径的分群搜索策略,使得在搜索区间内果蝇的种群多样性大大增加;同时在果蝇个体的飞行距离与方向的步长函数上,针对不同的果蝇子群引入了不同的函数,该类函数具有周期震荡性质,可以很好地避免果蝇群陷入局部极小点而无法求得最优解。通过对8个测试函数的仿真实验,验证了这些策略能够有效地提高搜索精度、收敛速度和稳定性。
[Abstract]:Because the traditional Drosophila optimization algorithm (FOA) has the disadvantages of low precision and easy to fall into local minima, a cluster search strategy with different flight radius is proposed, which greatly increases the population diversity of Drosophila in the search region. At the same time, different functions are introduced for different Drosophila subgroups on the step function of flying distance and direction of Drosophila, which has the property of periodic oscillation. It can avoid the drosophila population falling into local minima and can not get the optimal solution. The simulation results of eight test functions show that these strategies can effectively improve the search accuracy, convergence speed and stability.
【作者单位】： 安徽大学计算智能与信号处理重点实验室;安徽大学计算机科学与技术学院;
【基金】：安徽省科技攻关项目(No.06060701)
【分类号】：TP18
本文编号：2373695
[Abstract]:Because the traditional Drosophila optimization algorithm (FOA) has the disadvantages of low precision and easy to fall into local minima, a cluster search strategy with different flight radius is proposed, which greatly increases the population diversity of Drosophila in the search region. At the same time, different functions are introduced for different Drosophila subgroups on the step function of flying distance and direction of Drosophila, which has the property of periodic oscillation. It can avoid the drosophila population falling into local minima and can not get the optimal solution. The simulation results of eight test functions show that these strategies can effectively improve the search accuracy, convergence speed and stability.
【作者单位】： 安徽大学计算智能与信号处理重点实验室;安徽大学计算机科学与技术学院;
【基金】：安徽省科技攻关项目(No.06060701)
【分类号】：TP18
【参考文献】
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