上海工程技术大学 机构 管理学院,上海,201620
纸质出版:2018
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杜艳艳, 刘升. 一种改进的自适应混合型蝙蝠算法[J]. 微电子学与计算机, 2018, 35(6): 135-140.
DU Yan-yan, LIU Sheng. An Efficient Adaptive Improved-bat Algorithm[J]. 2018, 35(6): 135-140.
针对基本蝙蝠算法(BA)存在的寻优精度不高
易出现早熟收敛等缺陷
本文提出了一种改进的自适应混合型蝙蝠算法(YSBA).首先
该算法舍弃了速度这一参数
简化了计算; 其次
加入位置收缩因子β
用来控制与约束蝙蝠的位置
平衡蝙蝠算法中局部和全局搜索
提高算法的寻优精度
最后
重新设置了响度A和脉冲频率r的计算方法
此方法可以也可以避免陷入局部最优.最后通过11个典型的基准函数优化试验
与基本蝙蝠算法(BA)以及采用机动飞行的蝙蝠算法(MFBA)相比
发现改进的自适应混合型蝙蝠算法能够解决局部过分搜索的问题
避免陷入局部最优值
具有较高的计算精度.
Aiming at the existence of basic bat algorithm (BA) optimization accuracy is not high
traps into local optima easily. This paper presents a new improved bat algorithm
which is named YSBA. In this algorithm
firstly
to simplify the calculation and improve the convergence speed
a new search equation is proposed in generate new solutions. Secondly
location constrict factor is added
which can be used to control with the location of the bats
balance the global and local search of bats and improve the optimization precision of the algorithm. Finally
reset the method of the calculation of loudness and rate
which can also be used to avoid trapping into local search. To verify the performance of our algorithm
11 typical experiments are employed. The experimental results show that the new algorithm (YSBA) is significantly improved
which includes optimization accuracy
convergence speed
and they can also avoid falling into a local optimum.
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