[1]吕赵明,张颖江.改进的新型蝙蝠算法[J].计算机技术与发展,2018,28(05):63-67.[doi:10.3969/j.issn.1673－629X.2018.05.015]
　LYU Zhao-ming,ZHANG Ying-jiang.An Improved New Bat Algorithm[J].,2018,28(05):63-67.[doi:10.3969/j.issn.1673－629X.2018.05.015]
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改进的新型蝙蝠算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年05期
页码:
63-67
栏目:
智能、算法、系统工程
出版日期:
2018-05-10

文章信息/Info

Title:
An Improved New Bat Algorithm
文章编号:
1673－629X(2018)05－0063－05
作者:
吕赵明; 张颖江
湖北工业大学 计算机学院，湖北 武汉 430068
Author(s):
LYU Zhao-ming; ZHANG Ying-jiang
School of Computing,Hubei University of Technology,Wuhan 430068,China
关键词:
蝙蝠算法; 细菌觅食算法; 翻滚; 惯性权重; 扰动因子
Keywords:
bat algorithm; bacterial foraging algorithm; roll; inertia weight; disturbance factor
分类号:
TP301.6
DOI:
10.3969/j.issn.1673－629X.2018.05.015
文献标志码:
A
摘要:
对基本的蝙蝠算法及其他群智能优化算法进行试验研究后，发现基本蝙蝠算法存在易陷入局部最优、求解精度不高等缺陷，而细菌觅食算法具有群体智能算法并行搜索、易跳出局部极小值等优点。由于基本蝙蝠算法对求解的空间搜索不充分，通过实验分析提出了试探扰动因子;另外针对蝙蝠算法缺乏对父代的继承性，分别使用线性递减权重法、随机权重法和自适应权重法对蝙蝠算法求解性能进行了对比实验，发现随机惯性权重求解精度较高。基于以上分析，提出了一种改进的新型蝙蝠算法。该算法融合细菌觅食算法的趋化算子来改进蝙蝠算法的局部搜索能力，增加试探扰动因子来提高算法的求解精度和充分性，采用随机惯性权重来均衡算法的探索能力和挖掘能力。为了验证该算法的性能，选择几个高维的经典函数进行实验，结果表明，改进的新型蝙蝠算法同基本的蝙蝠算法和粒子群算法相比提高了寻优性能。
Abstract:
Based on the experimental study of the basic bat algorithm and other groups of intelligent optimization algorithms,it is found that the basic bat algorithm is easy to fall into the local optimum and the accuracy of the solution is not high.The bacterial foraging algorithm has the advantages of parallel search and jumping out of local minimum easily from group intelligence algorithm.Because the basic bat algorithm is not sufficient to search the solution space,the probe disturbance factor is put forward.In addition，the bat algorithm lacks
the inheritance of the parent.For this,we use respectively the linear descent inertial weight，the random inertial weight and the adaptive inertial weight to compare the performance of the bat algorithm,which shows that the accuracy of the random inertia weight is highest．Based on the above analysis,we propose an improved new bat algorithm which combines the chemotaxis operator of the bacterial foraging algorithm to improve the local search ability of the bat algorithm,increases the probabilistic perturbation factor to improve the accuracy and sufficiency of the algorithm and adopts the inertia weight to equalize ability of exploer and exploit in the algorithm.To verify the performance of the improved algorithm,several high quality classical functions are selected to test．The experiments show that the proposed bat algorithm improves the performance compared with the basic bat algorithm and particle swarm algorithm.

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更新日期/Last Update: 2018-06-28