改进蝙蝠算法在模糊层次分析中的应用

摘要

为了解决基本蝙蝠算法易发生早熟收敛、求解精度较低等问题, 提出并实现了旨在提高群体多样性的改进算法. 首先在蝙蝠算法中引入速度权重因子, 令其在迭代过程中线性递减; 其次在局部新解不满足接受条件时, 对蝙蝠位置进行Cauchy分布随机数扰动, 并在算法运行中间隔性调用非线性规划函数. 改进算法能在寻优过程中保持群体多样性, 增强全局搜索和局部搜索能力. 标准函数测试及在模糊层次分析中的应用结果表明, 改进蝙蝠算法的性能远优于基本蝙蝠算法, 具有较好的实用价值.%In order to improve the basic bat algorithm's premature convergence and low solving accuracy, an improved algorithm is proposed to enhance the diversity of the swarm. Firstly, the velocity weighting factor is introduced into the bat algorithm to make it decrease linearly during the iteration. Then the position of the bat is perturbed by the random number of Cauchy distribution when the local new solution does not satisfy the acceptance condition and the nonlinear programming function is called at intervals between algorithm runs. The improved algorithm can maintain the diversity of the swarm and enhance the ability of global and local search in the optimization process. The standard function test and its application in fuzzy hierarchical analysis show that the performance of the improved bat algorithm is much better than that of the basic bat algorithm, and has better practical value.