蝙蝠算法快速解算性能优化设计研究

摘要

In regard to the problem of slow convergence rate and accuracy in later stage,and poor optimization performance due to effect of the population,a new updating model of bat algorithm is defined by using the correlation theory of dynamic matrix.The parameter selection area of the convergent algorithm and the different convergence behavior under different parameters of the algorithm are obtained.In order to solve the problem that the algorithm is easy to fall into the local minimum point,the new model is optimized and a random inertial weight learning strategy is introduced.Numerical simulation results show that the proposed algorithm can improve the performance effectively.%针对基本蝙蝠算法由种群的趋同效应导致的后期收敛速度慢、收敛精度低、解算性能差的问题,运用动态矩阵的相关理论,定义了蝙蝠个体新的更新模型,并得到了上述模式下使算法快速收敛的参数选取区域和不同参数选取下算法的具体收敛性态.为进一步解决算法易陷入局部极小点的不足,对模型进行优化设计,引入随机惯性权重学习策略,通过仿真,验证了算法的解算性能得到有效提升.