基于DE算法改进的蝙蝠算法的研究及应用

Research and Application of Improved Bat Algorithm Based on DE Algorithm

为解决基本蝙蝠算法寻优精度不高、后期收敛速度慢、易陷入局部最优等问题,提出了一种基于差分进化算法的改进蝙蝠算法。把差分进化算法中的变异、交叉、选择机制应用于蝙蝠算法,使缺乏变异机制的蝙蝠算法具有变异机制,从而提高蝙蝠算法的多样性,避免种群个体陷入局部最优,增强算法全局寻优能力。通过6个标准测试函数测试的仿真结果表明,改进的算法能够较大幅度地提高其收敛精度、收敛速度以及鲁棒性,并有效地避免早熟收敛问题。把改进算法应用于求解非线性方程组问题,通过数值算例,验证了改进算法的可行性和有效性,扩展了蝙蝠算法的应用领域。

In order to solve the problems of bat algorithm, such as low convergence accuracy, slow convergence velocity and easily falling into local optimization, this paper presented an improved bat algorithm based on differential evolution algorithm. The mutation, crossover and selection mechanism of differential evolution algorithm were intro- duced into the bat algorithm, so that the bat algorithm lack of mutation mechanism has the variation mechanism, which can enhance the diversity of bat algorithm, the avoid the population falling into local optimum, and enhance the ability of global optimization for bat algorithm. The simulation results of six standard benchmark functions show that the improved algorithm can greatly improve the convergence precision, convergence speed and robustness, and effec- tively discourage the premature convergence. Meanwhile, the improved algorithm was applied to solve nonlinear equa- tions and the numerical examples were proposed, which proves the feasibility and effectiveness of the improved algo- rithm.