具有Levy变异和精英自适应竞争机制的蚁狮优化算法

摘要

针对蚁狮优化算法易陷入局部最优、收敛速度慢的缺点,本文提出一种具有Levy变异和精英自适应竞争机制的蚁狮优化算法.利用服从Levy分布的随机数对种群较差个体进行变异,可改善种群多样性提高算法的全局搜索能力;精英自适应竞争机制使得多个精英并行带领种群寻优,提高了算法的收敛速度,为避免较大计算量,并行竞争的精英个数会随着寻优代数增加而减少.同多个改进算法进行比较,结果表明本文所提算法具有更好的寻优精度和收敛速度.最后将本文改进算法应用于硅单晶热场温度模型的参数辨识,仿真结果说明该算法具有较好的参数辨识能力.%The ant lion optimizer (ALO) reveals such deficiencies as easily relapsing into local optimum and low con-vergence speed. This paper proposed an improved ALO algorithm with Levy variation and adaptive elite competition mechanism. By carrying out Levy variation to poor individuals, the diversity of population and the global search ability of the algorithm can be increased. Moreover, the adaptive elite competition mechanism that many elites lead the popula-tion to search at the same time can improve the convergence speed of the algorithm. To reduce the amount of calcula-tion, the number of the elites competing in parallel will decrease with the increase of iterations. By contrast with other improved optimization algorithms, the test results show that the improved algorithm proposed in this paper has better search precision and convergence speed. Finally, this improved algorithm is applied to identify parameters of silicon single crystal thermal field temperature model and the simulation results prove its excellent ability of parameters identi-fication.