水文循环算法的改进

Improvements of hydrological cycle algorithm

针对水文循环算法在求解过程中存在收敛速度慢、寻优精度低以及运行时间过长等问题,提出水文循环算法的改进方法。为强化算法的全局搜索能力、提升算法的收敛速度,利用混沌初始化产生初始水滴种群;为减少算法的运行时间,自适应调整温度更新参数、减少流动阶段不必要的循环;为提高算法的寻优精度,在凝结阶段引入逆水流过程代替变异操作。采用10个基准测试函数进行仿真实验,实验结果表明,改进后的水文循环算法克服了水文循环算法的缺点,总体性能优于粒子群算法、遗传算法与鲸鱼算法。

Due to the slow convergence rate,low search accuracy and long running time of hydrological cycle algorithm(HCA)in the problem-solving process,the improved methods were proposed.To improve the global search ability and convergence rate,the chaotic initialization was used to produce the initial water drop population.To reduce the running time,the temperature updating parameter was adaptively adjusted and unnecessary cycles in the flow stage were reduced.To enhance the search accu-racy,the inverse flow procedure instead of mutation operations was introduced into the condensation stage.Simulation experiments were conducted on the 10 conventional test functions.The simulation results demonstrate that the improved algorithms not only overcome the shortcomings of HCA,but also achieve better overall performance than the particle swarm optimization,genetic algorithm and whale optimization algorithm.