多策略融合的改进哈里斯鹰算法及其路径规划应用

Multi-strategy Fusion Improved Harris Hawk Algorithm and Its Path Planning Application

针对哈里斯鹰算法(HHO)后期缺乏全局搜索、易陷入局部极值等问题,提出了一种多策略融合的改进哈里斯鹰算法(MFHHO).首先,提出了自适应混沌和核心种群划分策略,平衡算法后期的全局和局部搜索性能,提高算法的多样性;其次,修改位置更新公式,将莱维飞行替换为黄金正弦策略,提高算法寻优性能和效率;最后,融合自适应正态云最优解扰动策略,提高算法跳出局部最优解的能力.在国际测试函数、CEC2014复杂函数的数值实验中,改进算法性能提升明显;在路径规划实验中,改进算法性能最优,相较于对照组算法性能提升了14.75%,且具有较好的稳定性.

Aiming at the problems of lack of global search and easy to fall into local extremum in the later stage of Harris Hawk algorithm(HHO),an improved Harris Hawk algorithm(MFHHO)with multi-strategy fusion is proposed.Firstly,an adaptive chaos and core population division strategy are proposed to balance the global and local search performance of the algorithm in the later stage and improve the diversity of the algorithm.Secondly,the position update formula is modified,and the Levy flight is replaced by the golden sine strategy to improve the optimization performance and efficiency of the algorithm.Finally,the adaptive normal cloud optimal solution perturbation strategy is integrated to improve the ability of the algorithm to jump out of the local optimal solution.In the numerical experiments of international test functions and CEC2014 complex functions,the performance of the improved algorithm is obviously improved.In the path planning experiment,the improved algorithm has the best performance,which is 14.75%higher than that of the control group,and has good stability.