基于自适应调整哈里斯鹰优化算法求解机器人路径规划问题

Solving robot path planning problem by adaptively adjusted Harris hawk optimization algorithm

针对启发式算法在机器人路径规划过程中存在路径长度不稳定和易陷入局部极小点的问题,提出一种基于自适应调整哈里斯鹰优化(AAHHO)算法。首先,利用收敛因子调整策略,调节全局搜索阶段和局部搜索阶段的平衡,同时利用自然常数为底数,提高搜索效率和收敛精度;其次,在全局搜索阶段,采用精英合作引导搜索策略,通过3个精英哈里斯鹰合作引导其他个体更新位置以提高搜索性能,通过3个最优位置加强种群间的信息交流;最后,通过模拟种内竞争策略增强哈里斯鹰跳出局部最优的能力。函数测试和机器人路径规划对比实验结果表明,所提算法无论是函数测试还是机器人路径规划都优于IHHO(Improve Harris Hawk Optimization)和CHHO(Chaotic Harris Hawk Optimization)等对比算法,对于求解机器人的路径规划具有较好的有效性、可行性和稳定性。

Aiming at the problem that the heuristic algorithms have unstable path lengths and are easy to fall into local minimum in the process of robot path planning,an Adaptively Adjusted Harris Hawk Optimization(AAHHO)algorithm was proposed.Firstly,the convergence factor adjustment strategy was used to adjust the balance between the global search stage and the local search stage,and the natural constant was used as the base to improve the search efficiency and convergence accuracy.Then,in the global search phase,the elite cooperation guided search strategy was adopted,by three elite Harris hawks cooperatively guiding other individuals to update the positions,so that the search performance was enhanced,and the information exchange among the populations was enhanced through the three optimal positions.Finally,by simulating the intraspecific competition strategy,the ability of the Harris hawks to jump out of the local optimum was improved.The comparative experimental results of function testing and robot path planning show that the proposed algorithm is superior to comparison algorithms such as IHHO(Improve Harris Hawk Optimization)and CHHO(Chaotic Harris Hawk Optimization),in both function testing and path planning,and it has better effectiveness,feasibility and stability in robot path planning.