【摘要】 为了从根本上改善路径寻优的策略,增加使用者的选择空间,文中将一个较新的优化算法(人群搜索算法)首次应用于路径规划领域。人群搜索算法的全局搜索能力有助于在一开始的优化过程中就找到最优的搜索方向,之后其较强的局部搜索能力又能在该方向进一步搜索最优解,用于路径寻优非常符合需求。将该方法与传统的遗传算法、粒子群优化算法、灰狼优化算法就路径优化方面进行了比较。Matlab的仿真结果证明了人群搜索算法在路径规划中的稳定性和收敛性较好,而且计算量小,对于路径规划领域有着重要研究意义。同时对于算法应用于路径规划容易出现冗余提出了一个优化技巧,实验证明该技巧能够在保留算法全局搜索能力的同时保证所产生的路径的平滑性。更多还原

【Abstract】 In order to fundamentally improve the path optimization strategy and increase the users ′ choice space,a relatively new optimization algorithm(seeker optimization algorithm)is applied to the path planning field for the first time. In the seeker optimization algorithm, the global search ability can help to find the optimal search direction at the initial optimization process,and then its strong local search ability can further search the optimal solution in this direction,which is very suitable for path optimization. At the same time,this method is compared with the traditional genetic algorithm,particle swarm optimization algorithm and gray wolf optimization algorithm in path optimization. The simulation results of Matlab prove the seeker optimization algorithm in path planning has better stability and convergence,and the calculation is small,which has important research significance for the path planning field. An optimization technique is proposed for the redundant path which is easy to appear when the algorithm is applied to the path planning. The experiments show that this technique can preserve the global search ability of the algorithm and ensure the smoothness of the generated path.更多还原