【摘要】 针对求解机器人路径规划问题,本文提出了一种多策略集成的樽海鞘群算法.在该算法中,提出了新的自适应领导者结构,以平衡算法的探索和开发能力;引入可以提高Lyapunov指数的Logistic-Cubic级联混沌映射作为食物源的扰动算子,来避免算法陷入局部最优;采用基于自适应参数的分散觅食策略使部分追随者探索有前景的区域.在CEC 2014测试集的多种函数上,本文算法与3种改进的樽海鞘群算法和5种先进的群智能算法进行比较,结果表明本文算法综合优化性能更好.本文算法2将其用于求解机器人路径规划问题,其中用三次样条插值对路径进行平滑.在障碍是8,9,13的环境下分别进行仿真实验,仿真结果表明,本文算法在给定的仿真场景下与给定的对比算法相比获得了最好的结果.更多还原

【Abstract】 A multi-strategy ensemble salp swarm algorithm is proposed for solving problem of robot path planning.In the algorithm,a new adaptive leader structure is proposed to balance the exploration and exploitation ability of the algorithm.The chaotic map of Logistic-Cubic cascade which can improve the Lyapunov exponent of the cascade chaotic system is introduced as the disturbance operator of the food source to avoid the algorithm falling into the local optimum.A disperse foraging strategy based on adaptive parameters is adopted to force a part of followers to explore promising areas.The algorithm in this paper is compared with three improved SSA algorithms and five state-of-the-art swarm intelligence algorithms on IEEE CEC 2014 functions.The results show that the comprehensive optimization performance of the algorithm in this paper is better.The proposed algorithm is applied to solve the robot path planning problem,in which the path is smoothed by cubic spline interpolation.Simulation experiments are implemented on computer in the environments where the obstacles are 8,9,13,respectively.The simulation results demonstrate that the proposed algorithm can achieve the best results compared with the given contrast algorithms in given simulation scenarios.更多还原