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.