关键词: 蝗虫优化算法, Logistic混沌映射, S型生长曲线, t分布, 机器人路径规划

Abstract:

Given that heuristic algorithms have low convergence accuracy， low search path efficiency and tendency to fall into local optimum in solving robot path planning problem， an S-shaped Growth Curve Integrated Grasshopper Optimization Algorithm （SGCIGOA） was proposed. Firstly， the initial population of grasshoppers was optimized through the introduction of Logistic chaotic sequences， which results in the enhancement of the diversity of the grasshopper population in the early stages of iteration. Secondly， the non-linear inertia weight of S-shaped growth curve was introduced to adjust the decline way of the decline parameter， thus improving algorithm convergence speed and optimization accuracy. Finally， a t-distribution based position disturbance mechanism was introduced during the iteration， enabling full utilization of effective information of the current population， thereby balancing global search and local exploitation and reducing the probability of the algorithm being trapped in local optimum. Experimental results show that compared with 10 comparison algorithms such as MOGOA （Multi-Objective Grasshopper Optimization Algorithm）， IGOA （Improved Grasshopper Optimization Algorithm）， and IAACO （Improvement Adaptive Ant Colony Optimization）， the proposed algorithm reduces the optimal path length by an average of 0-14.78% and the average number of iterations by an of 56.60%-90.00% in simple environment， and has the optimal path length shortened by an average of 0-11.58% and the average number of iterations decreased by an of 45.00%-92.76% in complex environment. It can be seen that SGCIGOA represents an efficient approach to solving the path planning problem for mobile robots.

Key words: Grasshopper Optimization Algorithm (GOA), Logistic chaotic mapping, S-shaped growth curve, t-distribution, robot path planning