Research on the Application of Improved Gazelle Optimization Algorithm in Robot Path Planning
To address the problems that the gazelle optimization algorithm(GOA)is slow rate of conver-gence and easy to fall into local optimum,this paper proposed an improved gazelle optimization algorithm(IGOA).Firstly,in order to Increase particle diversity to improve algorithm's ability to escape local opti-ma,the improved algorithm used the Logistic chaos mapping for population initialization.Secondly,in order to speed up the convergence of the algorithm,the improved algorithm used the t of iterations to as the sys-tem parameter to construct the sine-cosine perturbation operator and the jump step weighting factor to up-date the particle positions.Also,the improved algorithm improved the expression of predator cumulative effect to improve the convergence accuracy of the algorithm.And finally,in order to improve the conver-gence accuracy of the algorithm,the improved algorithm improved the position of out-of-bounds particles based on their upper and lower boundaries.The improved algorithm compared with the traditional GOA al-gorithm and four advanced algorithms on eight standard test functions.The results show that the improved algorithm has significant advantages in terms of convergence accuracy and convergence speed.Using im-proved algorithms for robot path planning,the results show that the improved algorithm has higher search efficiency,faster convergence and shorter planning path.
万方数据
维普
