To solve the shortcomings of traditional algorithm in solving the mobile robot path planning problem,such as slow convergence speed and easy to fall into the local optimum.An improved discrete whale optimization algorithm is proposed.First,the method uses an ant colony algorithm to initialize the population and improve the quality of the initial solution.Then,an improved nonlinear convergence factor is utilized to balance the global exploration and local search capabilities of the algorithm to avoid the algorithm from falling into a local optimum.Finally,the new crossover operator,variable neighbourhood search operator and mutation operator are introduced to improve the update formula of the whale optimization algorithm in combination with the path planning problem,which improves the convergence speed and search accuracy of the algorithm.The effectiveness of the algorithm improvement is verified by comparing the improved algorithm with other algorithms through the TSPLIB standard arithmetic library.Under the global path planning simulation environment of different sizes,the experimental results show that the improved algorithm can quickly and stably obtain the global optimal path,effectively shorten the path length,reduce the number of path inflection points,and has good applicability in the path planning problem.
关键词
移动机器人/离散鲸鱼算法/路径规划/非线性收敛因子
Key words
mobile robot/discrete whale optimization algorithm/path planning/nonlinear convergence factor
维普
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