Path Planning of Mobile Robot Based on Improved Wolf Swarm Algorithms
Chen Guang Xu, Yi Zhang, Kui Li, Baiyue Huang · 2019
Aiming at solving the shortcomings of traditional wolf swarm algorithm, such as low search efficiency due to fixed search direction and radius, and easy to fall into local optimum when updating rules, an improved wolf swarm algorithm is proposed in this paper. Firstly, the exploding rules of fireworks algorithm are introduced to reduce the exploring steps of wolves near prey and increase the searching direction in order to improve the ability of local exploration, while the wolves far from prey the searching radius are increased and the searching direction are decreased in order to improve the global search ability. Secondly, the moving step size of running behavior is improved automatically to adjust the step size according to the location of each wolf, which can improve the efficiency of running behavior. Finally, the updating rules of wolf swarm algorithm is improved for the individual wolf swarm is selected according to probability to enhance the global optimization ability of the algorithm. The performance test in test function and path planning simulation experiment results show that the improved wolf swarm algorithm has faster convergence speed and higher convergence accuracy.