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In order to deal with the complex environmental factors in microgravity test system, an improved wolf swarm algorithm (IWPA) was proposed and applied to the path planning of space simulator in micro gravity environment. Different from the traditional wolf colony algorithm, this paper uses Tent mapping to initialize the population; Introduce adaptive weight coefficient; And improve the fierce wolf's attack strategy; The wolves are updated in the iteration process by introducing the wolves' updating scale factor and reverse learning strategy. Considering the dynamic characteristics of the simulator, the position points obtained from the algorithm are used as control points, and the curve is fitted by B-spline, and the traffic cost of the fitted curve is used as the evaluation standard of the path. The performance of IWPA and WPA is compared by several benchmark functions, and the results show that IWPA is better than WPA. Finally, the algorithm is applied to the path planning of the simulator in the microgravity environment model. The experimental results show that IWPA has better path planning performance than WPA.
Wu et al. (Fri,) studied this question.