Application of multi adaptive particle swarm optimization in robot path planning

摘要

Standard particle swarm optimization is easy to fall into local optimization, premature and poor search effect in complex environment, which limits its development in robot path planning. A multi adaptive particle swarm optimization is proposed. The concept of particle evolution degree is proposed. The particle adaptively selects the parameters that meet its needs according to its own evolution state. The adaptive strategy of population number and the concept of population aggregation degree are integrated, and the population number is dynamically adjusted according to the aggregation degree in the process of algorithm optimization. Cubic spline interpolation and improved algorithm are combined to solve the path planning problem to get a smoother robot moving path. Compared with the standard particle swarm optimization and adaptive particle swarm optimization, the multi adaptive particle swarm optimization in the same environment is obviously better than other algorithms in robot path planning.