Particle swarm optimization algorithm based on Beetle Antennae Search algorithm to solve path planning problem

Particle Swarm Optimization (PSO), as a swarm intelligence optimization algorithm, researching and applying in the track planning of robot in large range, but it has the defects of low search accuracy and easy fall into a locally optimal path. To overcome its shortcomings, this paper proposes an evolutionary algorithm combining beetle algorithm search (BAS) and particle swarm algorithm (PSO) and named BAS -PSO. This method is applied to multi-objective optimization path planning. In the optimization process, the update rule of each particle is derived from the BAS. In each iteration, each particle has its own judgment of the space of the environment, which reduces the number of iterations and improves the search speed. Finally, BAS-PSO algorithm and PSO algorithm are respectively used to simulate the path planning and the results demonstrate the BAS-PSO algorithm is more stable and effective.