Mobile robot path planning based on ABC-PSO algorithm

Abstract

Aiming at the problem of particle swarm optimization (PSO) algorithm converging fast, easy to fall into local optimum, poor accuracy, and artificial bee colony (ABC) algorithm global and local search ability is strong but the convergence speed of the optimization process is slow, a solution is proposed. The search process and optimization process in the artificial bee colony algorithm are added to the optimized particle swarm algorithm, namely the ABC-PSO algorithm, which has good global search capabilities and fast convergence. In order to improve the efficiency and accuracy of particle search, an adaptive inertia weight method is proposed, which uses different weight values at different stages. The final results show that the algorithm in this paper can find the optimal path quickly and efficiently.