Robot path planning using fusion algorithm of ant colony optimization and genetic algorithm
Kangkang Ma, Lei Wang, Jingcao Cai, Dongdong Li, Anheng Wang, Tielong Tan
Abstract
Aiming at the shortcomings of single ant colony optimization such as many redundant nodes, slow convergence and low efficiency, based on the idea of “selection-crossover” of genetic algorithm, an improved fusion algorithm of ant colony optimization and genetic algorithm is proposed. In this paper, the fusion algorithm includes “optimal strategy” and “genetic region strategy”. The optimal strategy is that high-quality parents are selected by roulette in the first [Formula: see text] paths of each generation; genetic region strategy is that according to the path information of the parents, the grid map is divided into genetic area and nongenetic area. Genetic area refers to the area where the offspring ants can pass, and nongenetic area refers to the area where the offspring ants can’t pass; finally, the offspring ant searches the path in the genetic region to reduce the search range of the offspring ant and improve the convergence speed. Simulation results show that the fusion algorithm has faster searching speed and more stable convergence than the basic ant colony optimization and other improved ant colony optimization.