Mathematics, Journal Year: 2024, Volume and Issue: 12(24), P. 4017 - 4017
Published: Dec. 21, 2024
Aiming at some shortcomings of the genetic algorithm to solve path planning in a global static environment, such as low efficiency population initialization, slow convergence speed, and easy-to-fall-into local optimum, an improved is proposed problem. Firstly, environment model established by using grid method; secondly, order overcome difficulty initialization method with directional guidance proposed; finally, balance optimization searching speed up solution non-common point crossover operator, range mutation simplification operator are used combination one-point traditional obtain algorithm. In simulation experiment, Experiment 1 verifies effectiveness this paper. The success rates Map 1, 2, 3, 4 were 56.3854%, 55.851%, 34.1%, 24.1514%, respectively, which higher than two methods compared. 2 (IGA) paper for planning. four maps, compared five algorithms shortest distance achieved all them. experiments show that has advantages
Language: Английский