Optimal solution of robots task assignment problem based on improved artificial bee colony algorithm
Haiquan Wang, Fanbing Zhu, Wudai Liao, Xuekai Sun · 2017
In order to obtain the optimal solution of robotics task assignment problems fast and correctly, we proposed an intelligent optimization method of Artificial Bee Colony algorithm. In consideration of discrete characteristic of assignment problems solutions, food sources are coded in a disperse way when Artificial Bee Colony algorithm is being applied. Food sources are generated from global permutation and combination with random; employed bees and onlookers update solutions with a method of state shift, generating candidate solutions and guaranteeing the solutions to be feasible and variable. The results of data experiment showed that the improved discrete Artificial Bee Colony algorithm had a good performance of rate of convergence and precision of solutions, both of the performances are superior to other intelligence algorithms. The proposed dispersion coding in initialing food sources and updating solutions can also provide other combinational optimization problems with some advices.