| Artificial electric field algorithm is an intelligent designed artificial system for functional optimization.The idea of this algorithm is based on Coulomb’s law of electrostatic force and Newton’s law of motion.The algorithm has few adjustable parameters,fast convergence speed,good development ability and low computational complexity,and has been successfully applied in the fields of scientific calculation,engineering optimization and intelligent control.However,the algorithm is easy to fall into the local optimal,the late algorithm convergence speed is slow,precision is not high defects.The main research work of this paper is as follows:(1)In order to improve the slow convergence speed and low precision of the artificial electric field algorithm,the memory and group information sharing mechanism of particle swarm optimization algorithm is added to the basic artificial electric field algorithm,and the adaptive coulomb parameters are added to balance the detection and development ability between algorithms.A hybrid particle swarm artificial electric field algorithm is proposed.In order to verify the performance of the proposed algorithm,the algorithm is applied to the order batching problem.In the test case,the algorithm can find a better order batching scheme,and the algorithm has faster convergence speed and higher accuracy.(2)In order to extend the application range of artificial electric field algorithm,a discrete artificial electric field algorithm is proposed.Firstly,the basic artificial electric field algorithm is discretized,and the embedded local search strategy enables the algorithm to solve the four-color coloring problem of planar graph effectively and accurately.The simulation results show that the average iteration times of the algorithm is small,the coloring success rate is high in processing graph coloring problems,and the correct coloring scheme can be found in the actual map coloring problems. |
