Research and applications of the genetic algorithm based on improved multi-agent cooperation

This paper presents an improved genetic algorithm, applied to the collaborative multi-agent shop scheduling, and sets up the least of processing costs, processing time-effective objective function of multi-agent model. At the same time, because of the effective innovation on the encoding mechanism and cross-variation, the algorithm improve the operating speed, reduce the number of iterations, as well as shorten the route to find the optimal time. Simulation examples demonstrate the feasibility of the algorithm.