Solving multiobjective flexible scheduling problem by improved DNA genetic algorithm

Build mathematical models for multi-objective flexible scheduling problems, put forward a improved genetic algorithm based on DNA computation, combine it with Pareto non-dominated sorting method to work out multi-objective flexible scheduling optimization problems. In order to ensure the diversity of optimal solution sets, RNA four-digit-system encoder mode and genetic operator based on DNA computation were adopted, designed subs ection crossover and dynamic mutation operation. Through simul ation, test the designed algorithm performance; by comparing with conventional genetic algorithm test results, it proved the efficiency of the algorithm.