Research And Applications For The Problem Of Maximum Flow Minimum Cut Algorithms

The maximum flow minimization problem belongs to a combinatorial optimization problem.After many years of research,a lot of research results have been received.The maximum flow of the smallest cut problem in a large number of real life in the network have made the application.At the same time,many of the duration optimization,process control and other management disciplines and image processing can be transformed into the maximum flow of the smallest cut-off problem model solution.In this paper,we mainly study the related algorithms of the minimum cutoff problem,and the main contents are as follows:At first,an improved minimum augmented chain algorithm is proposed.In the improved algorithm,the saturation arc is reached in the original network.Thus optimizing the process of constructing the residual hierarchical network in the algorithm.The simulation results show that the improved algorithm is superior to the traditional algorithm.And then,the genetic algorithm is proposed to solve the minimum cutoff problem.In the algorithm,the coding and decoding methods,the group initialization method,the fitness function and the selection cross mutation operator are mainly designed.The simulation results show that the algorithm can optimize the efficiency of the algorithm compared with the traditional algorithm in solving the minimum cutoff problem of the maximum flow.At last,the application of the maximum flow minimum cut problem in graph cutting technology is introduced.Describes the transformation between the graph cut problem and the maximum flow minima problem model.Finally,the simulation experiment,the gray image segmentation.