Research On Improvement Of Swarm Intelligence Algorithm In Image Segmentation

Image segmentation is a fundamental problem in the field of digital image processing.Its timeliness directly affects the efficiency of the whole system.The traditional threshold segmentation method deals with images with huge data volume,which often cannot meet the real-time performance.It is required that how to improve and improve its processing efficiency has become an urgent problem to be solved.In this paper,the research on swarm intelligence algorithm improvement in image segmentation is to improve the group intelligence algorithm and introduce it into image segmentation in the context of image segmentation,and improve segmentation efficiency and accuracy.The main contents of this paper are as follows:1.In the background of image segmentation application,set the crosspopulation swarm intelligence optimization algorithm comparison method,firstly use the standard test function to test the performance of the swarm intelligence algorithm,and then introduce the improved algorithm into the image segmentation for verification,and according to this problem stipulates a unified evaluation index to test the performance of the algorithm.2.In-depth analysis of the optimization process of the cuckoo algorithm,and propose a cuckoo optimization algorithm based on hybrid dimension update for the degradation problem of dimension update.The hybrid dimension update strategy pair algorithm is introduced by introducing the dimension-by-dimensional update and the overall update.The correction is performed to perform a stepwise overall neighborhood search with a normal distribution with adjustable variance.The experiment proves that the search accuracy of the algorithm is effectively improved,the average optimization accuracy is improved to 76.94%,and the running time is reduced by 1.3s compared with the exhaustive method.3.In-depth analysis of the optimization process of the bird group algorithm,a bird population optimization algorithm based on dynamic inertia weight is proposedfor its rigid problem of balanced production and foraging ability.The algorithm introduces nonlinear dynamic inertia weight balance local and global search.Ability to introduce Levi flying during the foraging of producers.Experiments show that the improved bird group algorithm effectively improves the convergence speed and optimization accuracy of the algorithm;improves the average optimization accuracy to 94.72%,and reduces the running time by about 1.2s compared with the exhaustive method.