To improve the convergence and distributivity of multi-objective particle swarm optimization,we propose a method for multi-objective particle swarm optimization by fusing multiple strategies(MOPSO-MS),which includes t...To improve the convergence and distributivity of multi-objective particle swarm optimization,we propose a method for multi-objective particle swarm optimization by fusing multiple strategies(MOPSO-MS),which includes three strategies.Firstly,the average crowding distance method is proposed,which takes into account the influence of individuals on the crowding distance and reduces the algorithm’s time complexity and computational cost,ensuring efficient external archive maintenance and improving the algorithm’s distribution.Secondly,the algorithm utilizes particle difference to guide adaptive inertia weights.In this way,the degree of disparity between a particle’s historical optimum and the population’s global optimum is used to determine the value of w.With different degrees of disparity,the size of w is adjusted nonlinearly,improving the algorithm’s convergence.Finally,the algorithm is designed to control the search direction by hierarchically selecting the globally optimal policy,which can avoid a single search direction and eliminate the lack of a random search direction,making the selection of the global optimal position more objective and comprehensive,and further improving the convergence of the algorithm.The MOPSO-MS is tested against seven other algorithms on the ZDT and DTLZ test functions,and the results show that the MOPSO-MS has significant advantages in terms of convergence and distributivity.展开更多
In order to develop an automated segmentation system for Computed Tomography （CT） brain images, a new approach which consists of several unsupervised segmcotation techniques was introduced. The system segments the C...In order to develop an automated segmentation system for Computed Tomography （CT） brain images, a new approach which consists of several unsupervised segmcotation techniques was introduced. The system segments the CT brain images into three partitions, i. e., abnormalities, cerebrospinal fluid （CSF）, and brain matter. Our approach consists of two phase-segmentation methods. In the first phase segmentation, k-means and fuzzy cmeans （FCM） methods were implemented to segment and transform the images into the binary images. Based on the connected component in binary images, a decision tree was employed for the annotation of normal or abnormal regions. In the second phase segmentation, the modified FCM with population-diameter independent （PDI） segmentation was applied to segment the images into CSF and brain matter. The experimental results have shown that our proposed system is feasible and yield satisfactory results.展开更多
基金National Natural Science Foundation of China(No.61702006)Open Fund of Key laboratory of Anhui Higher Education Institutes(No.CS2021-ZD01)。
文摘To improve the convergence and distributivity of multi-objective particle swarm optimization,we propose a method for multi-objective particle swarm optimization by fusing multiple strategies(MOPSO-MS),which includes three strategies.Firstly,the average crowding distance method is proposed,which takes into account the influence of individuals on the crowding distance and reduces the algorithm’s time complexity and computational cost,ensuring efficient external archive maintenance and improving the algorithm’s distribution.Secondly,the algorithm utilizes particle difference to guide adaptive inertia weights.In this way,the degree of disparity between a particle’s historical optimum and the population’s global optimum is used to determine the value of w.With different degrees of disparity,the size of w is adjusted nonlinearly,improving the algorithm’s convergence.Finally,the algorithm is designed to control the search direction by hierarchically selecting the globally optimal policy,which can avoid a single search direction and eliminate the lack of a random search direction,making the selection of the global optimal position more objective and comprehensive,and further improving the convergence of the algorithm.The MOPSO-MS is tested against seven other algorithms on the ZDT and DTLZ test functions,and the results show that the MOPSO-MS has significant advantages in terms of convergence and distributivity.
文摘In order to develop an automated segmentation system for Computed Tomography （CT） brain images, a new approach which consists of several unsupervised segmcotation techniques was introduced. The system segments the CT brain images into three partitions, i. e., abnormalities, cerebrospinal fluid （CSF）, and brain matter. Our approach consists of two phase-segmentation methods. In the first phase segmentation, k-means and fuzzy cmeans （FCM） methods were implemented to segment and transform the images into the binary images. Based on the connected component in binary images, a decision tree was employed for the annotation of normal or abnormal regions. In the second phase segmentation, the modified FCM with population-diameter independent （PDI） segmentation was applied to segment the images into CSF and brain matter. The experimental results have shown that our proposed system is feasible and yield satisfactory results.
