43results about How to "Overcome the shortcoming of easy to fall into local optimum" patented technology

The invention discloses an image segmentation method based on genetic rough set C-mean clustering, which mainly solves the problem that the conventional method has poor robustness, easily falls into local optimum and loses too much local information. The method comprises the implementation steps of: (1) inputting a to-be-segmented image; (2) extracting image texture features; (3) generating clustering object data; (4) initializing population; (5) updating membership; (6) dividing the clustering object data; (7) updating the population; (8) calculating an individual fitness value; (9) evolving the population; (10) judging whether a termination condition is satisfied; (11) generating an optimal individual; (12) marking; (13) generating segmented images. In the method, the texture features of each pixel of the image are extracted, and the texture features are marked through the C-mean clustering method based on the genetic algorithm and the thought of rough set so as to divide the pixels, thus, stability of image segmentation is improved, and more accurate image segmentation result is obtained.

The invention discloses a multidimensional collaborative power grid planning method which includes the processes: acquiring geographic environmental information data and calculating comprehensive construction cost data by a rasterized map; building a power grid planning model; and calculating the solution of a power grid planning scheme by an optimal ant colony algorithm according to the acquired data and the power grid planning model. The method includes the steps: comprehensively considering substation locating and sizing, line selection and line corridor selection; and building a multidimensional collaborative power grid planning model considering complicated environmental influence. The multidimensional collaborative power grid planning method can accurately consider the influence of environmental factors on power grid planning. Line construction cost calculation precision is improved, and estimation difficulty is reduced. Catalytic elements are generated according to deviation of the local movement direction and the target movement direction of ants, so that the state transition rule of the ants is improved. The construction cost of lines on a mass raster and a substation is full-automatically and rapidly calculated, and calculation precision and efficiency are remarkably improved.

The invention discloses an image segmentation method based on intuitionistic fuzzy C-means clustering. It mainly solves the problems of being sensitive to noise, easy to fall into local optimum, and the number of clusters need to be set in image segmentation. Curve fitting, screening all peak points of the fitting curve as a set of initial value ranges of cluster centers, and counting the maximum number of cluster centers; on the basis of , using the pixel position information and gray level information to construct the linearity in the intuitionistic fuzzy objective function The weighting function coefficients are used to obtain the membership degree matrix U; the optimal membership degree matrix is ​​obtained by evaluating U according to the graded distance index evaluation index, and the error detection strategy is used to screen the misclassified pixels for correct classification. The invention enhances the robustness to noise, can adaptively determine the number of image clusters, and can be used for preprocessing of image recognition and computer vision.

The invention relates to an antenna array spare construction method based on a quantum glowworm search mechanism. The antenna array spare construction method comprises the following steps: establishing an antenna spare array model, and determining a key parameter, which corresponds to the quantum glowworm search mechanism, of the antenna spare array model; substituting a quantum glowworm position into a fitness function to obtain a fitness value of the quantum glowworm position, and confirming a local optimal position and a global optimal position in a glowworm group; updating the fluorescein value and the learning neighborhood of each quantum glowworm; updating a quantum glowworm quantum position and the quantum glowworm position; updating the dynamic decision domain radius of the quantum glowworm; calculating the fitness value under a new quantum glowworm position, and confirming the local optimal position and the global optimal position in a quantum glowworm group again; and if a maximum iteration is achieved, outputting the global optimal position, and mapping to obtain a spare antenna array form.

The invention provides a locating and sizing method of a static synchronous series compensator. The method comprises the steps of: constructing a target function for locating and sizing of the static synchronous series compensator, and determining the constraint conditions of locating and sizing; and adopting a catastrophic variable speed quantum genetic algorithm to calculate the function value of the target function, comparing the target function values of individuals in a group, and further determining the optimal installation position of the static synchronous series compensator and an injection voltage amplitude. Compared with the prior art, the locating and sizing method of the static synchronous series compensator starts from a plurality of targets, the optimal line and capacity are searched in the total network lines for the installation of the static synchronous series compensator, and the comprehensive level of the economic performance and the stability of the power grid is improved.