9results about How to "Increase population diversity" patented technology

The invention provides a valve leakage diagnosis method and system based on an improved Hereum algorithm optimized SVM (Support Vector Machine), and relates to the technical field of valve leakage diagnosis. Decomposing the collected original signal to obtain a limited number of IMF components; calculating a fuzzy entropy, and constructing a feature vector; taking the feature vectors as data samples, and dividing the data samples into a training set, a verification set and a test set; using the training set to train the SVM model, and according to the classification error rate of the verification set on the trained SVM model, performing iterative optimization on the parameters of the SVM model based on an improved He-Ma algorithm to obtain an optimized SVM model; inputting the test set into the optimized SVM model to obtain a valve leakage diagnosis model; and acquiring a new acoustic emission signal, and inputting the new acoustic emission signal into the valve leakage diagnosis model to obtain a diagnosis result. The method not only can improve the classification accuracy and generalization ability of the diagnosis model, but also can enhance the stability of the model under different working conditions and environmental noise.

The invention discloses an unmanned aerial vehicle three-dimensional path planning method based on an improved dung beetle algorithm, and relates to the technical field of unmanned aerial vehicle path planning. According to the technical scheme, an unmanned aerial vehicle flight environment is modeled; establishing constraint conditions; establishing a target function; and performing unmanned aerial vehicle global path planning by adopting an improved dung beetle optimization algorithm. The method has the beneficial effects that initialization is carried out by utilizing a good point set strategy, so that the population diversity is improved; an improved sine algorithm is combined with a variation strategy of Levy flight and Brownian motion to be embedded into position updating of dung beetles, so that the search range is expanded, and the global optimization ability of the algorithm is improved; a simplex method and a self-adaptive reverse learning strategy are added for disturbance, and the algorithm is helped to jump out of a local optimal solution; the efficiency and accuracy of unmanned aerial vehicle path planning are remarkably improved, the robustness and adaptability of the algorithm are enhanced, the flight path of the unmanned aerial vehicle is optimized, and further development of the unmanned aerial vehicle technology is promoted.

The invention relates to a noise feature vector classification model construction method, a speech enhancement method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: coding penalty parameters and kernel function parameters of a support vector machine; taking the penalty parameter and the kernel function parameter as optimization variables of a genetic algorithm, setting an initial population, and constructing a fitness evaluation function based on classification accuracy; performing selection operation, crossover operation and mutation operation on the initial population, and updating an iterative population according to a fitness evaluation function to obtain an optimal penalty parameter and an optimal kernel function parameter; and training a support vector machine through the optimal penalty parameter and the optimal kernel function parameter, and constructing a noise feature vector classification model. In a noisy environment, the classification effect of the noise feature vector is improved under the condition that nonlinear and non-stationary noise exists, and the noise feature classification requirement in a complex sound signal processing scene is met.

The present application relates to the field of Gastrodia elata cultivation, and particularly relates to a method for continuously planting Gastrodia elata in old planting holes. The present application effectively improves the yield of Gastrodia elata, and significantly reduces the incidence of new Gastrodia elata and the decay rate of mother Gastrodia elata by scientifically planting after rhizoma polygonati. In the present application, the breeding hole is sexual reproduction, and it does not need to cultivate armillaria bed. The armillaria, fungus material and Gastrodia elata seeds can be placed at the same time. The planting hole is asexual reproduction. Since the Gastrodia elata seed needs a large amount of nutrient elements, the armillaria bed needs to be cultivated. This operation mode can form the difference of microbial community between the breeding hole and the planting hole, which is beneficial to ecological competition, and thus improves the soil recovery and improvement.

The invention discloses an electric power system load model parameter identification method based on an improved bat algorithm, and relates to the field of electric power systems and automation of the electric power systems. And fitness sharing and hybrid local search strategies are applied to the bat algorithm, so that the algorithm can jump out of a local optimal solution, and the problem of global optimization is solved. The method comprises the following steps of: firstly, traversing all solution spaces through chaotic mapping, initializing the solutions, applying a bat algorithm, calculating the fitness of each solution in the algorithm application process, sharing the fitness for the solutions near a peak value in the solution spaces, and after the algorithm runs for a certain number of cycles, determining the fitness of the solutions near the peak value in the solution spaces, wherein the more the solutions exist in a certain range, the lower the fitness is; and carrying out secondary search by using a simplex method. According to the method, the problem that a traditional bat algorithm is easily limited to a local optimal solution is solved, so that the load model parameter identification method has higher identification precision.

The application relates to a WSN cluster head election method and system based on an elite multi-objective gold panning algorithm, and the method comprises the following steps: calculating an optimal cluster head number based on a wireless sensor network; generating an initial population by using a Sobol sequence and performing population iteration; updating individuals of the population by using an optimized gold panning optimization algorithm to obtain a first population; performing Gaussian mutation on the first population to obtain a second population; combining the three populations to form an enhanced elite candidate population; performing target function calculation, quickly non-dominant sorting the individuals by using an NSGA-III algorithm to obtain the level to which the individuals belong; judging whether the current iteration number reaches a target iteration number; if not, selecting the next generation population based on the enhanced elite candidate population and by using the NSGA-III algorithm to perform population iteration; and if yes, outputting a Pareto front to determine a cluster head node in a wireless sensor network (WSN). The application introduces the enhanced elite candidate population to expand population diversity and avoid loss of historical elite solutions.

The invention discloses a software defect feature selection method, device and system and a storage medium. The method comprises the following steps: S1, carrying out feature coding on software measurement features of a software defect historical data set; s2, performing sectional probability initialization on the initial population by adopting a minimum redundancy and maximum correlation algorithm according to the coded software measurement characteristics to obtain a plurality of sub-populations; s3, performing parallel chromosome evolution operation and crossover mutation operation on each sub-population; and S4, after parallel evolution and overall crossover variation of each sub-population, putting a parent chromosome and a generated sub-chromosome sum into a chromosome pool, selecting an optimal individual chromosome according to a fitness descending order, then carrying out remodeling of the sub-population, and continuing to repeat genetic manipulation until the maximum number of iterations is reached. By adopting the technical scheme of the invention, a high-quality feature combination which is most effective to predict defects and has the lowest redundancy is quickly and accurately screened out from high-dimensional software features.

The present application relates to the technical field of production planning and operation optimization, in particular to a cigarette equipment capacity matching optimization method based on an improved NSGA-Ⅱ algorithm, comprising: obtaining basic data of matching of cigarette production resources and demand; calculating constraint parameters of each product according to the basic data; constructing a cigarette equipment capacity matching multi-objective optimization model based on the constraint parameters; solving the cigarette equipment capacity matching multi-objective optimization model based on the improved NSGA-Ⅱ algorithm to obtain an optimal capacity matching scheme. The embodiment of the present application constructs a multi-objective optimization model, covers capacity gap, surplus rate balance and overtime, and improves the accuracy and efficiency of the algorithm in a complex production environment through an improved initial population generation strategy and a self-adaptive crossover and mutation mechanism. The method can accurately match capacity and demand, reduce waste, overtime and uneven load, significantly improve production efficiency and resource utilization, and optimize cigarette production management.

The present invention discloses a method and system for designing low sidelobe patterns of sparse array antennas. The method comprises: initializing the population using Logistic‑tent chaotic mapping, and optimizing the fitness value of the sparse array antenna pattern through random motion and hard frost puncture crossover. By recalculating the fitness value and updating the position, the system outputs the optimization result when the maximum number of iterations or the accuracy of the solution is reached. The improved RIME algorithm: 1. When initializing the population, a chaotic function is introduced to make the movement and condensation of each rime particle more covering and random. 2. In the hard frost puncture stage, the Levy flight function is introduced to control the puncture distance during the hard frost puncture process, so that the area of ​​information exchange is wider, the information network is larger, and the result is more excellent. The present invention has diversity and randomness, can effectively avoid falling into the local optimum, and obtain the optimized pattern design result within a certain number of iterations, and can also be extended to optimization problems in other engineering fields.