4results about How to "Increase population diversity" patented technology

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 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.