18 results about "Population size" patented technology

The invention relates to a factory job shop scheduling control optimization method and device, equipment and a medium, and the method comprises the steps: classifying workpieces irrelevant to all MPBPO sets into a first workpiece set, classifying the workpieces corresponding to each MPBPO set into one set to determine a second workpiece set, independently generating job task sorting sub-chromosome segments, and generating a job task sorting sub-chromosome segment; corresponding operation machine numbers and operation time are respectively filled into corresponding gene positions of the operation machine sub-chromosomes and the operation time sub-chromosomes, and the operation task sorting sub-chromosomes, the operation machine sub-chromosomes and the operation time sub-chromosomes are combined to obtain initial chromosomes so as to generate initial population individuals; genetic manipulation is carried out on the initial population individuals, and population individuals with the high fitness and the number of the population individuals being the population scale are selected as a next-generation initial population; and repeatedly executing, and outputting the production scheduling scheme with the optimal earliest completion time. According to the method, algorithm premature convergence can be avoided in a generalized job-shop scheduling problem with a forced parallel batch processing procedure.

The invention discloses a network chain double-structure multi-body mutualism evolution analysis method, which comprises the following steps: constructing a multi-index system to determine the weight of each index, and further obtaining the population scale of a value co-creation unit according to the weight; on the basis of obtaining the population scale of the value co-creation unit, realizing symbiotic coefficient measurement and calculation by combining a relational expression between an intermediate parameter in the converted multi-subject symbiotic evolution model constructed by the multi-subject symbiotic evolution model and a symbiotic coefficient in the multi-subject symbiotic evolution model; and determining a symbiotic mode among the value co-creation units in the current ecological subsystem according to the obtained symbiotic coefficient. Furthermore, through the obtained symbiotic mode, the stable state of the ecological subsystem in long-term operation is analyzed in combination with numerical simulation, and conditions and support are provided for guiding the optimal advancing direction of the ecological subsystem.

This invention provides a quantitative prediction method for the spatiotemporal distribution changes of vole populations, belonging to the field of species distribution prediction in the ecological environment. The method includes: determining vole habitats based on their activity range; dividing the study area of ​​the vole habitat into an unstructured grid based on triangular units using a hydrodynamic model and a two-dimensional sediment model; constructing a vole habitat quality assessment model, a vole subpopulation migration model, a vole population size change model, and a vole stored energy change model; coupling these models for calculation, and predicting the distribution changes of the vole population based on the calculation results. This invention, through the coupled calculation of different models, can accurately predict the distribution range of vole based on the calculation results, which is of great significance for effectively controlling vole outbreaks.

The invention provides a self-adaptive knowledge migration multi-task optimization method based on a multi-population evolution framework. The method comprises the following steps: 1) initializing a population of each task; 2) selecting self evolution or migration evolution according to the migration probability to generate a filial generation, and using an enhanced migration strategy to improve the migration effect when an individual selects migration evolution; 3) generating a next-generation population through environment selection, combining a filial generation generated by migration evolution and a filial generation generated by self evolution, comparing the combined filial generation with a parent individual, selecting individuals with higher fitness to form the next-generation population, eliminating disadvantageous individuals through continuous iteration, and retaining a solution with better performance; 4) adaptively updating the migration probability; 5) reducing the size of the linear population, performing judgment after evolution of each generation of population is finished, and performing population reduction when conditions are met; and 6) when the maximum number of evaluation times is reached, outputting an optimal solution by the population. According to the method, the problem of continuous single-target multi-task optimization can be solved, and the core problem of a single-target multi-task optimization algorithm is improved.

The invention relates to a rectification system multi-objective optimization method based on dynamic NSGA-II, and belongs to the technical field of chemical process system optimization. The method comprises: determining a decision variable and a target function according to a rectification system model; a diversity index is obtained by calculating an invalid solution proportion of each generation of population and diversity of a target and decision space; and the crossover probability, the mutation probability and the population size are dynamically adjusted, a dynamic NSGA-II algorithm is constructed, the rectification system is optimized, and an optimal solution is obtained. According to the method, dynamic parameter adjustment is achieved by introducing the invalid solution proportion and the population diversity index, the number of non-convergent solutions can be effectively reduced, the population diversity is enhanced, premature convergence is avoided, the calculation cost is reduced while the optimization efficiency is improved, and an efficient and practical solution is provided for multi-objective optimization of a complex chemical system.

The invention relates to the technical field of communication, and particularly discloses a large-scale MIMO (Multiple Input Multiple Output) resource allocation method based on a genetic algorithm, which comprises the following steps: S1, setting the population size, and generating a candidate solution in each population; s2, calculating the fitness of each candidate solution in the population; s3, calculating the probability that each candidate solution is selected; s4, randomly selecting two candidate solutions in the population as a parent according to the selection probability, and mixing genes of the filial generation and the parent; s5, mutation operation is carried out; s6, repairing the generated offspring; s7, evaluating the obtained candidate solutions of the new offspring population, selecting an optimal solution in the current offspring population, if the current optimal solution is better than the global optimal solution, replacing the global optimal solution with the current optimal solution, otherwise, abandoning the current optimal solution; and S8, repeating the steps S1-S7 until the final result tends to converge. According to the technical scheme, the system performance and the resource utilization rate can be effectively improved.

The invention discloses an intelligent ore blending method based on a CEALA algorithm. According to the invention, the co-evolution ALA algorithm is applied to an intelligent ore blending scene. The population scale is dynamically adjusted according to the population evolution condition in the optimization process through a cyclic population attenuation mechanism, the calculation cost is reduced, and convergence is accelerated while the population diversity is guaranteed. Meanwhile, the algorithm provides a co-evolution mechanism, based on a self-adaptive exploration mechanism and a self-adaptive development mechanism, individuals are dynamically allocated to different evolution mechanisms, the influence of parameter setting on the optimization performance of the algorithm is reduced, exploration and development are balanced, population diversity is increased, and local optimum is avoided. A parameter adaptive mechanism is introduced into the algorithm to capture beneficial information in a population iteration process, so that the influence of parameter setting on the optimization performance of the algorithm is reduced, and the robustness of the algorithm is enhanced. And finally, the algorithm is based on external archiving, beneficial information of abandoned solutions is mined, and the optimization performance of the algorithm is improved.

The invention provides a production line personnel balancing method based on a genetic algorithm, a medium and a terminal. The method specifically comprises the following steps: carding basic information related to a production line and an allowable adjustment interval of the production line; establishing a target function based on the carding result; encoding the adjustment value of the production line into a chromosome gene based on the allowable adjustment interval of the production line; randomly initializing the number of populations; the fitness of each individual is calculated, and excellent genes are selected; genetic manipulation is carried out on the selected individuals, and an optimal solution in the current population is selected; repeating the previous two steps until the maximum number of iterations, comparing the optimal solution of each generation of population, and selecting a global optimal solution; and applying the global optimal solution to actual production line arrangement of a workshop to obtain balanced personnel attendance arrangement. According to the method, workshop production halt caused by the worker turnover problem is solved, production is more balanced, the production efficiency is improved, and the plan achievement rate is remarkably increased.

The invention discloses a method for predicting an endangered grade of madrepore under future climate change based on a species distribution model. The method comprises the following steps of (1) species distribution information collection, (2) environmental factor selection, (3) model construction and model performance evaluation, and (4) species eradication risk evaluation under climate change. The invention discloses a method for evaluating the endangered grade of madrepore based on a species distribution model for the first time, and the method makes up for the dependence of the traditional IUCN evaluation on the field population quantity and trend data. According to the scheme for predicting the suitable habitat of the species under the climate change of the madrepore, the endangered grade of the madrepore under the climate change can be evaluated. The method provides beneficial enlightenment for the endangered level in evaluating or updating the eradication risk state of marine species and designing protective measures.

The present invention discloses a design method for an optical phased array based on a multi-objective optimization algorithm, comprising: S1, initializing a population; S2, normalizing the objective functions of all solutions in the population; S3, performing non-dominated sorting on the population and associating it with a reference point; S4, judging whether the current population meets a termination condition, and if so, entering S5, and if not, entering S6; S5, outputting a Pareto front solution; S6, selecting, crossover, and mutating the population; S7, merging the offspring population obtained in S6 and the parent population to obtain a new population; S8, normalizing the objective functions of all solutions in the new population; sorting the new population through non-dominated sorting and associating with a reference point, selecting solutions layer by layer according to the non-dominated layer, and giving priority to solutions closest to the reference point until N solutions are filled, wherein N is the population size defined when the population is initialized; S9, using the solution selected in step S8 as the next generation population, updating the reference point and normalization parameters, and returning to step S4.

The invention provides a computer-implemented method for decision support in selecting crossings for population development, the method comprising: receiving a set of candidate parents, a set of traits, and one or more constraints comprising a resource constraint representative of a maximum number of crossings and / or a maximum progeny population size; carrying out a multi-objective optimization; evaluating a pareto front of the set of solutions and selecting a subset of one or more solutions within the set of solutions based on a result of evaluating the pareto front; selecting a solution of the subset of solutions; and providing the list of crossings of the selected solution and a predicted required number of progenies for each of the crossings comprised in the list of crossings.

We provide the system. [Solution] A system having a computer program that manages multiple organisms and simulates changes in the population size of each organism in order to mimic an ecosystem, A means for calculating the fluctuations in the population of the organism based on the birth rate and death rate, A means for displaying the year-to-year changes in the population size of each of the aforementioned organisms, A system including means for performing a simulation at predetermined time intervals to mimic the passage of time.

The present invention discloses a nearshore ecological environment safety early warning and dynamic process analysis system and method, which relate to the field of data processing. The present invention deploys sensors in the nearshore environment, collects current nearshore pollution data through the sensors, and collects population size data of predators and prey in the nearshore ecological environment based on the sensors and satellite remote sensing; detects whether the current predator population size and the current prey population size are within a reasonable range; and determines whether to issue an alarm; collects historical pollutant concentration data, and constructs a trend compliance model for pollutant concentration changes over time; constructs a nearshore ecosystem dynamics model; obtains predator population size data and prey population size data at future moments based on current nearshore environment data and the nearshore ecosystem dynamics model, detects whether the predator population size data and the prey population size data at future moments are within a reasonable range, and determines whether to issue an alarm.

The application relates to a multi-agent control strategy optimization method for multi-population multi-stage constraint multi-objective based on reinforcement learning, which comprises the following steps: generating a population matrix; generating optimal population sizes of various populations; screening parent individuals of an exploratory operator and a development operator from the population, and generating evolutionary offspring individuals; performing preliminary screening of the offspring individuals based on the parent individuals; judging the convergence state of the population according to the total sum difference of objective function values of adjacent two generations of individuals in the population and the number of non-dominated solutions in the population, so as to trigger the switching of the constraint processing mode; screening the offspring individuals and the parent individuals, updating various populations; if the iteration number reaches the maximum iteration number, outputting an external population, and obtaining an optimal strategy of current multi-agent control based on the external population; otherwise, increasing the iteration number by 1 and performing action selection. Compared with the prior art, the application can give an approximate optimal unmanned aerial vehicle three-dimensional deployment scheme, a device transmission power scheme and a computing resource allocation strategy.

A method includes receiving a plurality of original features and a ground truth, crossing over the plurality of original features to generate a plurality of current features, generating a subsequent generation of features by: calculating a current importance score of each of the plurality of current features in reference to the ground truth, calculate a parent-pool size, a population size, and a mutation rate, determining the parent-pool size number of current features having the highest current importance scores to identify a plurality of current parent features, crossing over, based on the mutation rate, the plurality of current parent features to generate the population size number of subsequent features, iterating the generating process to generate a plurality of subsequent generations of features until fitness scores of a predetermined number of consecutive generations of features stop rising and adding the parent features to the original features to form an enhanced dataset.

This invention proposes a method for predicting airborne infectious diseases based on population movement and contact intensity, belonging to the field of infectious disease transmission prediction. The method includes: S1: Preprocessing population movement trajectory data to obtain preprocessed trajectory data; S2: Based on the preprocessed trajectory data, identifying contact events between people within a set time window and distance threshold, and constructing a contact matrix; S3: Establishing a RealSEIR model based on basic data on airborne infectious diseases in the target area and prediction requirements; S4: Establishing a disease model differential equation system based on the RealSEIR model; establishing disease model compartment transmission rules based on the disease model differential equation system and the contact matrix; S5: Calculating the population size in each state based on infectious disease prediction requirements, the contact matrix, and the disease model compartment transmission rules, and predicting the epidemic trend of airborne infectious diseases. This invention's method can accurately predict the transmission trend of infectious diseases.

The invention relates to the technical field of pest monitoring, and discloses a termite population number estimation method and system using diet special effects, and the system comprises a writable disturbance preference model and a read-only reference vitality model; in the disturbance estimation mode, opening a B chamber channel, and calculating an estimated population value by using a disturbance preference model; switching to a reference calibration mode, closing a B chamber channel, and calculating a calibration anchor point by using a reference vitality model; updating the disturbance preference model on line according to the calibration anchor point; and switching back to the disturbance estimation mode by using the updated model. The system comprises a data acquisition terminal and a data processing system, and the data processing system is provided with a state machine module, a dual-model storage module, a data decoupling calculation module and a dynamic response baseline calibration module so as to realize mode switching, closed-loop calibration and population value calculation of the method. By means of dual-mode switching and closed-loop calibration, the problem of model drifting is solved, the population number and the ingestion activity are decoupled, and high-precision estimation of the termite population is achieved.

This invention discloses a method for calculating optimal contribution for breeding and mating selection in endangered and rare cattle and sheep populations. The method includes: acquiring pedigree data and genomic information of the rare cattle and sheep; constructing a pedigree matrix based on the pedigree data; constructing a genomic kinship matrix based on the genomic information; minimizing the average kinship of the population using a genetic algorithm based on the pedigree matrix or genomic kinship matrix to determine the set of individuals to be culled; calculating the optimal contribution value of each individual using a simulated annealing algorithm for the set of individuals to be retained; and minimizing the average kinship between all pairs using a simulated annealing algorithm based on the optimal contribution values ​​of all individuals to obtain the target mating scheme. This invention balances constraints such as sex ratio and population size with kinship control, aiming to minimize the average kinship coefficient of the next generation population, greatly reducing the risk of inbreeding and ensuring population genetic diversity.