154 results about "Population size" patented technology

The present invention provides a method, apparatus, and computer instructions for improved control of clinical trials. In a preferred embodiment, after a clinical trial is initiated, data is regularly cleaned and processed to statistically analyze the data. The outcome includes a predictive measure of the timing and level by which the study will achieve one or more statistically significant levels, allowing mid-course modifications to the study (e.g., in population size, termination, etc.). Modification can be planned as part of the initial protocol, using thresholds or other appropriate criteria relating to the statistical outcome, making possible pre-approved protocol changes based on the statistical findings. This process has significant implications for the management of clinical studies, including ensuring the minimum possible time and number of patients are used in clinical studies to either prove (or disprove) the clinical efficacy of drugs or treatments.

A hybrid evolutionary algorithm (“HEA”) technique is described for automatically calculating well and drainage locations in a field. The technique includes planning a set of wells on a static reservoir model using an automated well planner tool that designs realistic wells that satisfy drilling and construction constraints. A subset of these locations is then selected based on dynamic flow simulation using a cost function that maximizes recovery or economic benefit. In particular, a large population of candidate targets, drain holes and trajectories is initially created using fast calculation analysis tools of cost and value, and as the workflow proceeds, the population size is reduced in each successive operation, thereby facilitating use of increasingly sophisticated calculation analysis tools for economic valuation of the reservoir while reducing overall time required to obtain the result. In the final operation, only a small number of full reservoir simulations are required for the most promising FDPs.

The invention is nerve net system which can realize heredity arithmetic. It is made up of computer nerve net model component and the interfaces. The character lies in: 1. after that the computer setsthe population size, coding type and length, heredity operation probability and the arithmetic ending condition of the arithmetic, the nerve net uses population size to realize the whole heredity operation including selection, crossing, mutation and personal adaptability value, and outputs the optimized calculation and result through computer; 2. designs the heredity operation nerve net model which can realize multi-father crossing operation and multi-gene mutation operation, realizes the two operation of two-value coding heredity arithmetic and real number coding heredity arithmetic.

The invention aims at providing a method for collecting spatial population distribution in real time on the basis of mobile phone big data and realizing large passenger flow early warning. The method is characterized by comprising the following steps of obtaining real-time mobile phone big data from a mobile phone communication operator at each statistics moment; dividing a target region into different space regions; at the current statistics moment, mapping the latest space position of each EPID (Electronic Portal Imaging Device) obtained in the first step to each space region of the target region; and obtaining the real population size (the distribution condition of the total population in the whole target region) in each space region at the current statistics moment. The method has the advantages that the sufficient relying on the existing mobile phone big data resources is realized; the continuous encrypted position information of the existing mass anonymous mobile phone users in the mobile communication network is used; the low-cost high-frequency and automatic implementation can be realized; and the popularization distribution data in a large-range urban space range can be continuously obtained in a fast deploying way.

The invention discloses a method and a device for route optimization of logistics delivery vehicle, and belongs to the technical field of logistics. The method comprises the following steps of: initializing a congestion matrix alpha and a distance matrix D, generating a delivery route weight matrix omega=alpha D, and initializing a population module N<ZQ>; selecting a population size N<X>, a maximum number of generations N<G>, a crossing-over rate beta, a mutation rate gamma and a number of generations n=0, generating an initial route r1 through a greedy algorithm, and performing mutation operation on the initial route r1 to generate N<ZQ>-1 new routes; calculating fitness A<n> of each route of a first generation population formed by the initial route and the new routes, selecting N<X> routes with the highest fitness from the current population by adopting selection operators, and performing crossover and mutation operations on the N<X> routes to generate a population of next generation; updating n=n+1, when n=N<G>, calculating the fitness A<n> of all the routes in the latest population, and selecting the delivery route with the highest fitness in the current population as the optimal route. According to the invention, when the logistics delivery vehicle delivers goods, the delivery time can be as less as possible, and the delivery route can be as short as possible.

The invention relates to a multilayer obstacle-avoiding Steiner minimal tree construction method for a very large scale integration. The method includes the following steps: 1, reading benchmark test circuit network data; 2, initializing parameters such as population sizes and iterations, and generating initial populations randomly; 3, updating positions and speeds of particles according to a particle updating formula; 4, calculating fitness values of new particles according to a punishment mechanism based fitness calculation function, judging whether or not the fitness values of the new particles are smaller than historical optimal values of the particles, and if yes, updating the new particles as historical optimal particles of the particles; 5, judging whether or not the fitness values of the new particles are smaller than global optimal values of the populations, and if yes, updating the new particles as global optimal particles of the populations; 6, judging whether iteration end conditions are met or not, if yes, outputting final wiring trees, and if not, returning to the step 3 for next iteration. By the method, total wiring cost is reduced, and quality of the wiring trees is improved.

The invention discloses a method for solving the traveling salesman problem based on an improved genetic algorithm. The steps include: aiming at the TSP problem, encoding the path using a decimal number string; calculating the total length, and then judging the total length; after encoding On the search space U of the decimal number string path, define the fitness function f(x), and define the population size n, the crossover probability Pc, the mutation probability Pm and the number of iterations T; in the search space U, randomly generate n individuals s1, s2, s3, ..., sn, constitute the initial population S0 = {s1, s2, s3, ..., sn}, set the current iteration number t = 0; according to the fitness function f(x), evaluate the individual fitness in the population , if t<T, then end the step, otherwise perform the genetic operation step; the individual with the highest fitness obtained through the genetic operation step is the optimal solution of the traveling salesman problem solving method. Based on the traditional genetic algorithm, the present invention optimizes the traveling salesman problem to achieve the purpose of improving the shortcoming that the algorithm is prone to premature convergence and optimizing the search efficiency.

The invention provides a transmission and transformation project construction cost assessment method and device. The transmission and transformation project construction cost assessment method comprises the following steps: input historical sample data of a transmission and transformation project are received; iterations, inertia weight, learning factors, particle velocity of a chaos particle swarm and the population size of the particle swarm are initialized to build a chaos particle swarm model; according to the chaos particle swarm optimization, parameters of the chaos particle swarm model are optimized; according to the historical sample data and the optimized chaos particle swarm model, optimal values of the iterations, inertia weight and learning factors of the chaos particle swarm model are determined; according to the determined optimal values of the iterations, inertia weight and learning factors, penalty coefficients, insensitive coefficients and kernel function parameters of a least square support vector machine model are determined respectively to build the least square support vector machine model; input actual sample data of the transmission and transformation project are received; according to the actual sample data of the transmission and transformation project and the built least square support vector machine model, a construction cost assessment result of the transmission and transformation project is generated.

The invention discloses a batch scheduling method for flexible job shops based on genetic algorithms. The steps of the method are: (1) Determine the operating parameters, including population size M, crossover probability P _C , the mutation probability P _M , the number of iterations T; (2) Initial population generation, using segmented coding method to generate batch codes and process codes; (3) Individual fitness calculation, taking the reciprocal of the individual’s total completion time as its fitness value; (4) Select Operation, using the roulette selection operator; (5) crossover operation, setting crossover execution criteria, performing crossover on batch codes or process codes according to the criteria, and repairing after crossover; (6) mutation operation, using multiple Point mutation, using reverse sequence mutation for the process code; (7) Termination discrimination, judging whether the number of generations meets the termination condition, stop if it is satisfied, and output the optimal scheduling plan, otherwise go to (3). The invention can optimize the production operation of the flexible workshop, effectively shorten the production cycle, has strong applicability and is easy to popularize.

The invention discloses a multi-workflow scheduling method based on a genetic algorithm under a cloud environment. The method comprises the following steps that a previous workflow scheduling state isreserved, the genetic algorithm and a new workflow are initialized, the adaptability degree of each individual of the new workflow is calculated, and two parent individuals are selected; according tothe genetic algorithm, the parent individuals are subjected to cross operation and single-point variation, progeny individuals are obtained, the adaptability degrees of the progeny individuals are calculated, the adaptability degrees of the progeny individuals and the corresponding parent individuals are compared, and two smaller progeny individuals are selected and added to the progeny population; if the size of the progeny population is equal to that of the parent population, the progeny population and the parent population are merged, the individuals which accord with the genetic algorithmare selected from the merged population to form the new population, and otherwise, the step of selecting the parent individuals again is skipped to; finally, according to the iteration frequency, optimal scheduling is output. According to the multi-workflow scheduling method based on the genetic algorithm under the cloud environment, the situation is avoided that previous workflow scheduling is damaged so that additional communication cost can be generated, and the utilization rate of computing resources of a virtual machine is further increased.

The invention discloses an SAR image change detection method based on quantum-inspired immune clone, which mainly solves the problems of long time consumption, easy falling into local optimal solution, and inaccurate positioning of complex image borders in the prior optimizing method. The method comprises the following steps: (1) filtering the two time-phase to-be-changed detection images, and calculating the logarithmic ratio difference striograph; (2) setting the population size, class number k and halt conditions, and randomly generating a quantum antibody Q(t) as the initial clustering center; (3) observing Q(t) into a binary antibody p(t), calculating the affinity fk of each antibody, and reserving the optimal antibody qbest of Q(t); (4) carrying out mutation operation on Q(t) to obtain Qm(t); (5) recombining Qm(t) to obtain Qc(t); (6) observing Qc(t) into a binary antibody pc(t), and calculating the affinity fc of each antibody; (7) selectively operating pc(t) to obtain the filial generation antibodies; and (8) if the filial generation antibodies satisfy the halt conditions, dividing the image class corresponding to the antibody with the highest affinity in the filial generation antibodies as the output result. The invention has the advantages of high change detection precision and accurate border positioning, and can be used for detecting changes of complex images.

The invention relates to a particle swarm optimization method based on a complex network. The particle swarm optimization method is used for solving the multiobjective optimization problem in the real world. The particle swarm optimization method based on the complex network comprises the steps that the population network topology is established according to a scale-free network generation mechanism, the optimization space, the population size, the positions of particles and the speeds of the particles are determined, the adaptive value is calculated according to a fitness function, the historical best position of each particle, the historical best position of the corresponding neighbor particle and the global historical best position of the particles are recorded, the positions and the speeds of the particles are updated in an iteration mode every time, the adaptive value is calculated again until iteration is completed, and the global best position is output. The particle swarm optimization method based on the complex network further provides four indexes for evaluating the optimal performance of center particles and non-center particles, the influence in neighborhood, the information transmission capacity, the advantages and disadvantages of the adaptive value and the capacity for maintaining population activeness. By means of the particle swarm optimization method based on the complex network, the local optimum can be effectively avoided, and the convergence rate and the optimization effect for resolving targets are balanced through the application of the particle swarm optimization algorithm.

An optimized scheduling method based on a particle swarm optimization algorithm and used for a multi-lift-car elevator cluster includes the following steps that passenger flow information is generated through a passenger flow generator, wherein the passenger flow information is a hall layer elevator calling signal; parameter initialization is conducted, wherein parameters include the parameters including cluster size, the iteration number and the like of the adopted particle swarm optimization algorithm and the comprehensive evaluation function determined according to elevator running information; optimized calculation is conducted through the particle swarm optimization algorithm, the optimal solution is obtained through the limited times of iteration, and the optimal elevator dispatching scheme is determined; and the elevator dispatching scheme is converted into a control signal, elevator calling signals are reasonably distributed for all elevators, operation of all the elevators is coordinated, and therefore optimized scheduling of the elevator cluster is achieved. Because the update strategy of the global extremum is improved in the scheduling algorithm, optimal particles which are evenly distributed are obtained, the algorithm convergence and the scheduling performance are improved, meanwhile, it is avoided that the danger of collision may happen when lift cars run in the same shaft, and running safety of a whole elevator system and validity of the scheduling algorithm are ensured.

The invention discloses a method for synthesizing directional diagrams of linear antenna arrays on the basis of wavelet mutation wind drive optimization algorithms. The method includes steps of building models of the linear antenna arrays and determining comprehensive radiation characteristic requirements and objective functions of the antenna arrays; determining the wind drive optimization algorithms and wavelet mutation operator parameters and setting population sizes, weight values of fitness functions and speeds and position boundaries of air particles; randomly generating initial speeds and positions of the air particles, substituting the positions of the air particles into the fitness functions, sorting fitness values according to ascending order, updating population sequences and determining the global optimal positions and the local optimal positions; updating the speeds and the positions of the air particles; selectively carrying out wavelet mutation on the positions of the air particles according to mutation probability; computing fitness values of the air particles at novel positions, sorting the fitness values according to ascending order again, updating the population sequences and updating the global optimal positions and the local optimal positions until the maximum number of iterations are carried out. The method has the advantages of high solving precision and convergence speed.

The invention discloses a particle swarm optimization user request dispatching method facing multi-type service. The method comprises the following steps of: 1, initializing parameters, population size and particle construction of a particle swarm optimization algorithm, and calculating computer resource consumed by each service type and loads of server nodes in a cluster; 2, updating the allocation weight of each node of the server cluster, and calculating a fitness function of the cluster; and 3, combined with the set particle swarm parameters, updating the velocity and position of each particle in the particle swarm so as to enable the particles to approach a globally optimal solution. According to the invention, the priority of each server is determined based on the priority of user requests and the resource leisure rate of each server, then the received user requests are dispatched initially according to the priority of the servers, the priority is changed dynamically in real time according to the load state of each server, the load result is optimized by adopting a particle swarm thought, and the particle swarm algorithm is avoided to fall into a locally optimal solution.

The invention discloses a method for optimizing the project duration of an engineering project based on potential anti-key working procedures in the technical field of engineering project duration control technologies. The method comprises the following steps: identifying potential anti-key working procedures in an engineering project based on certain technical characteristics; dividing all working procedures of the project into a potential anti-key working procedure set X and a non-potential anti-key working procedure set Y; coding the execution modes of all the working procedures in the potential anti-key working procedure set X to generate an initial group of which the size is NP, and adopting a mode of fastest execution for all the working procedures in the non-potential anti-key working procedure set Y; calculating the total duration value corresponding to each single body in the group and converting the reciprocal of the total duration value into the adaptation value of the single body; adjusting the start time of non-key working procedures; selecting a parent, and producing a child by a single-point crossover operator and a single-point mutation operator; combining the parent and the child to form a new group; and if the maximum genetic algebra is obtained, stopping calculation and outputting an optimal solution, thus obtaining the optimal duration scheme of the project.

A construction method of a test case constraint control technology based on epigenetics includes steps of: 1: defining a fitness function, a genetic coding method, and a constraint control rule; 2: initializing parameters: setting a population size, an evolution number, and a termination fitness function value; 3: initializing a population: randomly generating an initial population; 4: performing evolution termination judgment; 5. performing constraint regulation based on the epigenetics: performing constraint methylation and constraint acetylation according to the constraint control rule; 6: selecting individuals; 7: completing epigenetic evolution of the population; and 8: outputting a test case set; wherein through the above steps, construction of an epigenetic test case constraint control technology is completed, so as to design test cases that are more in line with the actual operation conditions of the software, find more potential software failures, and improve the quality of software testing.

The invention discloses a cognitive radio method based on an improved genetic algorithm in the technical field of radio communication. The cognitive radio method comprises the steps of: setting initial parameters of cognitive radio and using the initial parameters as chromosomes of the genetic algorithm; setting initial mutation probability, population size and maximum evolutional generations, and setting target functions which reflect current link quality and the weight of each target function; calculating a population fitness value; conducting scale transformation of the fitness value; selecting the chromosomes; using self-adaptive crossover probability and mutation probability to conduct two-point crossover and individual mutation on the chromosomes; judging whether a condition of convergence is reached or not, and if not, returning to calculate the population fitness value; and if so, outputting a result set and using the results as the parameters of the cognitive radio. The cognitive radio method solves the problem that the final solution set is apt to be converged into a locally optimal solution in the genetic algorithm, and guarantees the diversity of populations and the convergence of the genetic algorithm at the same time.

The invention discloses an image enhancement method based on an adaptive immunity genetic algorithm. The image enhancement method based on the adaptive immunity genetic algorithm includes steps that S1, normalizing an image pixel gray level f(x, y) to obtain n(x, y); S2, coding parameters (alpha, beta) to be optimized, randomly generating a group of initial individuals to form an initial population, and inputting a control parameter crossover probability p<c>, a mutation probability p<m>, a population size N, a maximum running algebra G and the like; S3, judging whether an evolution algebra t is equal to G, if so, ending the algorithm, and outputting the optimal solution of (alpha, beta), otherwise, turning to the next step; S4, using a roulette strategy to select M individuals, and carrying out crossover and mutation operations on the individuals according to crossover and mutation methods in genetic operation; S5, selecting two vaccines, the individuals to be vaccinated and a vaccination point number to perform immunization, making a immunization choice after the vaccination, and using the optimal individual retention strategy for the vaccinated population; S6, obtaining the corresponding nonlinear transformation function F(u) of each group of (alpha, beta), and using the nonlinear transformation function to perform an image gray level transformation to obtain an output image g(x, y).

The invention discloses a variable step size constellation orbit optimization method and device based on a genetic algorithm. The method comprises: the orbital plane number of a target satellite constellation and the satellite number of each orbital plane are set; initializing to obtain a genetic algorithm population according to the number of orbital planes and the number of satellites, and setting preset parameters of a genetic algorithm; wherein the preset parameters of the genetic algorithm comprise population size, crossover rate, variation rate and maximum genetic algebra; calculating toobtain the time resolution of the satellite constellation by adopting a variable step size strategy, and taking the time resolution as a fitness function; according to the fitness function, the crossover rate and the variation rate, performing crossover and variation processing on a part of individuals randomly selected from the genetic algorithm population; and under the condition that selection, crossover and mutation operations are determined to be completed according to the maximum genetic algebra, obtaining the individual with the highest fitness value in the new generation of population, and decoding to obtain the constellation configuration. According to the invention, the time precision of constellation orbit optimization can be improved, the optimization time is reduced, and thecoverage of better time resolution is realized.

The invention discloses a modeling method for a catalytic cracking main fractionator with a varying-population-size DNA genetic algorithm, comprising the following steps: 1) that input and output data of the catalytic cracking main fractionator are collected as modeling data through on-site operation or experimental sampling; 2) that the modeling data are used to train a support vector machine, and a mean square deviation collected by cross validation is taken as an objective function; 3) that operational parameters of the DNA genetic algorithm are set; and 4) that the varying-population-size DNA genetic algorithm is operated to optimize the parameters of the support machine, wherein collected optimization parameters of the support vector machine are used for training the support vector machine and further collecting a nonparametric model of the catalytic cracking main fractionator. The method of the invention combines the varying-population-size DNA genetic algorithm with the support vector machine, and at the same time introduces a mutation operator enlightened by flora drug resistance into the nonparametric modeling of the catalytic cracking main fractionator, thereby effectively increasing modeling precision of the support vector machine.

Selection of a trait may be received. A complex segment rule may be created that is usable to evaluate one or more qualification events. For example, the segment rule may be usable to evaluate a combined recency and frequency of the one or more qualification events. The qualification events may be based on collected network data associated with the plurality of visitors with each qualification event corresponding to a separate qualification of visitor according to the trait. The qualification events may be evaluated together according to the segment rule. For example, the combined recency and frequency of the one or more qualification events may be evaluated according to the segment rule. Evaluating the segment rule may include estimating a segment population size in real-time.

The invention discloses a population size counting method of double cameras. The counting method comprises the steps as follows: setting a target camera A and an assistant camera B in a monitoring scene and respectively calculating characteristics of crowd block data sets in video frames of two cameras; using a transfer learning algorithm to extract sample data adjacent to the characteristic of a target population data set from an assistant population data set as a migration population data set; combining the migration population data set and the target population data set as a new population data set; fusing pixel feature and textural feature of the population data set according to a difference value between a predicated population number and real population in shaded population block in the new population data set; training a population size statistic model according to a fusion feature; using the trained population size statistic model to predict the population of newly input population image. The population size counting method of the invention solves the problem of a large number of population data scaling, unbalanced population data distribution and data missing via transfer learning, and gives full play of characteristic of different type features via feature fusion, thereby improving accuracy of the population size statistic model.