360 results about "Mutation (genetic algorithm)" patented technology

Sensors are selected from a sensor network for tracking of at least one target. The sensors are selected using a genetic algorithm construct having n chromosomes, wherein each chromosome represents one sensor, defining a fitness function based on desired attributes of the tracking, selecting one or more of the individuals for inclusion in an initial population, executing a genetic algorithm on the initial population until defined convergence criteria are met, wherein execution of the genetic algorithm has the steps of choosing the fittest individual from the population, choosing random individuals from the population and creating offspring from the fittest and randomly chosen individuals. In one embodiment, only i chromosomes are mutated during any one mutation, wherein i has a value of from 2 to n−1.

The invention relates to a mobile-robot route planning method based on an improved genetic algorithm. A raster model is adopted to preprocess a working space of a mobile robot, in a rasterized map, an improved rapid traversing random tree is adopted to generate connections of several clusters between a start point and a target point, portions for the mobile robot to freely walk on in the working space are converted into directed acyclic graphs, and a backtracking method is adopted to generate an initial population which is abundant in diversity and has no infeasible path on the basis of the directed acyclic graphs. Three genetic operators, namely a selection operator, a crossover operator and a mutation operator, are adopted to evolve the population, wherein the selection operator uses a tournament selection strategy, the crossover operator adopts a single-point crossover strategy, and the mutation operator adopts a mutation strategy which displaces an aberrance point with an optimal point in eight-neighbor points of the aberrance point. A quadratic b-spline curve is adopted to smooth an optimal route, and finally, a smooth optimal route is generated. According to the method, the route planning capability of the mobile robot under a complex dynamic environment is effectively improved.

The invention discloses an intelligent layout method used for a rectangular part. The method comprises the steps that S1 relative parameters of the genetic algorithm are initialized; S2 relative information of the rectangular part is extracted from a rectangular part bank to be laid out; S3 relative information of raw material boards is extracted from a board tank; S4 the obtained information is coded, and primary species are generated randomly; S5 one-by-one decoding is conducted on the primary species by means of the lowest horizontal line search algorithm to obtain solution using efficiency; S6 selection, crossover and mutation operation is conducted according to the genetic algorithm until iteration is finished, and the optimal layout scheme is output. According to the intelligent layout method, the process requirement of the rectangular part can be met well, the intelligent algorithm and the heuristic algorithm are combined, one optimizing scheme can be found rapidly and efficiently, and therefore the material using rate of an enterprise is greatly improved, layout time can be obviously shortened, and layout efficiency is improved.

The invention relates to a three-dimensional encasement novel genetic algorithm model under a multi-constrain condition. At present, the logistics transportation industry rapidly develops, but encasement plan decision models are not perfect; particularly, a three-dimensional encasement model under the multi-constrain condition has the following typical problems: 1, the time complexity is higher; 2, the space utilization ratio is lower; 3, a encasement plan cannot be perfect. The design considers the constraint conditions such as the space utilization ratio, the centre-of-gravity position and the load bearing in the encasement problem, combines an improved genetic algorithm with a Monte Carlo method, a gene injection algorithm and a non-dominated sorting algorithm, aims at improving the space utilization ratio of the encasement plan and reducing the time complexity of the algorithms under the multi-constrain condition, and belongs to the field of intelligent arithmetic optimization. The three-dimensional encasement novel genetic algorithm model is mainly characterized in that population is initialized through the Monte Carlo method based on normal distribution; the gene injection algorithm is used in the encasement decision mode; the probability of crossover, mutation and gene injection operators is fitness functions; an online space combining method is used.

A field ready, unsupervised-use ready, method and apparatus for audio fitting a hearing aid is described in a hand held configuration having paired comparisons (hearing selections) stored in and derivable from a memory therein. The paired comparisons are presented one at a time to a user and a preferred selection for each paired comparison is made by a select indicator after the user toggles back and forth between the selections for as many times necessary in determining their preferences. A genetic algorithm converges all the preferences upon a single solution. Crossover and mutation genetic algorithm operators operate on a linear range of indexes representative of parametric values of the pairs. A fully integrated hearing aid having all the above described features incorporated therein is also presented.

The invention discloses an intelligent stock-layout optimization method for woodworking sheet parts. The intelligent stock-layout optimization method includes the steps of S1, initializing relative parameters of a genetic algorithm, S2, selecting information of rectangular parts to be subjected to stock layout from a part library, S3, selecting relative information of woodworking sheets, capable of being used for stock layout of the rectangular parts, from a woodworking stock library, S4, encoding the information of the rectangular parts to be subjected to stock layout and generating an initial population randomly, S5, decoding the initial population one by one by a surplus rectangle filling based 'guillotine cutting' algorithm to acquire utilization rate of each stock layout scheme, and S6, optimizing the stock layout schemes by means of selection, crossover and mutation operations through the genetic algorithm, and outputting the optimal scheme correspondingly. According to the intelligent stock layout optimization method, 'guillotine cutting' process requirements for woodworking rectangular parts can be met, the optimal scheme can be found rapidly by means of combination of the intelligent algorithm and the heuristic algorithm, and thus, stock layout time is shortened obviously while material utilization rate of enterprises is increased greatly, and stock layout efficiency is increased.

The invention discloses a parameter optimization control method of semiconductor advance process control (APC). In semiconductor technological process, a traditional method uses a linear prediction model for the optimization control method of batch process. The parameter optimization control method of the semiconductor advance process control uses an optimized back propagation (BP) neural network prediction model based on genetic algorithm, optimizes the initial weight values and threshold values of the neural network through the genetic algorithm, uses selecting operation, probability crossover and mutation operation and the like according to the fitness function F corresponding to each chromosome, and outputs the optimum solution finally to determine the optimum initial weight value and the threshold value of the BP neural network. The performance of the BP neural network is improved with an additional momentum method and variable learning rate learning algorithm being used, so that the BP neural network after being trained can predict the non-linear model well. The genetic algorithm in the method has good global searching ability, a global optimal solution or a second-best solution with good performance is easy to obtain, and the genetic algorithm well promotes the improvement of modeling ability of the neural network.

The embodiment of the invention discloses an allocation method and device for multitasking of an unmanned aerial vehicle. The method comprises the steps that location information of the unmanned aerial vehicle and multiple target points and motion parameters of the unmanned aerial vehicle and a wind field are obtained; according to the location information and a preset genetic algorithm, an initial population taking an European-style flight path as an individual is built; the flight state of the unmanned aerial vehicle and the running time of the track passage of the European-style flight path are determined according to the motion parameters of the initial population, the unmanned aerial vehicle and the wind field, and the running time corresponding to chromosomes in the initial population is obtained according to the running time of the track passage and an SUAV-VS-EVRP model; on the basis of the genetic algorithm, cross and mutation processing is conducted on the chromosomes in the initial population, and after the predetermined number of iterations is achieved, the European-style flight path with the shortest running time is selected as the optimal flight path of the unmanned aerial vehicle. Accordingly, the unmanned aerial vehicle track planning problem is combined with the actual flight environment of the unmanned aerial vehicle, and the optimal flight path scheme obtained through planning is superior to the unmanned aerial vehicle constant speed scheme.

The embodiment of the invention provides a customer classification method and device based on an improved particle swarm optimization algorithm, and the method comprises the steps: initializing a particle speed and a particle position according to a classification number and a feature dimension, and setting an initial value, so as to build an initial population of a particle swarm; performing iterative updating operation on the inertia weight, the particle speed and the particle position of the population according to a preset fitness function including the customer characteristic data until apreset iteration frequency is reached; after the number of iterations is preset, respectively carrying out selection operation, crossover operation and mutation operation on the particle swarm according to a genetic algorithm after each update for next iteration update until the iteration update reaches the total number of iterations or meets a convergence condition; and obtaining a clustering center according to the particle swarm reaching the total number of iterations or meeting the convergence condition, and classifying the customers. According to the method, through organic fusion of thegenetic algorithm, falling into a local optimal solution can be avoided, the later convergence speed is increased, and the search precision is improved.

The invention discloses an electric power Internet of Things task allocation method based on edge cooperation. The method comprises the steps that a two-point cooperation task allocation model is established according to all service requests and EN idle resources of the electric power Internet of Things at a certain moment; a task allocation problem model is constructed by taking the minimized average task completion delay as an optimization target; and the task allocation problem model is solved by applying a TENCTA-IPSO algorithm. According to the method, the task completion time delay of the power grid service is effectively reduced. According to the TENCTA-IPSO algorithm, a particle updating strategy in a particle swarm algorithm is improved by utilizing crossover and mutation operations of a genetic algorithm; the particle swarm diversity is improved while the self-learning ability of the particles is kept, premature is prevented from falling into local optimum, and the calculatedtask allocation scheme shortens the average task completion delay to the maximum extent.

Provided is a method for selecting a broadcast routing path using a genetic algorithm in an Ad-hoc network. In the method, a plurality of nodes of the Ad-hoc network is defined as one chromosome, and the chromosome is represented with pairs of {order, power} in each node. Child nodes are created by performing an order based crossover and a power based crossover with respect to parent nodes neighboring to a source node. A mutation operation is performed with respect to the parent nodes and the child nodes. Relay nodes are determined by converting order and power information of each node of the chromosome into routing tree information. Power of the relay nodes is determined. A broadcast routing path is selected using the number and power of the relay nodes.

The present invention discloses a method for combinatorial layout design, in particular family mold layout design, using a computer. The method starts with receiving design specific parameters and information about molding parts from user. A Genetic Algorithm module is then invoked to generate a population of layout designs which satisfies the design specific parameters. The Genetic Algorithm module first automatically generates a population of specially designed chromosome with three interdependent sessions. Crossover, mutation and replacement operation are applied on the population subsequently to evolve such towards a more optimal population over successive generations. In each generation step, a Genotype-Phenotype mapping module is utilized to decode the chromosome to corresponding layout design for fitness evaluation. A system for combinatorial layout design automation and optimization using this evolutionary design approach is also disclosed in the present invention.

The invention discloses an energy spectrum overlapping peak analysis method. The analysis method includes the steps that background rejection is carried out on energy spectrum sections which are obtained from radioactive measurement and to be subjected to overlapping peak resolution, and net peak areas of overlapping peaks and all channel net counts corresponding to the net peak areas of the overlapping peaks are obtained; the energy spectrum sections obtained after background rejection are regarded as a linear sum of multiple Gaussian functions; parameters of the Gaussian functions are combined into a chromosome; population initialization is carried out on the combined chromosome, probability construction fitness functions, from individuals, of the energy spectrum sections are combined, selection, cross and mutation operators of a genetic algorithm are adopted, the weight, average and standard deviation of all the Gaussian functions are obtained after multi-generation operation, and overlapping peak resolution is completed. Calculation is simple, the overlapping peaks overlapping three or more spectrum peaks can be resolved, and the overlapping peak resolving method can be effectively applied to qualitative and quantitative analysis for the spectrum peaks and is good in performance.

Provided is a method for selecting a broadcast routing path using a genetic algorithm in an Ad-hoc network. In the method, a plurality of nodes of the Ad-hoc network is defined as one chromosome, and the chromosome is represented with pairs of {order, power} in each node. Child nodes are created by performing an order based crossover and a power based crossover with respect to parent nodes neighboring to a source node. A mutation operation is performed with respect to the parent nodes and the child nodes. Relay nodes are determined by converting order and power information of each node of the chromosome into routing tree information. Power of the relay nodes is determined. A broadcast routing path is selected using the number and power of the relay nodes.

The invention relates to a soft measurement method for granularity of cement raw material grinded by ball mill. The method comprises: selection of auxiliary variables, wherein a rotating speed of a powder concentrator, a feeding flow, an inlet wind pressure of the powder concentrator and a load of the powder concentrator are used as auxiliary variables of the soft measurement model for granularity of finished products; Data pre-treatment, wherein original data are screened, filtered and standardized; soft measurement modeling based on GA-NN (genetic algorithm optimized neural network), wherein a learning algorithm used for neural network training is brought forward based on GA (genetic algorithm), weights needed to be adjusted in a network are recognized as chromosomal genes, and the network training process is completed through chromosome selection, crossover and mutation; and correction of the soft measurement model by using a long-term correction way. The method can measure quite important finished product granularity parameters which are hard to be directly detected, thereby being capable of implementing advanced control and optimized method for a raw material grinding process by the ball mill.

Systems and methods are disclosed that utilize a genetic algorithm to search for an index configuration for a collection of data such as, e.g., a database. Genetic algorithms can include stochastic search heuristics that mimic processes of natural evolution including inheritance, mutation, crossover, and selection. A population of chromosomes representing candidate index configurations can evolve to increase or optimize the fitness of the population and to identify the best (e.g., most fit) index configuration. Fitness of a chromosome may be measured based at least in part on the cost of computer resources used for executing Structured Query Language (SQL) statements in the indexed database. In various implementations, virtual indexing may be used to simulate building an index, chromosomes may be encoded using non-bitmapped representations of index configurations, chromosomes may include genes representing a column in a table in a database, dropping an index from a table in a database, or a composite index for a database, and / or a participation pool may be used to select fitter genes for an initial population of chromosomes.

The invention relates to an AGV scheduling method, in particular to an AGV scheduling method based on an ant colony and a genetic algorithm. A vehicle capacity factor and a time window factor are introduced into the ant colony algorithm to improve the ant state transition probability, and a pheromone volatilization factor is improved, so that the pheromone volatilization factor can be automatically adjusted along with a calculation process, meanwhile, a pheromone updating strategy is improved, and elite ants exceeding a globally optimal solution are rewarded. And finally, local optimization isperformed on an optimal solution obtained by the ant colony algorithm by using selection, crossover and mutation operators in the genetic algorithm. The algorithm convergence speed is increased, thesolution quality is improved, the defects that when a traditional optimization algorithm is used for path planning, the convergence speed is low, and local optimum is likely to happen can be obviouslyovercome, the solving efficiency of actual problems can be improved, and blindness of the iteration process is reduced.

The invention discloses a semiconductor workshop production scheduling method based on a genetic algorithm. The method comprises: (1) analyzing a semiconductor assembly line workshop scheduling problem; (2) determining the size of each individual matrix in combination with the encoding mode according to the processing time table of each workpiece process in a workshop; (3) initializing an intermediate variable under the condition that the optimal value is not improved; (4) carrying out crossing operation on any two individuals in the population; (5) combining new and old populations, and calculating the fitness value of each individual; (6) judging whether Q' and Q are the same or not; (7) executing selection operation on the merged population; (8) judging whether r or n meets a termination criterion or not; (9) judging whether the individuals meet mutation operation or not; (10) returning the mutated population to the step (4) for operation when the other n is equal to n+1; and (11) outputting the optimal individual of the population. According to the invention, the scheduling problem of complex flexible flow production workshops in the semiconductor industry is solved; and multiple unnecessary calculation processes due to the fact that the maximum number of iterations is set to be too large are avoided, the algorithm calculation time can be shortened, and the efficiency is improved.

A multi-target flexible job shop scheduling method based on a double-layer genetic algorithm is characterized in that a traditional genetic algorithm is improved, so that a population optimized by thegenetic algorithm within limited time has high quality; meanwhile, the solving mode of the traditional genetic algorithm for the multi-target problem is changed, a double-layer solving framework is provided, and compared with the traditional genetic algorithm, the solving quality is obviously improved. According to the method, the crossover strategy and the mutation strategy are optimized, and the selection operator is deleted. The improved genetic algorithm is combined with a rapid non-dominated sorting and congestion degree calculation module, a special process framework is designed for theimproved genetic algorithm, and the improved genetic algorithm is called as a double-layer genetic algorithm. According to the algorithm, an excellent non-dominated solution set can be obtained within limited time, and the algorithm has good practicability and can be well applied to actual workshop scheduling.

The invention discloses a centralized resource management method based on a genetic algorithm, and relates to the field of wireless communication. The method mainly includes the steps that S1, network resources and users in a system are integrated, two-dimension chromosome coding is carried out on resource allocation, and N individuals are generated randomly and serve as an initial population, wherein N is an integer larger than two; S2, dynamic power distribution is carried out on each chromosome, and based on the power distribution and user requirements, fitness functions of the individuals are built; S3, population propagation is carried out, wherein the population propagation includes the processes of selection, intersection, mutation and correction, and the number of filial generation individuals is kept to be identical to that of parent individuals; S4, the parent individuals are replaced by the filial generation individuals, and the population propagation processes are repeated until an iteration stopping condition is met. The use ration of the power of the system can be improved, under the condition that the real-time user requirements are met, fairness among non-real-time users can still be effectively ensured, and system performance is greatly improved.

The invention discloses a human body composition analysis method based on a genetic algorithm. The human body composition analysis method based on the genetic algorithm comprises the following steps: eight sections of human body impedance models are selectively used and an expression of each section of human body impedance is analyzed and calculated; groups of different voltage and current are set so that groups of human body impedance data models are obtained through calculation; an AIC value of each group of human body impedance data models is calculated through an akaike information criterion and combination with human body physiological parameters; a fitting model is selected; and genetic evolution is conducted on a position coefficient of the fitting model, an unknown parameter of the fitting model is determined through copy, intersection and mutation operation and a human body composition predicting formula is obtained. According to a calculation method of the eight sections of human body impedance models, theoretical reference can be provided to eight section impedance measurement technology. According to the human body composition predicting method based on the genetic algorithm, human body composition predicting accuracy can be improved and an effective detection measure is offered for human body composition research and clinical application.

The invention discloses a BP neural network wind speed prediction method based on genetic algorithm optimization. The method comprises the following steps: firstly, collecting wind speed data of a wind power plant, establishing a BP neural network prediction model, and estimating an initial value range; then, performing real number coding on the weight and the threshold of the neural network, randomly generating a group of initial individuals to form an initial population, and each initial individual represents an initial solution of a problem; calculating the fitness of each individual in thepopulation, performing selection, crossover and mutation operations to form a next generation of population, evaluating the fitness of the individuals in the new population, judging convergence conditions, selecting an optimal individual, and taking the optimal individual as an initial weight and a threshold of the neural network; and finally, training by utilizing matlab to obtain a wind speed prediction value. According to the method, the wind speed prediction efficiency and accuracy of the BP neural network are improved.

The invention discloses a multi-target flexible job shop scheduling method based on an improved niche genetic algorithm. Constructing a production scheduling sequence according to the process data ofall the workpieces in the multi-target flexible job shop, taking the production scheduling sequence as an individual, and generating a primary population; calculating a total objective function valueof the individual, and calculating a fitness value of the individual by using an improved niche method; selecting an individual set in a roulette mode according to the fitness value; implementing crossover operation and mutation operation of the genetic algorithm; forming a new population by the obtained individuals and the individuals with the highest fitness value in the generation population; repeating the steps until a termination condition is met, outputting an optimal individual in the last generation population, and arranging processing treatment by adopting a scheduling sequence of theoptimal individual, so as to realize multi-target flexible job shop scheduling. The improved ecological niche genetic algorithm is adopted to solve the scheduling problem in the production process, ahigh-quality scheduling result can be stably obtained, workshop resource allocation is optimized, and therefore the production efficiency of a workshop is improved.

The invention provides a construction project multi-objective optimization method. The construction project multi-objective optimization method comprises the following steps: determining a mathematical model and genetic algorithm parameters of multi-objective optimization; establishing a population with feasible constraints and a population target function matrix; calculating an objective weight of the target function by adopting an entropy weight method according to the target function matrix, and synthesizing a hybrid dynamic weight of the target function; sorting the population by adoptinga method based on dynamic weight to obtain a Pareto temporary solution set; attaching virtual fitness values to individuals according to population individual sorting, and selecting a filial generation population by adopting a proportional selection operator and a roulette method; performing crossover operation on the filial generation population; performing mutation operation on the filial generation population after the crossover operation; combining the Pareto temporary solution set with the filial generation population after mutation operation to generate a new population; and if the algorithm termination condition is met, terminating the algorithm, otherwise, returning. According to the method, the problem of ambiguity between an original multi-objective optimization algorithm and engineering application is well solved, and the method has better engineering applicability.

The invention discloses a parameter optimization method based on an improved genetic algorithm, computer equipment and a storage medium, and belongs to the field of parameter optimization. The optimization method comprises the following steps: 1) defining an initial chromosome population; 2) constructing a dynamic evaluation index function of the electric vehicle control system, and optimizing toobtain chromosome fitness; 3) selecting by using a brocade selection algorithm to serve as a parent population; performing operation by using an adaptive crossover and mutation algorithm to generate afilial generation population; adjusting the optimization region of the nth chromosome in the jth step by adopting a self-adaptive search strategy, checking whether j reaches the maximum allowable optimization step number or not after the search is completed, and if not, returning to the step 2); and 4) finding out the chromosome individual with the minimum fitness in the current population, wherein the value corresponding to each dimension of the chromosome is the parameter value of the electric vehicle control system, and the invention solves the problems of complex modeling and large calculation amount in the parameter optimization process of the electric vehicle control system.

The invention discloses an optimization method for vehicle path planning of an assembly type construction site. The method comprises the following steps: defining important elements in a fabricated construction site vehicle path optimization problem, setting parameters, and converting two-dimensional coordinates of a construction site into an Euclidean distance matrix; running an ant colony algorithm, performing probability operation of selecting a next access stacking point according to a roulette selection algorithm, and continuously performing pheromone updating; and, according to the genetic algorithm, encoding three important parameters alpha, beta and rho in the ant colony algorithm which are used as dyes, after the optimal combination of alpha, beta and rho is obtained through crossover and mutation operation, taking the optimal combination as an input parameter and then substituting the input parameter into the ant colony operation, and after finite iteration is performed, finally obtaining the optimal path of the vehicle. According to the method, the iterative performance and the optimization efficiency of the model are improved, a local optimal solution is prevented frombeing obtained, and meanwhile, the convergence speed of the model is increased, so that the method is greatly helpful to the scheduling optimization problem of prefabricated part transport vehicles ina prefabricated building site.

The invention relates to an algorithm for identifying a key protein based on a genetic algorithm in a PPI network. The algorithm comprises the steps of generating an initial population in a protein interaction network; calculating the fitness of individuals; performing selection operation by a roulette wheel method; performing crossing operation and mutation operation among the randomly selected individuals; and performing local optimization on multiple individual solutions. According to the algorithm, the defects of existing methods are overcome; indexes are optimized and bio-information is fused, so that the reliability is higher and lots of unnecessary calculations are reduced; and the predicted key protein can be locally optimized, so that the efficiency of key protein identification is improved, and the application range and practicality of the technology in the field of the bio-information are expanded and improved.

The invention provides a DNN task unloading method in an edge-cloud hybrid computing environment and a terminal. According to the types and the number of computing nodes, the number of DNN tasks to be unloaded and the number of layers of each DNN task to be unloaded, calculating the number of the DNN tasks to be unloaded; establishing a target function based on total cost minimization; determining a corresponding constraint condition. The influences of conditions such as computing power and time delay constraints of different types of nodes are considered, the feasibility of the obtained optimal solution is guaranteed, when the optimal solution is solved, crossover operation and mutation operation in the genetic algorithm are introduced into the particle swarm algorithm, a specific algorithm is given, and the problem that the particle swarm algorithm is prone to falling into local optimum in the optimal solution solving process is effectively solved.