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

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 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.

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 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 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 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.

The invention discloses a hybrid genetic algorithm-based calculation unloading method in mobile edge calculation, which comprises the following steps: S1, establishing a system model to obtain calculation time delay of a sub-task set in each processor and transmission time delay among the processors, and determining each task layer value in the sub-task set according to a constraint relationship of the sub-task set; S2, initializing a population according to the determined task layer value and a random strategy to obtain initial population individuals of the sub-task set, performing symbol coding to obtain a task scheduling sequence, and optimizing the individuals in the initial population; S3, constructing a fitness evaluation function, and performing selection operation on individuals inthe optimized initial population; S4, constructing a crossover mechanism, and crossover individuals in the new population by using crossover operation based on a tabu table search algorithm; S5, performing mutation operation on individuals in the new population by using mutation operation based on a simulated annealing algorithm; and S6, judging whether the iteration step length is reached or not, and if not, repeating the step S3-S5; and if so, outputting a globally optimal solution.

The invention discloses a rocket trajectory optimization method based on an improved genetic algorithm. The rocket trajectory optimization method comprises the following steps: step 1) converting an optimal control problem of rocket trajectory optimization into a parameter optimization problem of a maximum value of a rocket subsonic segment attack angle absolute value and a rocket pitch angle change rate through a variational method; 2) performing binary coding on the maximum value of the absolute value of the attack angle of the rocket subsonic segment and the change rate of the rocket pitchangle to generate a primary population; 3) calculating individual fitness in the population by taking the maximum range as an optimization target; 4) for equality and inequality constraints in the rocket flight process, adjusting the fitness of population individuals by using a penalty function; 5) reserving the optimal individual in the population by utilizing the population fitness obtained by calculation; 6) selecting two male parents from the population, self-identifying cross male parents by utilizing a similarity threshold, reselecting the male parents higher than the set male parent similarity threshold, and carrying out cross operation on the male parents lower than the set similarity threshold; 7) according to the diversity of the population and the algebra of population evolutionstagnation, adjusting the mutation probability, then selecting individuals to be mutated, and carrying out mutation operation; The rocket flight path optimization method has the advantages that the global search capability is higher, the excessive similarity between population individuals and optimal individuals is avoided, the optimization performance of the genetic algorithm is improved, and the performance of a rocket flight path optimization scheme is ensured.

An adaptive mutation operator for a genetic algorithm that programmatically mutates individuals in a constrained optimization for a modeled system is discussed. The mutation operator takes into account linear and bound constraints in generating new mutated individuals. The mutation operator generates random mutation direction vectors and random initial step sizes. A mutated individual is generated and moved along a randomly chosen mutation direction vector a distance equal to the initial step size. The generated mutated individual is compared to the linear and bound constraints. In the event the generated mutated individual is located in an infeasible region, the illustrative embodiment of the present invention automatically adjusts the step size to a smaller value and generates another mutated individual along the chosen mutation direction vector. The process iterates until the generated individual is within the feasible region. The number of available valid mutation directions increases as the step size decreases.