174 results about "Mutation probability" patented technology

Provided in the invention is a task scheduling method based on heredity and ant colony in a cloud computing environment. The method comprises the following methods: S1, initializing population; S2, selecting individuals according to a wheel disc type selection strategy; S3, carrying out crossover operation on the individuals according to crossover probability and carrying out reversion mutation operation according to a mutation probability so as to generate a new colony; S4, updating the new generated colony; S5, determining whether a dynamic fusion condition is met; S6, initializing ant pheromone by using an optimal solution found by heredity; S7, calculating probabilities of moving to next nodes by all ants and moving all the ants to the next nodes according to the probabilities; S8, enabling M ants to travelling N resource nodes and carrying out pheromone updating on an optimal ant cycle; S9, carrying out pheromone updating on all paths; and S10, determining whether an ant end condition is met and outputting an optimal solution. According to the invention, respective advantages of a genetic algorithm and an ant colony algorithm are drawn and respective defects are overcome; and on the basis of dynamic fusion of the two algorithms, time and efficiency of exact solution solving are both considered.

The invention discloses a cloud computing task scheduling method based on an improved genetic algorithm. A multi-objective-based weighted optimization evaluation method is adopted as a fitness function; Coding is carried out in a real number direct coding mode; Generating an initial population by adopting a static algorithm and random generation combined method; Carrying out selection operation byadopting a hierarchical selection strategy; Performing crossover operation according to the crossover probability and the chromosome difference degree; Performing mutation operation according to thedynamic mutation probability; Judging whether the program is ended or not according to the double-termination condition; And finding an optimal scheme and distributing. Multiple steps of a traditionalgenetic algorithm are optimized and improved, and the user satisfaction degree and the algorithm execution efficiency are improved.

The invention provides a self-adaptive genetic particle swarm hybrid algorithm optimization method. The self-adaptive genetic particle swarm hybrid algorithm optimization method includes: calculatingthe density and the radius of a center region of a parent population in a genetic algorithm, and distinguishing whether the parent population is in the overall centralized distribution, the local centralized distribution or the uniform distribution; performing a selection operation of the genetic algorithm, and selecting a parent individual to be evolved; establishing computational formulas of thecrossover probability and the mutation probability according to the three distributions of the parent population; performing crossover and mutation operations according to the established crossover and mutation probability formulas so as to achieve chromosome recombination and gene mutation, and forming an offspring individual; selecting a part of individuals with high fitness from a part of offspring individuals to perform the particle swarm algorithm to form offspring particles, and combining the offspring individuals and the offspring particles into an offspring population and saving the optimal individual thereof. The invention adaptively adjusts crossover probability mutation probability parameter values in the genetic particle swarm hybrid algorithm, so that the convergence speed and the convergence precision are greatly improved.

Disclosed is an on-chip network mapping method based on the ant-colony chaos genetic algorithm. The standard ant-colony algorithm is basically used and the genetic algorithm is introduced in the on-chip network mapping method, parameters about each ant are coded by real numbers, codes of the ants are utilized as chromosome in the genetic algorithm, and algorithm parameters of coded ants are adjusted by the genetic algorithm in each iteration. During running of the algorithm, recycled results of each iteration of the algorithm are monitored, if the fact that the algorithm is trapped in a local optimum solution is monitored, mutation probability of the genetic algorithm is increased by a method of introducing a chaos model, and further, the parameters of the ant-colony algorithm are changed by means of the genetic algorithm. By the aid of the on-chip network mapping method, capability of the anti-colony chaos genetic algorithm for searching the solution space can be improved effectively, and trapping in the local optimum solution is avoided. In addition, the on-chip network mapping method has excellent practical values and wide application prospect for solution of massive on-chip network mapping.

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 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-objective optimized overall workshop layout method based on a multi-population genetic algorithm. The method comprises the following steps: firstly, a multi-row linearworkshop layout mathematical model is established, and a functional area layout problem is converted into a combined optimization mathematical model problem; secondly, based on the optimization objective of minimum total material handling cost and maximum area utilization ratio of workshop layout, a precise workshop layout model is established by taking account of constraints including horizontaland vertical placement of main streets and functional areas, adaptive row spacing and the like of the manufacturing workshop, and multiple optimization objectives are converted into a single evaluation function with a weighting method; finally, solving is performed with the multi-population genetic algorithm, immigration operators are linked with populations in the solving process, information exchange and co-evolution of multi-population are achieved, different crossover and mutation probability parameters are set for different populations by crossover and mutation probability control formulae, and different search purposes are guaranteed. The total logistics handling cost of the workshop can be effectively reduced, and the utilization rate of the workshop area is increased.

The invention discloses a method for optimizing signal timing of a single intersection based on a genetic algorithm. The method comprises the following steps of: S1, inputting a traffic data flow collected by a camera; S2, building an initial population and initiating a first generation of individuals; S3, calculating the adaptation value of the population; S4, judging whether the adaptation value reaches the iterative maximum value of the population, executing a step S8 if yes, and otherwise executing steps S5-S7; S5, adopting a roulette selecting method and applying an optimal retention strategy; S6, adopting arithmetic crossover to generate two subgeneration individuals; S7, adopting uniform mutation, substituting gene values on each loci in chromosomes with mutation probabilities, and returning to execute the step S3; and S8, stopping operation and outputting the current optimum individual. The method has the advantages that an optimization model is solved by the genetic algorithm, so that the delay time of vehicles is shortened, and the robustness of the signal timing optimization model is improved.

The invention discloses an embedded software test data generating method based on a fuzzy-genetic algorithm and relates to a test data generating method. The problem that a test dataset generated with an existing test data generating method is large in scale, so that generating time is long is solved. A genetic algorithm is improved, a fuzzy control method is used, through population entropy and the disperse degree, selecting of a genetic operator in a genetic process is controlled in a self-adaptation mode, when population diversity becomes poor, crossover probability and mutation probability are enlarged, so that population is evolved in a global-optimum direction, and the scale of test data is decreased. Then, an ant colony algorithm is used for sorting the generated combination test data according to the large disperse degree, so that the distance between adjacent test data values is enlarged, and test data sorting with the large disperse degree are selected from the optimum path sorting of all combination test data and is used as final embedded software test data for outputting. The embedded software test data generating method is suitable for embedded software test data generating.

The invention relates to a gene assembly method of phage genetically engineered antibody library, belonging to the field of biomedical study and clinical applications. The method aims to overcome the problems of the prior art, including poor stability, low efficiency, high mutation probability and low fidelity, and indirect application to humanized single chain antibody study. The technical proposal adopted by the invention comprises the following steps of: obtaining heavy chain variable region gene and light chain variable region gene of a humanized antibody by RT-PCR, and ligating the antibody heavy chain variable region, a linker and the antibody light chain variable region with the pre-designed restriction enzyme cutting sites on two ends of primers using PCR and restriction enzyme method at the same time.

The invention provides a robot grid sub-map fusion method based on immune adaptive genetic algorithm. A matrix corresponding to two grid sub-maps is regarded as an antigen. An antibody is plane transformation made by a second grid sub-map. An antibody colony generates a next antibody in operations of copying, crossing and mutation operator basing on affinity degree of the antigen and the antibody. A selection probability calculated according to similar vector distance guarantees multiformity of the antibody. On the base of an immune principle, a crossover probability and a mutation probability are adaptively adjusted according to sufficiency of the antibody to reduce a probability of local optimum. The invention has a high searching efficiency and can effectively search the best plane transformation randomly distributed in a searching space. The invention is especially fit for a multiple mobile robot grid sub-map fusion problem in complex environment. And the invention can realize information sharing among robots as soon as possible and effectively realize coordinating exploration among robots, and improve exploration efficiency.

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 present invention discloses an adaptive coefficient genetic algorithm-based cloud manufacturing service resource matching method. According to the method, the value of the objective function of each individual in a population is calculated, and the capacity limit of each individual is judged; individuals which do not meet capacity requirements are discarded; an adaptive coefficient is calculated; the selection probability, crossover probability and mutation probability of iteration of a current round are calculated; genetic evolution is carried out according to the probabilities, so that anew population can be generated; and the population is supplemented with new individuals. According to the method of the invention, an optimal resource service combination matched with the task requirement of a cloud manufacturing user is solved according to the task requirement of the cloud manufacturing user; it can be ensured that the sum of the products of the cost and time of all tasks is minimum; the capacity limitation of resource services is satisfied, so that queuing and waiting can be avoided; and since an improved genetic algorithm has high robustness and fast convergence rate andwill not be trapped in local optimum, the diversity of the population can be significantly improved, and the accuracy of resource matching can be improved.

The invention discloses a self-adaptive genetic algorithm based on the population evolution process, including the first step, setting the parameters of the BAGA algorithm, setting the number of iterations of the algorithm, the number of populations in each generation, the discrete precision of the independent variable, and the total number of shooting times , a constant; the second step is to use binary code to generate the initial population; the third step is to judge whether the maximum number of iterations is satisfied, and if so, output the optimal individual of the last generation, which is the optimal value found, otherwise turn to the fourth step; The fourth step is to establish the relationship between the objective function and the fitness function, and then calculate the fitness of each individual and the average fitness of contemporary individuals, save the individual with the largest contemporary fitness, and calculate the evolutionary degree of the contemporary population, the degree of population aggregation, and Balance factor, crossover probability and mutation probability; the fifth step, selection, crossover and mutation operations to generate new populations, the selection operator uses roulette technology, the crossover operation uses univariate crossover, and the mutation operation uses basic bit mutation; the sixth step, Find the best individual in the contemporary population, keep it, and then go to the second step.

The invention discloses an ambiguity of whole cycle rapid obtaining method based on a real-coding adaptive genetic algorithm. The method includes the following steps: S1, observing data through a carrier wave phase, and establishing a carrier wave phase double difference observation equation; S2, after linearization of the observation equation, utilizing Kalman filtering to obtain a floating pointsolution of ambiguity of a whole cycle and a corresponding covariance matrix; S3, taking a known baseline length as a constraint condition and determining the search range of the ambiguity of the whole cycle; S4, performing decorrelation on the floating point solution and the covariance matrix through an improved Cholesky decomposition method; and S5, determining a fitness function on the basis of a target function, determining all running parameters in an IAGA, conducting real coding on a problem, and finally searching an optimal solution of the ambiguity of the whole cycle through the IAGA.Corresponding crossover and mutation probabilities can be adjusted in a non-linear manner on the basis of individual fitness, a Hamming cliff problem can be solved by means of real coding, and the solving efficiency of the ambiguity of the whole cycle can be increased.

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 resource scheduling method in a cloud computing system. According to the method, the position of an updating frog is computed by the aid of a leapfrog updating formula when each sub-population is locally searched, the fitness of the updating frog is computed to judge whether the fitness of the updating frog is superior to that of the worst frog or not, the position of the updating frog replaces that of the worst frog if the fitness of the updating frog is superior to that of the worst frog, the position of the optimal frog in the whole population replaces that of the worst frog if not, whether the fitness of the updated frog is superior to that of the worst frog or not is judged, the position of the updated frog replaces that of the worst frog if the fitness of the updated frog is superior to that of the worst frog, a new step length is generated by a double learning factor formula if not, the new step length is mutated according to mutation probability to obtain the position of the updated frog and replace the position of the worst frog, and the fitness of the updated frog is computed. The method can achieve good performances in optimal time span and load balance for task scheduling. The invention further discloses a resource scheduling system in the cloud computing system.

The invention discloses a power line path optimization method adopting a genetic algorithm, which performs optimization on a cable loop network path by adopting the genetic algorithm. The power line path optimization method comprises the steps of encoding n cable single-loop network cabinets and/or cable double-loop network cabinets, randomly generating a random permutation of n integers with the interval being [1, n], and forming a chromosome; selecting a fitness function used for judging the fitness of the chromosome for a target, wherein the fitness function is shown in the description; determining the population number N, the maximum algebra Gmax, the crossover probability pc and the mutation probability pm; and selecting an individual with a high fitness value by adopting a roulette mode to act as a male parent, wherein the roulette is selected according to the fitness of individuals, individuals with a high fitness value are selected, individuals with a small fitness value are removed, evaluation is performed on each chromosome in the new population, optimal individuals are stored, and an optimal solution is outputted. Compared with manual wiring, the power line path optimization method not only reduces the labor cost, but also improves the design efficiency. In addition, the power line path optimization algorithm is flexible and reliable, and saves the enterprise cost.

The invention relates to an adaptive genetic algorithm-based mixed flow shop sustainable scheduling control method. The method comprises the following steps of: 1) obtaining shop processing information which comprises a workpiece number, processing procedures, a machine number on each procedure, processing time of the workpieces on the machines, unit residence energy consumption of the workpieces,unit waiting energy consumption of the machines and last procedure batch processing information; 2) establishing a mixed flow shop scheduling model, taking minimized energy consumption as a target, for last procedure batch processing; and 3) solving the scheduling model by adoption of an adaptive genetic algorithm so as to obtain an optimized scheduling scheme. In the adaptive genetic algorithm,encoding and genetic recombination are carried out in a layering manner on the basis of mixed flow shop processing characteristics, and populations are updated by adoption of adaptive crossing and mutation probabilities. Compared with the prior art, the method has the advantages of being obvious in energy saving effect and high in solving efficiency.

Provided is a real-time dynamic channel assignment method based on a genetic algorithm in a radio communication system, and a computer-readable recording medium for recording a program implementing the method. The channel assignment method in accordance with the present invention has following advantages. First, an evaluation function clearly shows the difference between chromosomes, which represents channel assignment, can be set. Second, the efficiency in calculation time and memory capacity is increased by representing the assignment of channels arranged in one-dimensional using inherent channel numbers. Third, by controlling the Elitist pool crossover method and mutation probability properly, diversity is pursued in the initial process of the evolution program, and then as generation repeats, the convergence is enhanced so as to increase the efficiency in obtaining the optimum solution.

The invention provides a grayscale threshold acquisition method based on an adaptive genetic algorithm and an image segmentation method, and belongs to the technical field of image processing. The grayscale threshold acquisition method comprises the following steps that S01, population initialization is performed on an image grayscale value; S02, the fitness value of individuals in the population is calculated; S03, selection operation is performed and the population is updated; S04, the crossover probability of the individuals is calculated, crossover operation is performed according to the crossover probability and the population is updated; S05, the mutation probability of the individuals is calculated, mutation operation is performed according to the mutation probability and the population is updated; and S06, whether the termination condition is met is judged, and the optimal solution is obtained and the optimal grayscale threshold is obtained; or the step S02 is performed. According to the image segmentation method, image segmentation is performed according to the grayscale threshold obtained by the grayscale threshold acquisition method. The grayscale threshold acquisition method has autonomous learning and adaptability and high robustness and can solve the grayscale threshold from global concurrency so that the local optimum can be greatly avoided and the method is accurate and efficient.

The invention provides a complex network cell discovering method under an adaptive evolution bat algorithm for self-media network data. The method includes the following steps that S1, mass data is acquired, a network structure model is established, according to the bat algorithm, a modularity function serves as a fitness function, a coding mode based on characters is adopted, and an initialized population is improved through a label propagation method; S2, the individual speed of the bat algorithm is converted into a mutation probability value, position update is calculated through a cross operator and a mutation operator, therefore, adaptive evolution of the common bat algorithm is achieved, the adaptive evolution bat algorithm is used for dividing a network, and a more accurate network cell division result is obtained. Compared with other intelligent algorithms for cell discovery, the algorithm has the advantages of being high in convergence rate and solution precision, and is more suitable for cell discovery under the large-scale network.

The invention relates to a method for optimizing a filling material ratio, and belongs to the technical field of filling mining. The method comprises the following steps: optimizing and searching the filling material ratio based on an NSGA-II (Non-dominated Sorting Genetic Algorithm-II) algorithm thought; limiting a value range of each parameter of the filling material ratio; setting genetic parameters including a population size, a computing algebra, a crossover probability, a mutation probability and the like; setting a pre-set filling body strength value and optimally searching to obtain one non-dominated solution set containing a plurality of groups of solutions; finally, combining a mine conveying condition and selecting one group of solution, which meets filling body strength requirements of a mining method, meets mine conveying requirements and has the lowest cost, as filling material ratio parameters. By adopting the method provided by the invention, the number of times of experiments can be realized, and ratio parameters, which meet different filling body performance requirements, are rapidly recommended; meanwhile, the filling cost can be reduced. and the method is easy to popularize and apply.

The invention discloses an adaptive simulated annealing genetic algorithm used for sleep electroencephalogram staging feature selection. Sleep staging is performed through electroencephalogram signals, a large number of feature parameters require to be extracted out of the electroencephalogram signals, and the relatively optimal feature parameter combination is selected out through screening to be used for establishing a sleep electroencephalogram mathematical model. In the present simulated annealing genetic algorithm, the high overall search capacity of the genetic algorithm and the high local search capacity of the simulated annealing algorithm are reserved so as to enhance the probability of generating excellent individuals. In the simulated annealing operation of the present algorithm performed on the individuals in the iterative process, the mechanism for randomly generating new solutions in the neighborhood of the current optimal solution has the fatal flaw. The algorithm aims at the flaw and solves the disadvantages that the neighborhood new solution generation mechanism of the conventional simulated annealing genetic algorithm has low iterative efficiency and is greatly affected by the neighborhood range and can realize adaptive adjustment of crossover probability and mutation probability, and the fitness function can be designed by using the weighing method.

The invention discloses an adaptive mutation particle swarm optimization algorithm. According to the adaptive mutation particle swarm optimization algorithm, a mutation operation is introduced into PSO (Particle Swarm Optimization), namely a mutation probability factor is introduced into a whole swarm position; the mutation operation usually serves as a trigger applied to generation stagnancy (premature convergence) under control of each generation or a prefix interval or an adaptive strategy. The algorithm is capable of jumping out the current searched local optimal position and searching again in a larger solution space, so that the solution space searching range is expanded while the swarm diversity is retained; the algorithm is capable of effectively carrying out global search, so that the capability of searching global optimal solution of the swarm can be improved. The adaptive mutation particle swarm optimization algorithm is capable of finding the global optimal solution to a maximum extent under the precondition that the global search is not carried out; and the classifier performance can be improved.