215 results about "New population" patented technology

The invention provides a cloud task scheduling method based on an improved genetic algorithm which is the dual fitness multiplied genetic algorithm, and relates to the fields of cloud computing and scheduling algorithms. As for a cloud computing Map/Reduce programming model, the problem of matching between user tasks and virtual resources is researched to find appropriate resources for execution of the user tasks, the total task execution time and the mean task execution time are shortest at the same time through the algorithm, and according to the algorithm, one fitness and one multiplied algorithm comprehensively adopting task scheduling are added. An initial population is generated, the individual fitness value is calculated, the operations of selection, intersection and mutation are carried out, the number of iteration is added by one, new populations are generated constantly, the individual fitness values of the finally-obtained population are calculated respectively, the individual with the largest fitness value is the optimal solution, and a resource node sequence obtained by decoding the individual is the final result of the cloud task scheduling method based on the improved genetic algorithm.

A method of determining a set of parameters for modeling an active semiconductor device in which current flow through a channel or other area is regulated by voltage applied to the device terminals, for example, MOSFETs. The method comprises first providing a plurality of measured values for current as a function of voltage for a plurality of active semiconductor devices of differing geometries. There is then determined an initial population of vectors comprising individual values representing a plurality of desired active semiconductor device model parameters. Fitness is then evaluated for each of the vectors by comparing calculated values for current as a function of voltage from the population to the plurality of measured values for current as a function of voltage of the vectors, converting any current differences to voltage errors and adding any such voltage errors together to arrive at a fitness value for each vector. Vectors of best fitness are selected and at least one genetic operator is applied thereto to create a new population of the vectors. Vectors of best fitness are then selected. The steps of evaluating fitness and selecting vectors of best fitness are optionally repeated for such vectors of best fitness until a desired fitness is achieved to determine the desired active semiconductor device model parameters.

The invention relates to a hybrid genetic simulated annealing algorithm for solving a job shop scheduling problem. The job shop scheduling problem is solved through the algorithm. The hybrid genetic simulated annealing algorithm aims to solve the problems that the genetic algorithm is poor in local searching capability but high in capability of overall search process grasping, the simulated annealing algorithm has high local searching capability but knows less of conditions of the entire searching space and is inconvenient for enabling the searching process to enter the most promising searching region and the like. The genetic algorithm and the simulated annealing algorithm are combined for adopting the long points while overcoming the weak points, and the hybrid GASA is proposed. According to the algorithm, genetic operations such as selection, crossing, variation and the like are performed on populations to generate new populations, then each individual in the new populations is subjected to the simulated annealing, results are taken as input of genetic operation in the next steep, and the whole operation process is subjected to repeated iteration till certain end condition is satisfied.

The invention discloses an arc fault diagnosis method based on time domain feature parameter fusion. The arc fault diagnosis method comprises the steps of: step 1, designing a BP neural network used for arc fault diagnosis, wherein the design is implemented by (1) determining input and output modes of the neural network, (2) acquiring training set and test set samples of the neural network, (3) determining a number of layers of the BP neural network, (4) determining a number of nerve cells of a network hidden layer, and (5) selecting training parameters of the BP neural network; step 2, optimizing the BP neural network by adopting a genetic algorithm in a process shown in the chart 7, wherein the genetic algorithm optimization process is mainly implemented by (1) coding individuals and initializing a population, (2) calculating fitness, (3) and generating a new population. The arc fault diagnosis method has the beneficial effects that: the genetic algorithm optimized BP neural networkhas better performance compared with a BP neural network trained by using random weights and threshold values, and has relatively higher arc fault identification accuracy rate.

The invention discloses an image segmentation method based on an immune clonal selection cluster, and relates to the technical field of an image processing. The purpose of the invention is to solve the disadvantages that the robustness is lower due to sensitivity of a FCM cluster segmentation method to an initial clustering center and the noise; and spatial relationship between pixels of the image is not considered by the FCM cluster segmentation method. An implementation procedure of the method is as follows: an initial population is created at random according to a setup parameter; adaptation degree of each individual in the present population is calculated to judge whether a halt condition is met; a transitional population is created by a recurrence formula of the FCM; the adaptation degree of each individual in the transitional population is calculated; based on the adaptation degree, a cloning operation is made to the transitional population; a mutating operation is made to the individual in the cloned population; after the mutating operation, a roulette wheel selection is carried on to get a new population to carry out the second step; finally, an optimum individual is selected; and the image of a segmentation result corresponding to the optimum individual is output. The image segmentation method based on the immune clonal selection cluster can be used for the cluster segmentation of a pixel level of the image.

A transmitter for a digital transmission signal includes a pre-distorter to improve linearity of a power amplifier. An amplified transmission signal is conditioned into a narrowband feedback signal that is responsive to a logarithm of the power appearing in out-of-band components of the amplified transmission signal. The feedback signal is processed in a pre-distortion processor that implements a genetic algorithm to adapt pre-distortion functions implemented in the pre-distorter and improve linearity over time. The genetic algorithm tests a population of randomly-generated pre-distortion functions for fitness. A baseline component of the coefficients from pre-distortion functions used in a subsequent population tracks the best-fit pre-distortion function from the current population, allowing the use of a limited search space. New populations are generated from old populations using an elitism process, and randomized crossover, and mutation processes.

A method and system for returning an optimum (or near-optimum) solution to a nonlinear programming problem. By specifying a precision coefficient, the user can influence the flexibility of the returned solution. A population of possible solutions is initialized based on input parameters defining the problem. The input parameters may include a minimum progress and a maximum number of iterations having less the minimum progress. The solutions are mapped into a search space that converts a constrained problem into an unconstrained problem. Through multiple iterations, a subset of solutions is selected from the population of solutions, and variation operators are applied to the subset of solutions so that a new population of solutions is initialized and then mapped. If a predetermined number of iterations has been reached, that is if the precision coefficient has been satisfied, the substantially optimum solution is selected from the new population of solutions. The system and method can be used to solve various types of real-world problems in the fields of engineering and operations research.

The invention provides a multi-objective quantum-behaved particle swarm algorithm-based unmanned aerial vehicle cooperative task distribution method. According to the method, an unmanned aerial vehicle cooperative task distribution model is established; and three indexes such as an objective earlier-stage task execution success probability, an unmanned aerial vehicle survival probability and a task completion time in an unmanned aerial vehicle cooperation task distribution problem respectively serve as optimized objective functions of a multi-objective task distribution problem, so that the simultaneous optimization of the three indexes is realized. Aiming at the characteristics of the unmanned aerial vehicle cooperation task distribution problem, a repairing operator aiming at the earlier task constraint and a repairing operator aiming at the in-task constraint are designed, so that the quality of solution in populations is improved; and a new population variation mechanism, so that the convergence speed of the algorithm is effectively improved.

The invention discloses an improved particle swarm optimization (PSO) algorithm of solving the zero-waiting flow shop scheduling problem. Firstly, parameter initialization and population initialization are carried out, wherein initial workpiece sequences are generated, then a factorial encoding method is used to map all permutations to integers to form an initial population, and finally, a feasible initial velocity set is randomly generated; particles are moved; the population is updated through an original PSO population updating strategy, a new population is mapped to corresponding workpiecesequences, and work completion time of each new workpiece sequence is evaluated; an improved variable neighborhood search (VNS) algorithm is used for a local search, and results obtained by the search are used for replacement; a population adaption (PA) operator is used to increase diversity of the population; and checking of a termination condition is carried out, if the termination condition ismet, a process is stopped, and values of variables and corresponding sequences are returned to be used as a final solution, and otherwise, particle velocity is continuously updated. The method has the advantages of improving a particle swarm optimization algorithm, improving global search capability, and avoiding too early convergence.

The invention relates to a photovoltaic failure detection method based on an improved particle swarm optimization Elman network, which is characterized by comprising the following steps: (1) initializing particle swarm algorithm; (2) constructing an Elman neural network topology structure; (3) determining the particle evaluation function and calculating the particle fitness value; (4) updating theparticles and introducing the mutation operator to obtain new population particles: re-determining the individual extreme value and the global extreme value, and obtaining the optimal particle when reaching the set precision or the maximum number of iterations; (5) obtaining the optimal weight values according to the optimal particles obtained in the step (4) to carry out network training and result prediction. The method obtains the optimal weight value of the neural network through the improved particle swarm algorithm, overcoming the defect of the Elman neural network trapped in local optimal solution, greatly improving the prediction efficiency and speed, and facilitating the maintenance and management of the photovoltaic power generation system.

The invention relates to a multilingual method for summarizing an article, which comprises an offline stage in which a weights vector is determined using, among others, plurality of predefined metrics, a collection of documents and expert prepared summaries, subjection of all the document sentences to all said metrics, guess of a population of weights matrices, subjection of the population to said metrics, ranking of sentences, generation of a new population using a genetic algorithm, and repetition of the same until convergence. The invention further comprises a real time stage in which the weights vector, as determined, as well as said metrics are used to determine an extract of any new document.

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 relates to an indoor visible light communication LED array layout optimization method based on a genetic algorithm. The method comprises following steps: 1, constructing chromosomes according to the number of LED arrays and the length and the width of a room; 2, creating an initial population by using the chromosomes obtained in the previous step; 3, calculating the fitness value of each individual in the population, judging whether an algorithm ending condition is satisfied; 4, carrying out selection, intersection and mutation operations to each individual in the population, thus generating a new population; and 5, analyzing the optimized LED array layout according to the optimum individuals obtained by the genetic algorithm. According to the algorithm of the invention, the problem that the receiving powers received on the receiving plane in the existing LED layout are distributed unevenly can be solved; the optimized LED array layout enables the receiving powers on the receiving plane to be distributed evenly; and the reliability problem of the LED communication is solved.

The invention relates to an intelligent test paper method based on a genetic particle swarm optimization algorithm, comprising: generating the objective function corresponding to each constraint condition according to the constraint condition corresponding to test paper attribute information, and calculating the fitness function of test paper according to the objective function corresponding to each constraint condition; obtaining test questions from an item bank to form a plurality of pieces of test paper, and performing chromosome coding on each piece of test paper, wherein each piece of test paper corresponds to a chromosome, the chromosome includes a plurality of segments, each segment of chromosome corresponds to a type of test questions, and includes a plurality of genes, and each gene corresponds to a test question; obtaining an initial population through a particle swarm algorithm; and processing the initial population through a genetic algorithm to obtain a new population to output test paper therein. According to the technical scheme, the method employs test paper attribute information as constraint conditions to generate a fitness function, and performs particle swarm algorithm and genetic algorithm treatment on test paper according to the fitness function, thereby obtaining test paper meeting user needs.

The invention discloses a star choosing method for a satellite navigation system. The aims of reducing star choosing operational amount and navigational positioning calculated amount are fulfilled by choosing stars according to the set number of satellites on the premise of meeting the navigational positioning requirement. The method comprises the following steps of: setting parameters; acquiringthe positions of visible stars; performing star choosing operation to form a new population by coding, initializing populations, acquiring fitness function values, selecting, crossing and variation so as to select an individual with maximal fitness function value as an initial star choosing result; and when the termination condition is met, acquiring a final star choosing result according to the individual. The method effectively solves the problem of large calculated amount of the conventional star choosing method, and has the advantages of flexible operation, simple and quick star choosing,and suitability for real-time star choosing under the static and dynamic conditions.

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 relates to an improved genetic algorithm for solving a flexible job shop scheduling method with moving time, which effectively solves the problem that the existing algorithm ignores moving time and may affect the processing quality of a product in some specific production fields. The technical scheme for solving the problem comprises the following steps: 1. setting parameters; 2, initializing a population, and randomly generating a population individual by using a random selection method; 3, calculating, evaluating the fitness value of each chromosome in the population and carrying out size comparison, if the condition is satisfied or the approximate optimal solution is finished, otherwise, executing the step 4; 4, selecting the next generation population by using a tournament selection method; 5, that chromosome individual satisfying the crossover probability in the population; 6, compiling that chromosome individuals satisfying the mutation probability to obtain a new population; 7, returning to step 3. The invention realizes fast optimization of the scheduling scheme.

The invention discloses a graph-model-based uncertain steelmaking-continuous casting flexibility optimization scheduling method and system. The method comprises: a flexibility scheduling model is constructed, a flexibility decision-making mode of a scheduling solution is described by using a graph model according to features of a steelmaking-continuous casting production process, and time buffer is set to protect the performance index and feasibility of the scheduling solution under an uncertain condition; optimized solution solving based on a distribution estimation algorithm is carried out,and an association relationship between decision-making variables is described by using a procedure-association-based probability model to generate a high-quality new population, and an expectation performance of the flexibility scheduling solution is evaluated by using a stochastic simulation method. During the simulation process, the limited computing resources are allocated dynamically based onthe optimal computational allocation technique, so that the evaluation efficiency of the population is improved. Therefore, optimization of the waiting time of the steelmaking-continuous casting scheduling process and the expected value of the discontinuous casting ratio in the uncertain environment is realized.

The invention discloses a main line green wave coordination control signal time method for optimizing exhaust gas emission. The method comprises the following steps that first, the basic traffic parameter of a main line is surveyed and obtained, and a vehicle exhaust gas emission calculating platform is initialized; second, the basic parameter of a multi-objective genetic method is set, and a population of the multi-objective genetic method is initialized; third, based on the platform, the adaptive degrees of all individuals in the population are calculated; fourth, the non-domination sequence and the visual adaptive degree of the individuals in the population are calculated, a progeny population is generated through genetic section, genetic cross and genetic variation, and the adaptive degrees of all individuals of the progeny population are calculated; fifth, the population and the progeny population are combined to obtain a new population, the non-domination sequence and the congestion degree of all individuals of the new population are calculated, the individuals are chosen based on the non-domination sequence and the congestion degree, and the next generation population is obtained; six, when evolution algebra is larger than the best evolution algebra, execution of the method is completed, all the individuals with the non-domination sequence being equal to 1 in the last generation population are used as a final noninferior solution to be output, and the timing scheme in which the vehicle average delay and vehicle exhaust gas emission are comprehensively considered is obtained.

The embodiment of the present invention discloses a method and system for implementing network dynamic load balancing. The method includes: dividing a large-scale distributed system network into several local areas, and initializing the network topology population in the local area; Construct a multi-objective function according to multiple constraints in the local area, calculate the individual fitness of the individual in the population according to the multi-objective function, and select an individual with a higher fitness as the parent; perform a genetic operator on the parent , form offspring; Substitute the offspring into the multi-objective function to calculate its regret value, and judge whether the regret value of the offspring is less than the regret value of the parent generation; when the judgment result is yes, then use the The offspring replace the parent to form a new population; when the judgment result is no, the offspring are discarded. The system of the present invention can be used to implement the method. The technical scheme of the invention adopts a distributed strategy, the algorithm is simple, and has good adaptability.

The invention provides a biological evolution principle-based improved particle swarm algorithm. The algorithm comprises the following steps of: 1, initializing parameters of an improved particle swarm algorithm; 2, initializing a population position and a speed, calculating fitness value of each particle, and initializing global optimum; 3, judging whether to enter a crossover and variation operation or not, if the judging result is positive, entering step 4, and otherwise, skipping to step5; 4, carrying out grouping on the particles according to sorting of the fitness values, taking the first half of particles as an optimal solution group, taking the rest particles as a bad solution group, carrying out a crossbreeding operation on the optimal solution group, carrying out a variation operation on the bad solution group, combining the two groups of particles into a new population, and sorting the new population with an original population according to the fitness values so as to the first half of new excellent particles; 5, updating the particle positions and speeds, and updating individual optimal and global optimal; and 6, judging whether the improved particle swarm algorithm isconverged or achieves a maximum iteration frequency or not, if the judging result is positive, outputting a position of a global optimal solution as a solution of an optimization problem, and otherwise, returning to execute the step 3.

The invention discloses a ship-to-shore attack firepower distribution method based on a genetic algorithm. The method includes the steps: taking one weapon on a ship as a firepower unit, taking firepower distribution number of a shore target opposite to the firepower unit as an encoding bit, and performing encoding on each firepower unit by the aid of a random distribution generation method to obtain an initial population; calculating population fitness of the initial population by the aid of a fitness function, selecting the initial population according to the population fitness and a roulette to obtain a population after selection, and performing chromosomal chiasma and variation on the population after selection to obtain a new population; updating the initial population by the aid of the new population, repeating a last step for many times to obtain a ship-to-shore attack firepower distribution scheme. The method is stable in solving result, short in time consumption and rapid in convergence, and the initial population is easily generated.