260 results about "Chromosome (genetic algorithm)" patented technology

An evolutionary control for a subject such as an engine installed in a vehicle is conducted by the steps of: selecting coefficients, as genes, affecting the control characteristics of the control system; creating plural control units as chromosomes, each being constituted by plural genes; expressing the genes of each chromosome as control characteristics by controlling the subject using the control system having the control characteristics; selecting at least one chromosome from the created chromosomes based on the control characteristics expressed by the genes in view of the user's preference; and causing the at least one chromosome to evolve using a genetic algorithm, thereby obtaining control characteristics suitable to the user. In this method, the characteristics of the product can effectively be adjusted after its purchase based on the user's preference.

The invention discloses a reconfigurable assembly line sequencing method based on an improved genetic algorithm. The method comprises the following steps of: determining a population size according to a minimum production cycle of a reconfigurable assembly production line, and executing genetic encoding according to a standard of taking a chromosome as a full array of all tasks; calculating the idleness of the minimum reconfigurable assembly line, the quantity of unfinished work, the uniform parts use rate and the minimum production adjustment cost of the individual; executing a grading operation, executing a Pareto solution set optimization filtering operation, calculating the fitness of each grade, executing genetic operations according to the fitness, executing an elite reservation strategy, and obtaining a Pareto optimal solution set and a corresponding objective function value by judging whether convergence is realized or the pre-set maximum number of iteration is achieved. In the method, three major factors influencing the optimized sequencing of the reconfigurable assembly line are comprehensively considered, a plurality of technologies are used in the genetic operation, population diversity is ensured, algorithm prematurity is avoided, and global optimal search ability of the algorithm is enhanced.

The invention is applicable to the technical field of workshop production planning management, and provides a hybrid genetic algorithm-based MES (Manufacturing Execution System) production planning and scheduling method. According to an order priority generation mode and/or random generation mode, a workshop task sequence which meets constraint relations between tasks and a genetic algorithm coding rule is generated for a preset number of initial scheduling schemes; according to the best task starting and ending time and according to one or more distribution combination modes in resource load balancing principle distribution and random distribution, execution resources are set for each workshop task in the preset number of initial scheduling schemes; the preset number of initial scheduling schemes are converted into a series of chromosomes through a coding process to serve as an initial population for the hybrid genetic algorithm; and the initial population is introduced to the hybrid genetic algorithm, and a scheduling result after optimization is calculated according to a preset optimization target. High efficiency of the MES production planning and scheduling results in the prior art is improved.

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 present invention provides a multi-intelligent robot task distribution method facing a dynamic task which mainly solves the multi-task distribution problem of a task state quantity with time-variant characteristics. The method comprises the steps of: obtaining dynamic task feature parameters, combining intelligent robot ability parameters, and establishing a feature equation of a task point state quantity; according to the feature equation, designing an intelligent robot revenue function; according to the revenue function, designing a genetic algorithm fitness function; further designing agenetic algorithm difference selection operator and a local mutation operator, and providing an algorithm repair strategy; and finally, employing the genetic algorithm to generate an intelligent robot task distribution scheme to complete multi-task distribution. The multi-intelligent robot task distribution method takes obtaining of system maximum return as a target to achieve dynamic multi-taskrapid distribution, solve the algorithm chromosome deadlock problem and avoid that search falls into local optimum, and through a multi-stage distribution strategy, the method can fully deploy intelligent robots in the system to participate in task completion so as to improve the whole efficiency of the system.

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.

The invention relates to the rehabilitation training field and aims to optimize the quantifying factor and scale factor of a fuzzy controller and the fuzzy control rules, then control the current mode of an FES system accurately, stably and instantly and effectively improve the accuracy and stability of the FES system. The technical scheme adopted by the invention is as follows: the walking aid electrostimulation fine control method based on genetic-ant colony fusion fuzzy controller comprises the following steps: firstly, converting the selection of fuzzy control decision variable to the combinational optimization problem adapting to the genetic-ant colony algorithm, coding the decision variable, randomly generating a chromosome composed of n-numbered individuals; secondly, using the genetic algorithm to generate the initial pheromone distribution of the ant algorithm, utilizing the ant colony algorithm to randomly search and optimize the membership function, quantifying factor and scale factor of the fuzzy controller; and performing repeated self-learning and self-regulating according to the system output, and finally using the processes in the FES system. The invention is mainly used for rehabilitation training.

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 discloses an estimation method of Gaussian overlapped nuclear pulses, and the method is used to estimate Gaussian overlapped nuclear pulses after Sallen-Key digital shaping. Original Gaussian overlapped nuclear pulses to be decomposed are obtained by superposing N index attenuated nuclear pulses or double-index nuclear pulses and carrying out S-K digital shaping; all parameters of the N nuclear pulses and parameters of an S-K digital shaping algorithm are seen as genes of a chromosome; and an error between the Gaussian overlapped nuclear pulses represented by individuals and the original overlapped nuclear pulses serves as a target function, an optimal individual is obtained by using a genetic algorithm, and N corresponding Gaussian nuclear pulse components are reconstructed to estimate the Gaussian overlapped nuclear pulses. According to the method, an optimal combination of Gaussian nuclear pulses is searched in the global aspect, the Gaussian overlapped nuclear pulses are decomposed, the abandoning rate of the Gaussian overlapped nuclear pulses is greatly reduced, and the accuracy and credibility of radioactivity measurement are improved.

The invention belongs to the technical field of production scheduling of flexible manufacturing systems, and particularly relates to a deadlock-free scheduling method for a flexible manufacturing system based on an improved genetic algorithm. The method comprises the following specific steps of: establishing a Petri net model of the flexible manufacturing system, determining genetic parameters, encoding and decoding, generating an initialized population, performing detecting and repairing, calculating processing time and fitness, judging whether a termination rule and genetic operation are metor not, and outputting an optimal individual; adjusting all chromosomes into control feasible chromosomes through a two-step forward looking method, and decoding the control feasible chromosomes intoa deadlock-free scheduling sequence; optimizing and improviing the genetic algorithm in the design process of the scheduling strategy. Meanwhile, in the variation process, the chromosome gene is divided into a path gene and a process gene, variation operation is conducted on the path gene and the process gene at the same time, the variation rates of the path gene and the process gene are the same, and therefore the operation steps are simple, the production efficiency is greatly improved, and the application environment is friendly.

The invention provides a hybrid genetic algorithm for solving a multi-objective flexible job-shop scheduling problem. The algorithm takes three objectives of the completion time, the production cost and the equipment utilization rate in flexible job-shop scheduling into consideration, and aims at the circumstances that algorithms at present are difficult in decoding and long in computation time because of possession of a complicated coding method and that the genetic algorithm is faced with problems of global near optimum and insufficient search capability in local search. In allusion to the problems, the invention provides a new matrix chromosome coding method which almost does not need to perform decoding. In addition, the algorithm provided by the invention combines the global search capability and local search of the genetic algorithm, the search capability of the algorithm is enhanced, and a feasible solution is found more easily. The hybrid algorithm is high in practicability, and can be well applied to flexible job-shop scheduling.

The invention discloses a path planning method for multiple unmanned aerial vehicles to arrive at a designated place simultaneously in a three-dimensional environment, and the method comprises the steps of building a population, wherein a chromosomes represents a comprehensive flight path, and the comprehensive flight path is formed by the series connection of the flight paths of all unmanned aerial vehicles; setting the fitness of the genetic algorithm, and comprehensively evaluating the set violation degree W of the maneuverability constraint of the unmanned aerial vehicle by the total tracklength L of all unmanned aerial vehicles, the difference degree T of arrival time of different unmanned aerial vehicles and the track; and optimizing the chromosome by adopting the genetic algorithm,and obtaining an optimal chromosome after the iteration of the genetic algorithm, and decomposing the optimal chromosome to obtain the track of each unmanned aerial vehicle. By using the method, thetechnical purposes of gathering multiple unmanned aerial vehicles and reaching the task points at the same time can be achieved, and meanwhile time errors are reduced as much as possible.

The invention discloses a multi-heterogeneous unmanned aerial vehicle task allocation method based on an improved genetic algorithm, and belongs to the technical field of unmanned aerial vehicles. Themethod is characterized by establishing a task allocation optimization model by comprehensively considering multiple constraints such as resource consumption, task completion effect and load balancing, resource limitation and task priority; and encoding each feasible task allocation scheme into a complete chromosome by adopting a matrix encoding mode. Aiming at the problems that an existing genetic algorithm is insufficient in solving precision and too slow in solving speed, the concept of fuzzy elitibility is provided, all genetic operations are improved on the basis, the built optimizationmodel is solved through the improved genetic algorithm, and an optimal task allocation scheme is obtained within limited iteration times. The method has good universality in the field of multi-agent cooperative control, has the advantages of high solving speed and high solving precision, and can effectively solve the task allocation problem of a multi-heterogeneous unmanned aerial vehicle system with multiple constraints.

The invention designs a method for realizing unmanned aerial vehicle group formation reconstruction based on a genetic algorithm and a Dubins algorithm. The method specifically comprises the followingsteps: numbering unmanned aerial vehicles, establishing a position matching relation of each unmanned aerial vehicle in a new formation, and consequently completing coding of chromosomes; improving the Dubins algorithm, building an air route planning model, evaluating distance of completing reconstruction flight by a wing unmanned aerial vehicle; and allocating a reconstruction target position for each unmanned aerial vehicle based on the genetic algorithm. In the method provided by the invention, formation reconstruction is divided into task allocation and air route planning, relative to theexisting formation reconstruction algorithm, more stable air routes can be obtained, moreover, speed range and radius of turning circle of the unmanned aerial vehicles are considered, the air routesgenerated can be more rational and can be used in actual application more easily. In the method provided by the invention, by a mode of limiting variation and intersecting, each unmanned aerial vehicle is guaranteed to have a position allocated, situations of missing of allocation and allocating in mistake can be prevented, and quality of task allocation is improved further.

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 active power distribution network distribution automation terminal optimal configuration method. The method is used for realizing distribution automation terminal configuration at a section switch of a distribution network. According to the method, a genetic algorithm is adopted to solve a constructed distribution automation terminal optimal configuration mathematical model, and an optimal configuration scheme is obtained. In the genetic algorithm, chromosomes are formed based on the configuration state of the power distribution automation terminal at each section switch, one chromosome represents one configuration scheme, and the fitness value of each chromosome is obtained based on the target function in the power distribution automation terminal optimal configuration mathematical model and the power supply reliability under the configuration scheme. Compared with the prior art, the method optimizes and determines the installation type and the number of the DA terminals in the active power distribution network, considers the power supply reliability of the DA configuration to the active power distribution network, and improves the accuracy, the efficiency and the economy of the distribution automation terminal configuration.

The invention discloses an underwater robot path planning method. The method comprises the steps of constructing an environment model under a rectangular coordinate system according to the related parameters of an AUV and the known obstacle information; sorting the obstacles according to the positions of the obstacles under the rectangular coordinate system; randomly generating a chromosome groupaccording to a ternary coding rule; decoding the chromosomes, solving a path corresponding to each chromosome and the length of each chromosome, and judging whether an intersection point with the dangerous area exists or not through a detection operator; calculating the fitness value of each individual in the chromosome group according to a fitness function; selecting the optimal chromosome at thecurrent stage according to the fitness value; increasing the diversity of chromosome populations through a crossover operator and a mutation operator, and supplementing new chromosomes as one part ofthe population of next iteration; and outputting a planned path through multiple iterations. According to the method, the own kinematics constraint of the AUV is considered, and the path planning ofthe AUV is realized through the combination of a Dubins curve and a genetic algorithm.

The invention discloses an automatic container wharf exit box space distribution method. The method comprises the following steps: solving an exit container space distribution problem by utilizing a genetic algorithm, wherein each gene site on the chromosome represents a selectable shellfish site; setting an optimal target function on the basis of the shipment turnover time of an exit container, solving the allocation quantity of each bay in each container area, simulating a port gathering sequence of all containers under the allocation quantity on the premise of knowing the allocation quantity of the bays, and outputting the stacking state information of all the containers according to a set container stacking rule. According to the method, the outlet box space distribution problem is optimized based on the genetic algorithm, more reasonable space distribution is achieved, and the method can be practically applied to various wharf scenes such as unknown ship berths and random harbor gathering sequences.

The invention discloses a fault prediction method based on characteristic quantity optimization and a wavelet kernel function LSSVM, and relates to the technical field of fault prediction of power transformers. The method comprises the following steps: firstly, acquiring a ratio of DGA characteristic quantity, secondly, establishing a support vector machine model, and selecting a radial basis function as a kernel function of a classification model; Then, encoding the candidate characteristic quantity and the penalty factor c kernel parameter of the SVM classification model to the same chromosome, optimizing the chromosome by adopting a genetic algorithm, wherein the optimal characteristic quantity is the optimal chromosome selected from the genetic algorithm; taking the optimal feature combination as the input of the next fault prediction and diagnosis function; establishing a wavelet kernel function-least squares support vector machine prediction model; And optimizing the penalty factor of the prediction model and the kernel parameters of the wavelet kernel function by using an imperialism competition algorithm to obtain an optimal parameter combination, and constructing an optimal prediction model based on the parameter combination. Operation state analysis and fault prediction of the transformer at a future moment are realized.

The invention relates to the technical field of equipment test identification. The invention discloses a method for constructing an equipment combat test identification index system. The method is anequipment combat test identification index optimization method based on a rough set and a genetic algorithm and comprises the steps that (1) a basic framework of an equipment combat test index systemis constructed; (2) a decision information table is constructed, the concept of the decision information system is derived from a rough set method, and definition is given to the rough set and the decision information system; (3) the attribute reduction method based on the genetic algorithm comprises the following steps: a) chromosome coding, b) fitness function, c) roulette strategy selection operation, d) crossover operation,..; and (4) a simplified index set is obtained to obtain an attribute reduction set of the equipment decision information system. Through the rough set and the genetic algorithm method, various uncertain information can be well processed, qualitative and quantitative information is analyzed and mined, attributes of the information are reduced, and a simplified indexset is obtained; intelligent evaluation is achieved, and the method is simple, visual and good in effect.

The invention discloses an underwater wireless sensor network topology control method based on a genetic algorithm. The method comprises the following steps: a) constructing an underwater network model; b) establishing member node and cluster head node energy models and energy parameters; c) calculating the optimal cluster head number; d) determining an optimal cluster head by adopting a genetic algorithm, including the substeps: d-1) performing chromosome coding; d-2) selecting an initial population; d-3) constructing a fitness function; d-4) constructing a selection operator, a crossover operator and a mutation operator; d-5) calculating an individual fitness value; d-6) selecting an optimal cluster head; e) self-clustering and carrying out data communication. According to the underwaterwireless sensor network topology control method, the genetic algorithm is used in the cluster head selection stage, the multi-objective optimization problem can be solved, the energy and distance factors can be effectively balanced, the energy consumption of each node can be effectively balanced, and the overall energy consumption of the network is reduced, so the lifetime of the whole network isprolonged.