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266 results about "Chromosome (genetic algorithm)" patented technology
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In genetic algorithms, a chromosome (also sometimes called a genotype) is a set of parameters which define a proposed solution to the problem that the genetic algorithm is trying to solve. The set of all solutions is known as the population. The chromosome is often represented as a binary string, although a wide variety of other data structures are also used.
Global optimization, search and machine learning method based on the lamarckian principle of inheritance of acquired characteristics
InactiveUS20180260714A1Simple structureImprove global optimalityBiostatisticsMachine learningAcquired characteristicGenetics algorithms
The invention discloses a global optimization, search and machine learning method based on the Lamarckian principle of inheritance of acquired characteristics, comprising step 1: constructing an objective function ƒ(P) according to the problem being solved, where P represents a set of candidate solutions to the problem; step 2: encoding P into a genetic algorithm (GA) chromosome, inputting or automatically calculating algorithmic parameters of the GA, and initializing the algorithm and the population of candidate solution generation G0={P01, P02, . . . , P0S}, where S is the size of the population G and 0 stands for the initial generation; step 3: at generation k, optimizing the prevailing population of the candidate solutions Gk={Pk1, Pk2, . . . , PkS} iteratively using a Lamarckian “Heredity Operator” and a “Use-and-Disuse Operator” based on the values of ƒ(Gk); and step 4: outputting the final set of optimal solutions to the problem.
Owner:DONGGUAN UNIV OF TECH +2
Genotic algorithm optimization method and network
InactiveUS6957200B2Digital computer detailsElectric digital data processingPopulationFitness function
Sensors are selected from a sensor network for tracking of at least one target. The sensors are selected using a genetic algorithm construct having n chromosomes, wherein each chromosome represents one sensor, defining a fitness function based on desired attributes of the tracking, selecting one or more of the individuals for inclusion in an initial population, executing a genetic algorithm on the initial population until defined convergence criteria are met, wherein execution of the genetic algorithm has the steps of choosing the fittest individual from the population, choosing random individuals from the population and creating offspring from the fittest and randomly chosen individuals. In one embodiment, only i chromosomes are mutated during any one mutation, wherein i has a value of from 2 to n−1.
Owner:HONEYWELL INT INC
Evolutionary controlling system
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.
Owner:YAMAHA MOTOR CO LTD
Method For Task Scheduling And Resources Allocation And System Thereof
A system and method for automatically assigning multiple tasks with multiple objectives to multiple teams (group of technician on field) is provided. This invention provides a means to minimize total operation cost and business process through the optimization consisting of the best assignment of resources to available task. In the tasks assignment and resource allocation method disclosed herein, new genetically adapted search agent (Genetic algorithm) are employed to improve the population, to provide the most optimum match matrix among jobs and teams, and calculate the best score of the fittest chromosome. The assignments generated by this method satisfy all constraints.
Owner:TELEKOM MALAYSIA BERHAD
Reconfigurable assembly line sequencing method based on improved genetic algorithm
InactiveCN102323952AUniform rateEquilibrium costGenetic modelsSpecial data processing applicationsGenetics algorithmsAssembly line
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.
Owner:HOHAI UNIV CHANGZHOU
Hybrid genetic algorithm-based MES (Manufacturing Execution System) production planning and scheduling method
InactiveCN105629927AMaintain population diversityAvoid premature convergenceTotal factory controlProgramme total factory controlRandom assignmentPopulation
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.
Owner:WUHAN KAIMU INFORMATION TECH
Parameter optimization control method of semiconductor advance process control
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.
Owner:苏科斯(江苏)半导体设备科技有限公司
Allocation method and device for multitasking of unmanned aerial vehicle
ActiveCN107103164AAccurately calculate sailing timeExcellent flight pathGeometric CADDesign optimisation/simulationGenetic algorithmMotion parameter
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.
Owner:HEFEI UNIV OF TECH
Multi-intelligent robot task distribution method facing dynamic task
ActiveCN108416488AAvoid deadlockSolve multitasking problemsResourcesDNA computersTask completionLocal optimum
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.
Owner:CENT SOUTH UNIV
Method for selecting broadcast routing path using genetic algorithm in Ad-hoc network
ActiveUS20070133504A1Minimize the numberMinimize power consumptionPower managementNetwork topologiesGenetic algorithmDistributed computing
Provided is a method for selecting a broadcast routing path using a genetic algorithm in an Ad-hoc network. In the method, a plurality of nodes of the Ad-hoc network is defined as one chromosome, and the chromosome is represented with pairs of {order, power} in each node. Child nodes are created by performing an order based crossover and a power based crossover with respect to parent nodes neighboring to a source node. A mutation operation is performed with respect to the parent nodes and the child nodes. Relay nodes are determined by converting order and power information of each node of the chromosome into routing tree information. Power of the relay nodes is determined. A broadcast routing path is selected using the number and power of the relay nodes.
Owner:ELECTRONICS & TELECOMM RES INST
Method and system for combinatorial layout design
InactiveUS9317626B2Quality improvementShorten the timeMulti-objective optimisationSpecial data processing applicationsAlgorithmGenetics algorithms
The present invention discloses a method for combinatorial layout design, in particular family mold layout design, using a computer. The method starts with receiving design specific parameters and information about molding parts from user. A Genetic Algorithm module is then invoked to generate a population of layout designs which satisfies the design specific parameters. The Genetic Algorithm module first automatically generates a population of specially designed chromosome with three interdependent sessions. Crossover, mutation and replacement operation are applied on the population subsequently to evolve such towards a more optimal population over successive generations. In each generation step, a Genotype-Phenotype mapping module is utilized to decode the chromosome to corresponding layout design for fitness evaluation. A system for combinatorial layout design automation and optimization using this evolutionary design approach is also disclosed in the present invention.
Owner:CHAN WAI MAN
Energy spectrum overlapping peak analysis method
InactiveCN105203565AEfficient decompositionMaterial analysis using wave/particle radiationAlgorithmGenetics algorithms
The invention discloses an energy spectrum overlapping peak analysis method. The analysis method includes the steps that background rejection is carried out on energy spectrum sections which are obtained from radioactive measurement and to be subjected to overlapping peak resolution, and net peak areas of overlapping peaks and all channel net counts corresponding to the net peak areas of the overlapping peaks are obtained; the energy spectrum sections obtained after background rejection are regarded as a linear sum of multiple Gaussian functions; parameters of the Gaussian functions are combined into a chromosome; population initialization is carried out on the combined chromosome, probability construction fitness functions, from individuals, of the energy spectrum sections are combined, selection, cross and mutation operators of a genetic algorithm are adopted, the weight, average and standard deviation of all the Gaussian functions are obtained after multi-generation operation, and overlapping peak resolution is completed. Calculation is simple, the overlapping peaks overlapping three or more spectrum peaks can be resolved, and the overlapping peak resolving method can be effectively applied to qualitative and quantitative analysis for the spectrum peaks and is good in performance.
Owner:CHENGDU UNIVERSITY OF TECHNOLOGY
Method for selecting broadcast routing path using genetic algorithm in Ad-hoc network
ActiveUS7613165B2Minimize consumptionMinimize the numberPower managementInformation formatAlgorithmGenetics algorithms
Provided is a method for selecting a broadcast routing path using a genetic algorithm in an Ad-hoc network. In the method, a plurality of nodes of the Ad-hoc network is defined as one chromosome, and the chromosome is represented with pairs of {order, power} in each node. Child nodes are created by performing an order based crossover and a power based crossover with respect to parent nodes neighboring to a source node. A mutation operation is performed with respect to the parent nodes and the child nodes. Relay nodes are determined by converting order and power information of each node of the chromosome into routing tree information. Power of the relay nodes is determined. A broadcast routing path is selected using the number and power of the relay nodes.
Owner:ELECTRONICS & TELECOMM RES INST
Soft measurement method for granularity of cement raw material grinded by ball mill
ActiveCN103344530AIncrease credibilityImprove usabilityParticle size analysisNeural learning methodsOriginal dataBall mill
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.
Owner:SHENYANG INST OF AUTOMATION - CHINESE ACAD OF SCI
Walking aid electrostimulation fine control method based on genetic-ant colony fusion fuzzy controller
ActiveCN101837165AAdjust the quantization factor in real timeImprove stimulation effectAdaptive controlArtificial respirationFuzzy control systemGenetic algorithm
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.
Owner:大天医学工程(天津)有限公司
Systems and methods for index selection in collections of data
ActiveUS8499001B1Improve performanceShorten the timeDigital data processing detailsSpecial data processing applicationsComputer resourcesData set
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.
Owner:QUEST SOFTWARE INC
Estimation method of Gaussian overlapped nuclear pulses
InactiveCN106156852AImprove accuracyIncrease credibilityGenetic modelsNuclear engineeringGenetics algorithms
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.
Owner:CHENGDU UNIVERSITY OF TECHNOLOGY
Optimum solution search method and optimum solution search apparatus as well as storage medium in which optimum solution search program is stored
InactiveUS6363368B2Shorten the timeIncrease speedDigital computer detailsInference methodsSearch problemGenetic algorithm
The invention provides a technique regarding an optimal solution search method suitable for use for optimization of a system, wherein genetic algorithms can be executed at a high speed to allow an optimal solution to a problem to be searched for at a high speed. The optimal solution search method wherein candidates for solution to a problem are represented as chromosomes which are arrangements of genes and genetic operations are performed for individual chromosomes for each generation to successively update the generation to search for an optimal solution to the problem based on fitness values calculated from gene arrangements of the chromosomes.
Owner:FUJITSU LTD
Flexible manufacturing system deadlock-free scheduling method based on improved genetic algorithm
InactiveCN111563336AEasy to operateIncrease productivityForecastingDesign optimisation/simulationFlexible manufacturing systemProcessed Genes
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.
Owner:NANTONG UNIVERSITY
Hybrid genetic algorithm for solving multi-objective flexible job-shop scheduling problem
InactiveCN106610651AAvoid extra computing timeSimplify the coding processProgramme total factory controlCompletion timeAlgorithm
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.
Owner:SICHUAN YONGLIAN INFORMATION TECH CO LTD
Path planning method for multiple unmanned aerial vehicles to arrive at designated place simultaneously in three-dimensional environment
ActiveCN111273686ACheap to flyFewer constraint violationsPosition/course control in three dimensionsSimulationGenetics algorithms
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.
Owner:BEIJING INSTITUTE OF TECHNOLOGYGY
Centralized resource management method based on genetic algorithm
The invention discloses a centralized resource management method based on a genetic algorithm, and relates to the field of wireless communication. The method mainly includes the steps that S1, network resources and users in a system are integrated, two-dimension chromosome coding is carried out on resource allocation, and N individuals are generated randomly and serve as an initial population, wherein N is an integer larger than two; S2, dynamic power distribution is carried out on each chromosome, and based on the power distribution and user requirements, fitness functions of the individuals are built; S3, population propagation is carried out, wherein the population propagation includes the processes of selection, intersection, mutation and correction, and the number of filial generation individuals is kept to be identical to that of parent individuals; S4, the parent individuals are replaced by the filial generation individuals, and the population propagation processes are repeated until an iteration stopping condition is met. The use ration of the power of the system can be improved, under the condition that the real-time user requirements are met, fairness among non-real-time users can still be effectively ensured, and system performance is greatly improved.
Owner:BEIJING UNIV OF POSTS & TELECOMM
Multi-heterogeneous unmanned aerial vehicle task allocation method based on improved genetic algorithm
ActiveCN111860984AFast convergenceImprove work efficiencyInternal combustion piston enginesForecastingAlgorithmGenetics algorithms
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.
Owner:河北梓墨电子科技有限公司
Methods and systems for identification of DNA patterns through spectral analysis
InactiveUS8189892B2Improve visualizationHigh resolutionData visualisationBiostatisticsDNA PatternsTandem repeat
Systems and methods facilitate the location and / or identification of repetitive DNA patterns, such as CpG islands, Alu repeats, tandem repeats and various types of satellite repeats. These repetitive elements can be found within a chromosome, within a genome or across genomes of various species. The systems and methods apply image processing operators to find prominent features in the vertical and horizontal direction of the DNA spectrograms. The systems and methods for detecting and / or classifying repetitive DNA patterns include: (a) a comparative histogram method, (b) feature selection and classification using support vector machines and genetic algorithms, and (c) generation of a spectrovideo from a plurality of spectral images.
Owner:KONINKLIJKE PHILIPS ELECTRONICS NV
Method for realizing unmanned aerial vehicle group formation reconstruction based on genetic algorithm and Dubins algorithm
ActiveCN110658847APrevent misclassificationQuality improvementPosition/course control in three dimensionsGenetics algorithmsUncrewed vehicle
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.
Owner:BEIHANG UNIV
Parameter optimization method based on improved genetic algorithm, computer equipment and storage medium
PendingCN111898206ATake into account the overall situationTaking into account local optimizationGeometric CADSpecial data processing applicationsControl systemGenetics algorithms
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.
Owner:CHANGAN UNIV
Active power distribution network distribution automation terminal optimal configuration method
PendingCN110808633AExact installation typeAccurate quantityCircuit arrangementsMathematical modelControl engineering
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.
Owner:SHANGHAI UNIVERSITY OF ELECTRIC POWER
Underwater robot path planning method
ActiveCN110906936ARealize global path planningNavigational calculation instrumentsPosition/course control in three dimensionsAlgorithmRectangular coordinates
Owner:HARBIN ENG UNIV
Automatic container wharf exit box space distribution method
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.
Owner:SHENZHEN UNIV
Service routing addressing method for service operation of power communication network
ActiveCN110971525AThe effect of optimizing addressingImprove addressing efficiencyMultiplex system selection arrangementsData switching networksGenetics algorithmsPhysical layer
The invention discloses a service routing addressing method for service operation of a power communication network. Firstly, modeling is carried out on a power communication network; a chromosome coding mode based on priority is adopted; the service running state of the communication network is encoded; decoding is carried out by using a decoding mode corresponding to the code to obtain a servicepath set, then joint analysis is carried out on vulnerability factors of nodes and links in a physical layer, a network topology layer and a service organization layer to obtain a comprehensive vulnerability evaluation index of the power communication network, and an objective function and constraint conditions are set according to the comprehensive vulnerability evaluation index of the power communication network; and finally, the set target function is taken as a target optimization function of a genetic algorithm, the service route is optimized by adopting the genetic algorithm according tothe constraint condition, and the service route meeting the condition is selected from the service route set. According to the invention, the addressing efficiency can be improved, and the addressingeffect is improved.
Owner:WUHAN UNIV +4
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