Product
Patsnap Eureka
For R&D, Patsnap Eureka makes reading and utilizing patents & technical documents easy.
Patsnap Eureka AIR
Designed for self-driven R&D workflows. Generate viable solutions, solve complex R&D challenges, empower your innovation with AI.
Patsnap Eureka Materials
Designed for material experts only. Revolutionize your material R&D, from search, analyze, to developing new materials.
Feature
TechResearch
Generate reliable direction feasibility study reports for your R&D in just a few steps.
TechSeek
Discover and master advanced knowledge NOW. Basics, ideas, possibilities, all at once.
TechMind
As an expert in R&D Theories, TechMind can generates customized viable solutions instantly.
TechRisk
Analyze your overall solution with one click, know your potential R&D risks in advance.
TechMonitor
Get weekly tech updates, stay abreast of the latest tech innovations and key insights.
Patents
Literature
Hiro is an intelligent assistant for R&D personnel, combined with Patent DNA, to facilitate innovative research.
257 results about "Mutation operator" patented technology
Efficacy Topic
Property
Owner
Technical Advancement
Application Domain
Technology Topic
Technology Field Word
Patent Country/Region
Patent Type
Patent Status
Application Year
Inventor
Mutation (or mutation-like) operators are said to be unary operators, as they only operate on one chromosome at a time. In contrast, crossover operators are said to be binary operators, as they operate on two chromosomes at a time, combining two existing chromosomes into one new chromosome.
Space-ground integrated network resource allocation method based on improved genetic algorithm
ActiveCN108880663AImprove resource utilizationTake advantage ofRadio transmissionLocal optimumMutation operator
The invention discloses a space-ground integrated network resource allocation method based on an improved genetic algorithm, comprising the following steps: defining parameters and decision variables;establishing a multi-objective constraint model; and allocating resources based on the improved genetic algorithm. The method considers the allocation of multiple resources, so that the resource utilization rate of the space-ground integrated network is significantly improved. The improved selection mechanism effectively retains elite individuals and speeds up the convergence of the improved genetic algorithm. The shortest time for completing all tasks is taken as a objective function, and the priorities of the tasks are considered at the same time, so that the rationality of resource allocation is effectively improved; and the elite retention strategy is combined with the roulette strategy to improve the selection mechanism, adaptive crossover and mutation operators are designed to improve the existing genetic algorithm, and the improved algorithm can effectively avoid the shortcomings of poor local optimization ability of the genetic algorithm and easiness to fall into local optimum, prevent the loss of the optimal solution and effectively improve the optimization speed.
Owner:DALIAN UNIV
Operation workshop scheduling modeling method based on genetic algorithm
InactiveCN103870647AOptimizing and Harmonizing OperationsImprove Design PerformanceGenetic modelsSpecial data processing applicationsAlgorithms performanceTrace diagram
The invention discloses an operation workshop scheduling modeling method based on a genetic algorithm. The method comprises the steps of JSP genetic algorithm design of reverse cross of a stored gene segment, eM-Plant simulation modeling, data collection, improvement of mutation operator and obtaining of an optimized scheme; the JSP genetic algorithm design of the reverse cross of the stored gene segment comprises the steps of randomly generating an initial group according to a sequence code, calculating the fitness of the initial group, judging whether the cycling times is satisfied, outputting an optimal result and program running time if the cycling times is satisfied, drawing an algorithm performance trace diagram, drawing an optimal scheduling trace diagram, selecting through a roulette wheel if the cycling times cannot be satisfied, reversely crossing the stored gene segment, randomly mutating the gene segment, calculating the fitness of a novel population, re-inserting a filial-generation population to the parental population, and recording the performance of the optimal result trace algorithm. By adopting the method, the running of the production workshop can be optimized and coordinated, the design effect is good, the process is simple, and the production danger and production cost can be reduced.
Owner:XIAN TECH UNIV
Mobile-robot route planning method based on improved genetic algorithm
InactiveCN106843211AImprove environmental adaptabilityStrong optimal path search abilityPosition/course control in two dimensionsGenetic algorithmsProximal pointTournament selection
The invention relates to a mobile-robot route planning method based on an improved genetic algorithm. A raster model is adopted to preprocess a working space of a mobile robot, in a rasterized map, an improved rapid traversing random tree is adopted to generate connections of several clusters between a start point and a target point, portions for the mobile robot to freely walk on in the working space are converted into directed acyclic graphs, and a backtracking method is adopted to generate an initial population which is abundant in diversity and has no infeasible path on the basis of the directed acyclic graphs. Three genetic operators, namely a selection operator, a crossover operator and a mutation operator, are adopted to evolve the population, wherein the selection operator uses a tournament selection strategy, the crossover operator adopts a single-point crossover strategy, and the mutation operator adopts a mutation strategy which displaces an aberrance point with an optimal point in eight-neighbor points of the aberrance point. A quadratic b-spline curve is adopted to smooth an optimal route, and finally, a smooth optimal route is generated. According to the method, the route planning capability of the mobile robot under a complex dynamic environment is effectively improved.
Owner:DONGHUA UNIV
WSN (Wireless Sensor Networks) multi-objective optimization routing method based on genetic algorithm
ActiveCN105430707AImprove stabilitySimple structureHigh level techniquesWireless communicationMutation operatorAlgorithm
Owner:ELECTRIC POWER RES INST STATE GRID JIANGXI ELECTRIC POWER CO +3
Method and system for optimizing software test case
InactiveCN103810104AReduce participationAvoid situations where test performance is affectedSoftware testing/debuggingMutation operatorSystem under test
The invention discloses a novel method and system for optimizing a software test case. According to the invention, the advantage of analytic hierarchy process on determining weighted values, as well as the technical advancement of the ant colony algorithm and genetic algorithm on determining the simplest and first priority ordering case subset are utilized. The method comprises the following steps: determining the weighted value of each functional requirement of a to-be-tested system by using the single hierarchical arrangement and total ordering of the analytic hierarchy process as well as the quantification of consistency testing; determining a test case subset, with full covering on all functional requirements and minimum operation cost, by combining the ant colony algorithm with the obtained weighted values and through an essential strategy, a redundancy strategy and a greedy strategy; determining a new estimation formula by combining the genetic algorithm with the obtained weighted values and on the basis of obtaining the simplest case subset; determining a test case sequence with highest error detection rate by a selection operator, a crossover operator and a mutation operator.
Owner:中国人民解放军63863部队
Cloud computing task scheduling method based on improved NSGA-II
ActiveCN104035816AFast convergenceEvolution algebra fewResource allocationMutation operatorNew population
The invention provides a cloud computing task scheduling method based on the improved NSGA-II and relates to the field of cloud computing. The method includes the steps that firstly, the number of meta tasks is input, and a task scheduling model is generated through a DAG chart; secondly, the number of virtual machines is input, the virtual machines of different specifications are generated randomly, and a cluster model is generated; thirdly, a cloud computing task scheduling problem is expressed as a multi-target solving problem relevant to time and cost, and the problem is solved with the combination of the improved NSGA-II. A new population is generated by the adoption of a similarity task sequence crossover operator and a displacement mutation operator in the population evolution process according to the features of task scheduling, meanwhile, a congestion distance self-adaptation operator is introduced in, it is ensured that the optimal border of the obtained time and cost is obtained, and cloud computing task scheduling is achieved. The searching capability for the optimal solution in the application of cloud computing task scheduling becomes stronger, the population diversity can be better kept, and the optimal solution set with the better distributivity is obtained.
Owner:WUHAN FIBERHOME INFORMATION INTEGRATION TECH CO LTD
Polyclone artificial immunity network algorithm for multirobot dynamic path planning
InactiveCN103412490ARealization of dynamic path planningIncrease diversityAdaptive controlPosition/course control in two dimensionsMutation operatorAlgorithm
The invention provides a polyclone artificial immunity network algorithm for multirobot dynamic path planning, and relates to an improved polyclone artificial immunity network algorithm. According to the polyclone artificial immunity network algorithm, a polyclone artificial immunity network is applied to multiple mobile robot dynamic path planning, mutual influence among robots and influence on the robots of mobile barriers are considered, a computational formula of antibody concentration is defined, diversity of antibodies is increased through clonal operators, crossover operators, mutation operators and selection operators, and the problem of premature convergence of a traditional immune network is solved. Specific antibodies corresponding to antigens in a specific environment are stored, initial concentration of the specific antibodies is increased, response time is shortened, and multiple mobile robot dynamic path planning in an unknown environment is effectively achieved.
Owner:SHANDONG UNIV
Many-objective optimized scheduling method for combined operation of cascade hydropower stations
InactiveCN106203689AGuaranteed uniformityEnhanced Neighborhood Exploration CapabilitiesForecastingArtificial lifePareto optimalHydropower
The invention discloses a many-objective optimized scheduling method for combined operation of cascade hydropower stations, and aims at solving main problems in engineering application of standard quantum-behaved particle swarm optimization and problems in solving single-objective optimized scheduling. A multi-population evolution strategy is realized by external file set, advantageous individual selection and a chaotic mutation operator strategies, diversity of individuals is ensured, calculation of the method is accelerated, and an approximate Pareto optimal leading edge with sound distribution is obtained. An external file set is introduced to store elite individuals, dynamic update and maintenance of the file set are realized via non-inferior layered ordering and crowd distance, and distribution of the individual is kept uniform; and a chaotic mutation operator is used to carry out local disturbance on a non-control solution, and the neighborhood exploration capability of the individuals is enhanced. According to the invention, the particle swarm optimization is improved, and effectively applied to making the many-objective optimized scheduling scheme of combined operation of the cascade hydropower stations, and a feasible and high efficiency calculating method is provided for many-objective optimized scheduling of the cascade hydropower stations.
Owner:DALIAN UNIV OF TECH
Single-instruction-set heterogeneous multi-core system static task scheduling method
InactiveCN102866912AImprove initialization efficiencyAddress serious deficienciesMultiprogramming arrangementsMutation operatorOptimal scheduling
Provided is a single-instruction-set heterogeneous multi-core system static task scheduling method. The method includes five steps: step 1, population initialization; step 2, fitness value calculation; step 3, selection operator operation; step 4, cross operator operation; and step 5 variation operator operation. A local sequence represents an executing sequence of two tasks without depending relations, population initialization efficiency and effective individual are greatly improved, the executing sequence of the tasks is determined through a pre-order-relation matrix, and defects of a traditional height value method are overcome. The method can widen a hunting range of optimal individuals. When the population scale is large enough, a part of optimal solutions missed by the height value method can be found so as to obtain a more optimal scheduling sequence. For the same task set, finishing time of the whole task set is short, power consumption is low, and a purpose of energy conservation and consumption reduction is achieved.
Owner:CAPITAL NORMAL UNIVERSITY
Method for optimizing disaster emergency decision system path
The invention relates to the technical field of artificial intelligence algorithms, and provides a method for optimizing a disaster emergency decision system path. The method comprises the steps that S11, an objective function and a fitness function are defined; S12, chromosome coding is carried out; S13, operators are selected according to the fitness function; S14, the operators are crossed; S15, the operators are mutated according to the fitness function and pheromone updating guiding mutation rules; S16, a plurality of sets of optimal solutions are generated, and the best solution is output through the fitness function. The quality and efficiency of the solutions are effectively improved through the pheromone guiding mutation rules of an ant colony algorithm. A large amount of useless redundant iteration of a genetic algorithm is avoided through the positive feedback mechanism and the parallelism of the ant colony algorithm, and the solution speed of the genetic algorithm is further improved. The decision model with a single saving point and multiple disaster points based on continuous supply losses is more suitable for emergency situations, and practical significance is achieved.
Owner:SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
Parameter selection method for support vector machine based on hybrid bat algorithm
The invention discloses a parameter selection method for a support vector machine based on a hybrid bat algorithm. Regularization parameters and RBF kernel parameters have great influences on the learning performance and computation complexity. On the basis of analyzing the advantages and disadvantages of some classical parameter selection methods, an intelligent optimization algorithm is introduced to perform optimization on the parameters. The bat algorithm has the advantages of concurrency, high convergence speed and strong robustness. The bat algorithm is firstly utilized to perform optimization on the SVM parameters, then crossover, selection and mutation operators of differential evolution algorithm are introduced in allusion to a defect of early maturing of the bat algorithm, the position is further adjusted according to the three operators in each iteration process by using a bat individual, the search ability of the algorithm is enhanced, the algorithm is avoided from prematurely falling into a local optimal solution, and finally the SVM parameter selection is optimized by using an improved DEBA algorithm to obtain an excellent effect.
Owner:BEIJING UNIV OF TECH
Frequency stabilization control method for alternating-current/direct-current series-parallel system
ActiveCN104300568AImprove transient performanceImprove steady state performanceElectric power transfer ac networkSingle network parallel feeding arrangementsFrequency stabilizationInstability
The invention discloses a frequency stabilization control method for an alternating-current / direct-current series-parallel system, which comprises the steps of designing an under-frequency load shedding optimization model considering emergency power generation control and high-voltage direct current power support, providing an objective function giving consideration to the frequency recovery performance and the load shedding amount, selecting the action frequency of an under-frequency load shedding program and the load shedding amount of each turn as control variables, carrying out optimization on a scheme by using a cloud adaptive particle swarm algorithm containing a dimension mutation operator, and realizing intelligent control for the frequency of the alternating-current / direct-current series-parallel system. The control method disclosed by the invention solves a problem of coordination and optimization between the system instability possibly caused by frequency oscillation after the alternating-current / direct-current series-parallel system breaks down and the economy of system frequency recovery; an under-frequency load shedding scheme is optimized, an optimal setting scheme is searched intelligently, the frequency recovery performance is considered, the load shedding amount is minimized, and the transient-state performance and the steady-state performance of the alternating-current / direct-current series-parallel system are improved.
Owner:SOUTH CHINA UNIV OF TECH
Genetic ant algorithm-based unmanned aerial vehicle global path planning method
PendingCN107229287AReduce data volumeReduce raster sizeForecastingArtificial lifeMutation operatorAlgorithm
The invention discloses a genetic ant algorithm-based unmanned aerial vehicle global path planning method. The method includes the following steps that: modeling is performed for an environment through an improved grid method; a part of optimal solutions obtained by an MMAS (Max-Min Ant System) algorithm are transformed into the initial solutions of an EGA (Elitist Genetic Algorithm); the MMAS algorithm and the EGA are utilized to simultaneously perform path optimization; and an improved mutation operator and the MMAS algorithm are utilized to perform further optimization, and an optimal path is obtained. According to the genetic ant algorithm-based unmanned aerial vehicle global path planning method of the invention, a new sub-region division method is adopted, and therefore, the expression forms of the solutions are simplified, and the computation quantity and storage quantity of data are decreased; according to the characteristics of the convergence rates of the MMAS algorithm and the EGA, a method according to which iteration optimal solutions and optimal solutions obtained through EGA optimization are adopted each time to jointly update pheromones is adopted, and therefore, the search efficiency of the optimal solution can be improved; and when the algorithm is stagnant, the number of ants is increased, and the EGA mutation operator is improved, and therefore, the search efficiency of the optimal path can be further improved.
Owner:NAVAL UNIV OF ENG PLA
Using a genetic algorithm employing dynamic mutation
Apparatus and method for at least partially fitting a medical implant system to a patient is described. These apparatuses and methods comprise using a dynamic mutation rate. This genetic algorithm may comprise generating successive generations of child populations. In executing the genetic algorithm, children may undergo mutations based on a mutation rate. This mutation rate may be dynamic and be based on the characteristics of the children in the generation. Additionally, values may be frozen during execution of the genetic algorithm if the likelihood that the value has converged on a particular value exceeds a threshold.
Owner:COCHLEAR LIMITED
An optimization algorithm based on multi-objective resource-constrained project scheduling model
The invention provides an optimization algorithm based on multi-objective resource-constrained project scheduling model. The scheduling model requires scheduling the start time of each activity to achieve the optimal goal under the condition of satisfying the relevant constraints. Based on the RCPSP model, the invention introduces the optimal resource balance as the objective, and expands the model to a multi-objective model. The solution of RCPSP is mainly based on the heuristic algorithm. When the task list is used to encode chromosomes in the heuristic algorithm, the task list initialized randomly may not satisfy the constraint relation between the top and bottom. The invention provides an individual generation mode based on control relation, which presents a new crossover operator andmutation operator based on the NSGA-II algorithm. The invention can greatly reduce the time complexity of the algorithm, realize the balanced allocation of resources, improve the production efficiencyand save the production cost while ensuring the solution precision of the algorithm, thereby improving the economic benefit of the resource scheduling production process.
Owner:WUHAN UNIV
Method for optimizing electronic component parameters in antenna broadband matching network by adopting genetic-simulated annealing combination
ActiveCN102722613AIncrease computing speedImprove computing efficiencyGenetic modelsSpecial data processing applicationsOptimization problemAntenna matching
The invention discloses a method for optimizing electronic component parameters in an antenna broadband matching network by adopting the genetic-simulated annealing combination. According to the method, on the basis of a genetic algorithm, secondary optimization is carried out by a simulated annealing algorithm, so that the defect of poor fine tuning capacity of the genetic algorithm is overcome; and meanwhile, an optimum individual obtained by adopting the genetic algorithm to optimize is used as an initial value of a variable to be optimized by the simulated annealing algorithm, so that the independence of the simulated annealing algorithm on the initial value is avoided. Moreover, aiming at the optimization problem of the antenna matching network, the combination method adopts a multi-target parallel selection method for giving consideration to the requirements of two important technical indexes of the antenna standing wave ratio and the conversion efficiency, introduces the self-adaptive adjustment of crossover and mutation operators and is beneficial for improving the calculating speed and efficiency of the algorithm. Meanwhile, an optimal solution retention strategy is introduced so as to prevent the optimum individual from losing.
Owner:BEIHANG UNIV
Photovoltaic fault detection method based on improved particle swarm optimization Elman network
InactiveCN108665112AOvercome the defects of local optimal solutionEasy maintenanceForecastingNeural learning methodsLocal optimumNeural network topology
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.
Owner:DONGHUA UNIV
Wind farm multi-model draught fan optimized arrangement method based on genetic algorithm
InactiveCN103793566ACoding is intuitiveIntuitive and accurate position relationshipSpecial data processing applicationsAlgorithmSquare mesh
Owner:TONGJI UNIV
Thermal power plant environment economic dispatching method based on multi-target differential evolution algorithm
The invention discloses a thermal power plant environment economic dispatching method based on a multi-target differential evolution algorithm.The method comprises the following steps that a thermal power plant economic dispatching model with the lowest electricity generation cost and smallest pollutant discharge quantity as targets and with generator capacity and power balance as constraint conditions is built; the multi-target differential evolution algorithm is utilized for carrying out optimization solving on the model, an optimal Pareto solution set is obtained, the multi-target differential evolution algorithm adopts difference mutation operators for searching, mutation operators are selected based on the accumulation performance and using frequency of the operators of the latest several times of variation, and solution set convergence and distribution uniformity are ensured by means of non-dominated ranking, domination frequency and hypervolume contribution and the like; finally, a decision is made through the fuzzy set theory, and a compromise solution is selected from the Pareto solution set to be used as a final regulation scheme.The thermal power plant environment economic dispatching method has the advantages that precision is high, Pareto leading edge solution set distribution is uniform and convergence speed is high, and engineering realization is easy.
Owner:SOUTHWEST PETROLEUM UNIV
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
Optimization method of hybrid energy storage capacity configuration of microgrid
InactiveCN109904869AIncrease diversityEnergy storageAc network load balancingConfiguration optimizationMicrogrid
Owner:GUANGDONG UNIV OF TECH
Semantic mutation operator based test case generation and optimization method
InactiveCN105868116ASemantic Variation IdealSemantic Variation ImplementationSoftware testing/debuggingProgramming languageMutation operator
The invention relates to a semantic mutation operator based test case generation and optimization method. The method is characterized by mainly including test case generation and initial test case set optimization, test case generation refers to that semantic mutation operators and mixed execution are combined to generate initial test case sets high in coverage rate as far as possible, and initial test case set optimization includes: capturing operation states and fault detect results of the initial test case sets in semantic mutant execution, optimizing according to related indexes provided by the method, and assessing the optimized test case sets.
Owner:北京京航计算通讯研究所
A job shop logistics distribution path optimization method based on a genetic algorithm
ActiveCN109685243AImprove adaptabilityFast convergenceForecastingLogisticsLocal optimumMutation operator
The invention discloses a workshop logistics distribution path optimization method based on a genetic algorithm. The workshop logistics distribution path optimization method is used for effectively planning multi-target node logistics distribution paths with priorities in discrete workshops. And on the basis of the layout diagram and the adjacency matrix of the job shop, an algorithm is applied tooptimize the logistics distribution path of the shop, and the objective function is optimized. In traditional multi-target path planning, path planning is divided into a plurality of single target nodes and a path planning problem of a single starting node, but the path planning problem generally can only obtain local optimum rather than global optimum. And a multi-target node path optimization model is established, and a proposed cross operator and a proposed mutation operator are applied from the perspective of global optimization, so that the solving speed is increased, and the solving precision is improved. By the adoption of the method, the path distance of logistics distribution in the workshops can be effectively reduced, the logistics distribution operation efficiency in the workshops can be improved, and conditions are created for improving the production efficiency in the workshops and improving the enterprise income.
Owner:NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
Coding and genetic algorithm suitable for power distribution network and application in distribution network reconfiguration
ActiveCN103440521ASatisfy radial requirementsDoes not break radial constraintsGenetic modelsInformation technology support systemMutation operatorAlgorithm
Disclosed are a coding and genetic algorithm suitable for a power distribution network and application in distribution network reconfiguration. Coding is conducted through names of nodes, depth of the nodes and the degrees of the nodes, male parents are selected, a later generation is obtained by cross and mutation breeding through a cross operator and a mutation operator, when the male parents are selected to be crossed and mutated, one male parent is selected at random while the other male parent is selected optimally. The cross operator is used for exchanging a connection relation between a certain node and a father node in each tree mapped by two codes. The mutation operator is used for changing a father node of a certain node in each tree mapped by the codes, wherein the certain node is provided with spare branches. Radial constraint does not need to be verified, the situation that an invalid network is operated and repaired repeatedly to obtain a valid network is avoided, and computing time is greatly shortened. The valid network can be obtained certainly in networks of any scales, and the problem that an existing method cannot be applied to reconfiguration of large-scale actual power distribution networks.
Owner:NANCHANG UNIV
Robot grid sub-map amalgamation method based on immune self-adapted genetic algorithm
InactiveCN101266659AEnable Coordinated ExplorationAchieve integrationProgramme-controlled manipulatorGenetic modelsAntigenBiology
The invention provides a robot grid sub-map fusion method based on immune adaptive genetic algorithm. A matrix corresponding to two grid sub-maps is regarded as an antigen. An antibody is plane transformation made by a second grid sub-map. An antibody colony generates a next antibody in operations of copying, crossing and mutation operator basing on affinity degree of the antigen and the antibody. A selection probability calculated according to similar vector distance guarantees multiformity of the antibody. On the base of an immune principle, a crossover probability and a mutation probability are adaptively adjusted according to sufficiency of the antibody to reduce a probability of local optimum. The invention has a high searching efficiency and can effectively search the best plane transformation randomly distributed in a searching space. The invention is especially fit for a multiple mobile robot grid sub-map fusion problem in complex environment. And the invention can realize information sharing among robots as soon as possible and effectively realize coordinating exploration among robots, and improve exploration efficiency.
Owner:SHANDONG UNIV
Multi-defect positioning method based on search algorithm
InactiveCN105760295AGood effectEfficiency is feasibleSoftware testing/debuggingMutation operatorGreedy algorithm
The invention discloses a multi-defect positioning method based on a search algorithm. The method includes the steps that 1, the search algorithm at a first stage is executed, wherein the following processing that firstly, a population with multi-defect distribution is initialized through a greedy algorithm, then a selection operator, a crossover operator and a mutation operator are executed to generate a new individual, the new individual is re-inserted into the original population, a next-generation population is formed, and when a terminal condition of the search algorithm is met, a second stage is executed is specially included; 2, multi-defect positioning at a second stage is executed, wherein a final defect distribution combined population is obtained, an executable entity rank is obtained according to candidate defect distribution populations, the executable entity sequence is mapped to a real position of a program, a rank of equivocation coefficients of corresponding program entities is obtained according to multi-defect distribution in the optimal candidate defect distribution population, and the algorithm is completed. The effect of an adopted GAMFal algorithm on the multi (single) defect positioning problem is superior to that of an existing SFL method; only little artificial participation is needed; the efficiency of the algorithm is feasible.
Owner:TIANJIN UNIV
Method for synthesizing directional diagrams of linear antenna arrays on basis of wavelet mutation wind drive optimization algorithms
ActiveCN104899374AHigh speedGood precisionSpecial data processing applicationsWind driven optimizationGlobal optimal
The invention discloses a method for synthesizing directional diagrams of linear antenna arrays on the basis of wavelet mutation wind drive optimization algorithms. The method includes steps of building models of the linear antenna arrays and determining comprehensive radiation characteristic requirements and objective functions of the antenna arrays; determining the wind drive optimization algorithms and wavelet mutation operator parameters and setting population sizes, weight values of fitness functions and speeds and position boundaries of air particles; randomly generating initial speeds and positions of the air particles, substituting the positions of the air particles into the fitness functions, sorting fitness values according to ascending order, updating population sequences and determining the global optimal positions and the local optimal positions; updating the speeds and the positions of the air particles; selectively carrying out wavelet mutation on the positions of the air particles according to mutation probability; computing fitness values of the air particles at novel positions, sorting the fitness values according to ascending order again, updating the population sequences and updating the global optimal positions and the local optimal positions until the maximum number of iterations are carried out. The method has the advantages of high solving precision and convergence speed.
Owner:JIANGSU UNIV OF SCI & TECH
Particle-swarm-genetic-algorithm-based selecting method of remanufacturing assembling process
ActiveCN107945045AImprove assembly accuracyIncrease powerData processing applicationsManufacturing computing systemsMutation operatorGenetic algorithm
The invention discloses a particle-swarm-genetic-algorithm-based selecting method of a remanufacturing assembling process. With the method, optimized selection of the assembling process of the remanufactured parts is realized; and more qualified products are selected by taking cost minimization as an objective. On the basis of a quality loss cost function and a remaining part cost function of theTaguchi quality method as model bases, a comprehensive remanufacturing matching and selecting model is constructed by using an enclosed ring dimension as a model constraint; part parameters collectedin the remanufacturing assembling process are guided into the model and crossover and mutation operators in a genetic algorithm are integrated into a particle swarm algorithm as an optimal optimization algorithm to optimize the model; and then an optical matching and selecting optimization plan for the remanufacturing assembling process is provided. Therefore, a problem of low matching success rate in the selection process of remanufactured parts is solved; the utilization rate of remanufacturing resources in the matching process is increased substantially; and the cost of the enterprise is lowered.
Owner:SHENYANG POLYTECHNIC UNIV
Method for optimizing project duration of engineering project based on potential anti-key working procedures
InactiveCN104820872AReduce the number of variablesSimplify the solution processForecastingResourcesMutation operatorStart time
The invention discloses a method for optimizing the project duration of an engineering project based on potential anti-key working procedures in the technical field of engineering project duration control technologies. The method comprises the following steps: identifying potential anti-key working procedures in an engineering project based on certain technical characteristics; dividing all working procedures of the project into a potential anti-key working procedure set X and a non-potential anti-key working procedure set Y; coding the execution modes of all the working procedures in the potential anti-key working procedure set X to generate an initial group of which the size is NP, and adopting a mode of fastest execution for all the working procedures in the non-potential anti-key working procedure set Y; calculating the total duration value corresponding to each single body in the group and converting the reciprocal of the total duration value into the adaptation value of the single body; adjusting the start time of non-key working procedures; selecting a parent, and producing a child by a single-point crossover operator and a single-point mutation operator; combining the parent and the child to form a new group; and if the maximum genetic algebra is obtained, stopping calculation and outputting an optimal solution, thus obtaining the optimal duration scheme of the project.
Owner:NORTH CHINA ELECTRIC POWER UNIV (BAODING)
Power electronic circuit optimization method based on particle swarm algorithm
InactiveCN101446997ASimple conceptEasy to implementBiological modelsSpecial data processing applicationsElectric power transmissionMutation operator
The demand for automation design of power electronic circuits becomes higher and higher with the development of the power electronic technology. In the invention, a particle swarm algorithm is applied to the design and optimization of the power electronic circuits, and the invention mainly relates to the power electronics field and the intelligent computation field. In the optimization method, an optimization process is divided into two parts by a decoupling technology to respectively optimize the power transmission of the power electronic circuit and a feedback network. Meanwhile, a mutation operator is introduced into the particle swarm algorithm to increase diversity of the swarm and improve the optimization efficiency of the algorithm. The optimization design of a buck converter is taken as an example for testing, which proves that the optimization method is very effective.
Owner:SUN YAT SEN UNIV
Who we serve
- R&D Engineer
- R&D Manager
- IP Professional
Why Patsnap Eureka
- Industry Leading Data Capabilities
- Powerful AI technology
- Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.
© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com