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210results about How to "Improve local search capabilities" patented technology
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Cooperative air combat firepower distribution method based on improved multi-target leapfrog algorithm
InactiveCN103425840AReduced survival rateReduce consumptionBiological modelsResourcesSelf adaptiveOperational effectiveness
The invention provides a cooperative air combat firepower distribution method based on an improved multi-target leapfrog algorithm and belongs to the technical field of computer simulation and method optimization. The method comprises the steps that firstly, required data information is obtained through a cooperative air combat formation command and control system; secondly, a multi-target optimization module of cooperative air combat firepower distribution is established; then a multi-target quantum leapfrog algorithm based on a self-adaptive mesh method is carried out, and a Pareto non-inferior solution of firepower distribution problem is solved; finally, rules can be selected independently according to the optimal distribution scheme, and the optimal firepower distribution scheme is selected from the non-inferior solution. The cooperative air combat firepower distribution method based on the improved multi-target leapfrog algorithm has the major functions that weapons on a fighter aircraft on an attack mission are distributed to multiple targets according to the optimal firepower distribution scheme, the formed cooperative air combat is enabled to realize the optimal cooperative attack effect, and the maximum operational effectiveness is obtained.
Owner:NORTHWESTERN POLYTECHNICAL UNIV
Reservoir dispatching method based on multi-target shuffled frog leaping and differential algorithms
ActiveCN105243458AImprove traversalIncrease randomnessForecastingBiological modelsMathematical modelDifferential algorithm
The invention discloses and proposes a reservoir dispatching method based on multi-target shuffled frog leaping and differential algorithms. The reservoir dispatching method comprises the following steps of S11, acquiring basic information data of a reservoir; S12, building a multi-target optimal dispatching mathematic model considering constraint conditions of water balance, machine set output, discharged volume and generation power, wherein the target function is built based on maximum generation power, minimum water deficit, minimum surplus water and minimum output; S13, figuring out Pareto optimal solution of the multi-target optimal dispatching mathematic model by using the multi-target shuffled frog leaping and differential algorithms; and S14, automatically determining a reservoir optimal dispatching scheme by a multi-target decision method on the basis of the Pareto optimal solution. By the reservoir dispatching method, global optimization is achieved, the calculation efficiency is improved, and the requirement of automatically selecting the multi-target optimal dispatching scheme of the reservoir is met.
Owner:HOHAI UNIV
Immune genetic algorithm for AUV (Autonomous Underwater Vehicle) real-time path planning
ActiveCN103077425AAvoid premature convergenceAchieve global optimizationGenetic modelsImmune genetic algorithmVaccination
The invention relates to a real-time path planning method of AUV (Autonomous Underwater Vehicle), in particular to a method for carrying out online, real-time local path planning according to an online map in an AUV real-time collision preventation process. The method comprises the steps of: setting the quantity of small populations according to the quantity of path points of the AUV, initializing; carrying out immune selection on each small population to obtain subgroups; carrying out genetic manipulation on one subgroup, carrying out cell cloning on the other subgroup; then clustering through a vaccination and an antibody to form the next generation of small population, judging whether the next generation of small population meets the conditions or not; if yes, selecting optimal individuals of the small populations; and selecting the optimal individuals from the set consisting of all optimal individuals to be used as a planning path. According to the invention, the diversity of the population is maintained by using an antibody clustering principle, the premature convergence of an algorithm is avoided, and the global optimization is facilitated. The established immune genetic algorithm is used for clustering and analyzing generated filial generations by adopting a self-regulating mechanism, and the diversity of the population is ensured.
Owner:SHENYANG INST OF AUTOMATION - CHINESE ACAD OF SCI
A cloud manufacturing resource configuration method based on an improved whale algorithm
PendingCN109902873AImprove local search capabilitiesImprove convergence accuracyForecastingBiological modelsPredationAlgorithm convergence
The invention discloses a method for cloud manufacturing resource optimization configuration based on an improved whale algorithm, and the method comprises the steps: building a problem model, and defining a fitness function; setting improved whale algorithm parameters, and generating an initial population; Calculating fitness values of all individuals in the population, obtaining a current optimal resource allocation scheme and converting the current optimal resource allocation scheme into whale individual position vectors; Introducing a parameter p, and judging whether p is less than or equal to 0.5; If not, performing spiral motion iteration updating to complete population updating; If yes, whether the value A (1) of the coefficient vector of the improved whale algorithm is met or not is judged; If yes, performing shrinkage encircling iteration updating; If not, performing random search predation iteration updating; Obtaining a current optimal resource configuration scheme; Adding 1to the number of iterations, and judging whether the current number of iterations is smaller than the maximum number of iterations; If yes, repeating the operation; And if not, outputting the currentoptimal resource configuration scheme. The whale algorithm is improved, so that the algorithm convergence speed is higher, the optimal solution is easier to achieve, and a new method is provided forsolving the problem of resource allocation.
Owner:CHANGAN UNIV
Multi-objective optimization method for hybrid active power filter
InactiveCN102916429AImprove compromiseIncrease diversityActive power filteringHarmonic reduction arrangementCouplingEngineering
The invention discloses a multi-objective optimization method for a hybrid active power filter. The method comprises the following steps: analyzing the relationship between all elements of an active leg and a passive leg of the hybrid active power filter and a system impedance, and the coupling relationship between all the elements of the active leg and the passive leg so as to obtain a first class constraint condition; acquiring a performance, cost and loss model of the filter; setting a second class constraint condition according to the specification of the filter; establishing an objective function base on the performance, cost and loss model and combining the first class constraint condition and the second class constraint condition to serve as a constraint condition of the objective function; processing the objective function and the constraint condition thereof to construct a new objective function; and obtaining an optimal solution based on a chaotic algorithm and a multi-objective PSO (Particle Swarm Optimization) algorithm based on Pareto optimal solution. Compared with an optimization algorithm with two objects, the optimization algorithm with three objects formed by establishing a three-dimensional optimization objective function based on the performance, the cost and the loss can enable the optimal solution to obtain a better tradeoff among the three aspects of the performance, the cost and the efficiency.
Owner:CENT SOUTH UNIV
Method for solving robot inverse kinematics based on particle swarm optimization algorithm
ActiveCN108932216ASolve the shortcomings of easy to fall into local optimumReduce the number of iterationsArtificial lifeComplex mathematical operationsLocal optimumInverse kinematics
The invention relates to a method for solving robot inverse kinematics based on a particle swarm optimization algorithm. The method comprises the following steps: 1) establishing a kinematic model according to joint parameters of a robot, obtaining a kinematic positive solution formula, setting positions of target points and an attitude matrix, determining a fitness function; 2) initializing a particle population, calculating an individual fitness value of an initial population, obtaining an individual optimal value and a global optimal value; 3) updating the position and speed of the particlepopulation using dynamic inertia weight which changes with the number of iterations, calculating a new fitness value, and determining whether to update; 4) performing genetic variation operation on the particle population, calculating the fitness value; 5) determining whether a termination condition is satisfied according to the fitness value and the number of iterations. Compared with the priorart, the method solves a shortcoming that a traditional PSO algorithm easily falls into local optimal values, and can enhance local search capability and improve convergence speed and precision by reducing the number of iterations.
Owner:SHANGHAI UNIV OF ENG SCI
A multi-parking-lot and multi-vehicle-type vehicle path scheduling control method
ActiveCN109919376AGive full play to the local search abilityEasy to crossForecastingLogisticsNeighborhood searchDelivery vehicle
The multi-parking-lot and multi-vehicle-type vehicle path scheduling control method comprises the steps of 1, establishing an objective function by taking the lowest total cost of all delivery vehicles as an objective; Step 2, performing a coding step; 3, performing population initialization; 4, evaluating all the individuals by adopting the objective function as a fitness function; Step 5, performing selection and crossover operation; step 6, performing mutation operation; 7, performing neighborhood search on each individual in the population by using an improved extreme value optimization algorithm; Step 8, calculating fitness of all individuals in the population; Step 9, performing selecting; Step 10, performing elite retention; Step 11, completing iteration in sequence; Step 12, judging whether a termination condition is met or not, the termination condition being that the number of iterations g reaches the maximum number of iterations MaxGen or the number of iterations Nu of whichthe Gb fitness value remains unchanged reaches the specified number of iterations Kbest, if yes, continuing to execute the step 13, and if not, returning to execute the step 5; Step 13, outputting the individual Gb and the fitness value fGb thereof; And 14, interpreting the optimal individual Gb and the fitness value fGb thereof. The invention aims to improve the search efficiency and convergencespeed of the algorithm.
Owner:ZHEJIANG UNIV OF TECH
A method for solving flexible job shop scheduling based on an improved whale algorithm
PendingCN109886588AImprove local search capabilitiesVerify validityBiological modelsChaos modelsMathematical modelOptimal scheduling
The invention discloses a method for solving flexible job shop scheduling based on an improved whale algorithm. The method comprises the following steps: 1) establishing a mathematical model of a flexible job shop scheduling problem; 2) setting algorithm parameters and generating an initial population; 3) obtaining a current optimal scheduling solution; 4) judging whether the current number of iterations is greater than the maximum number of iterations; if yes, outputting a scheduling solution; if not, judging whether the counter value of the current optimal individual is not smaller than a preset value or not; if yes, carrying out variable neighborhood search operation, and updating a scheduling solution; if not, converting the scheduling solution into a whale individual position vector,and retaining the whale individual corresponding to the scheduling solution; and 5) updating whale individual position information by adopting an improved whale algorithm, converting the whale individual position vector into a scheduling solution to complete population updating, adding 1 to the number of iterations, and returning to the step 3). According to the method disclosed by the invention,all optimal solutions of flexible job shop scheduling can be well solved, and the solving speed and precision are improved.
Owner:CHANGAN UNIV
Method for genetic algorithm with local modularity for community detecting
ActiveCN103208027AImprove local search capabilitiesNarrow down the candidate solution spaceGenetic modelsComplex networkModularity
The invention relates to a method for genetic algorithm with local modularity for community detecting and belongs to the technical field of complex network community mining. The method comprises the steps of encoding network community division; initializing populations; calculating fitness functions; performing genetic operation: crossing, mutation and selection; and performing decoding to obtain optimum community division. According to the genetic algorithm method, roulette selection is added in a crossing operator rather than individuals in the populations are selected randomly for crossing operation, so that the high-fitness individuals have priority selective properties, and generation of optimum division can be accelerated; a local modularity function is introduced in a mutation operator, so that a mutated candidate solutions is close to an optimal solution, the local search capacity of the mutation operator can be improved, the pertinency is achieved, and the search performance of the algorithm is improved; and a good division effect can be obtained when a genetic algorithm with local modularity for community detecting (LMGACD) is used for mining complex network communities, and the time complexity is low.
Owner:JIANGSU BOZHI SOFTWARE TECH CO LTD
Particle swarm improved algorithm based on chaotic backward learning
InactiveCN106447024AImprove local search capabilitiesImprove calculation accuracyArtificial lifeShortest distanceChaotic particle swarm optimization
The invention discloses a particle swarm improved algorithm based on chaotic backward learning. The algorithm mainly comprises a backward learning strategy and chaotic particle swarm optimization. The basic principle of the backward learning strategy includes: the backward learning strategy generates a corresponding backward solution for each initial candidate solution, and selects the solutions with short distance (high fitness) from two kinds of solutions (candidate solutions and corresponding backward solutions) as members of an initial population so that the convergence rate in an optimization process can be increased. In order to maintain the diversity of the population and enable individuals of the initial population to be uniformly distributed as much as possible, the initial population is generated by employing the backward learning strategy.
Owner:SHANGHAI DIANJI UNIV
Distributed photovoltaic power generation maximum consumption capability calculation system based on active power distribution network
The invention relates to a distributed photovoltaic power generation maximum consumption capability calculation system based on an active power distribution network. The system comprises an input module, an initialization module, a load flow calculation module, a particle swarm operation module and an output module; the input module is used to obtain a distribution network parameter, a photovoltaic power generation system parameter, a photovoltaic time sequence characteristic parameter, a load time sequence characteristic parameter, the number of various typical days in a year and an adaptive chaotic particle swarm algorithm parameter; the initialization module initializes a population by utilizing a chaotic algorithm, and each particle in the population represents a distributed photovoltaic access scheme; the load flow calculation module performs a load flow calculation considering active management and obtains adaptive values of the particles in a time section t; the particle swarm operation module performs a loop computation through adoption of an adaptive chaotic particle swarm algorithm and obtains a particle having an optimal total adaptive value; and the output module outputs an optimal distributed photovoltaic access scheme and the maximum year consumption amount under the distributed photovoltaic access scheme. Compared with the prior art, the system has the advantages of high calculation efficiency, high integration and the like, and is closely combined with the actual.
Owner:STATE GRID QINGHAI ELECTRIC POWER +2
Multi-objective quantum Shuffled Frog Leaping Algorithm (SFLA) based water resource optimization and diversion method
ActiveCN105809279AIncrease diversityAvoid getting stuck in local optimaForecastingSystems intergating technologiesWater resourcesDiversion program
The invention discloses a multi-objective quantum Shuffled Frog Leaping Algorithm (SFLA) based water resource optimization and diversion method, comprising the following steps: firstly obtaining basic information data on water resources; establishing multi-objective optimization and diversion models for water resources; executing the shuffled Frog Leaping Algorithm (SFLA) to find the best solutions to Pareto of the multi-objective optimization and diversion of water resources; and finally according to a multi-objective decision theory, choosing the best theory to divert water resources by combining objective weights and subjective weights. According to the invention, optimization is achieved through an overall choosing process. Calculation efficiency is increased so as to meet the requirements for best multi-objective diversion programs in a water resource system.
Owner:HOHAI UNIV
Two-dimensional irregular layout method based on multi-subpopulation particle swarm optimization
ActiveCN103336855AImprove search abilityGood nesting effectBiological modelsSpecial data processing applicationsGraphicsParticle swarm algorithm
A two-dimensional irregular layout method based on an improved particle swarm algorithm comprises the following steps of: converting geometric figures of a sample wafer and a material into a train of two-dimensional coordinate intervals, judging whether the two-dimensional intervals of the sample wafer and the material are overlapped with a heuristic bottom-left searching algorithm to move the position of the sample wafer relative to the material, and executing an improved PSO (Particle Swarm Optimization) searching process. According to the method of dividing multiple subpopulations, the influence of subpopulation optimal solutions on particles in the subpopulations is added under the condition of not changing a parameter trending to the current optimal solution, when the iteration time of a particle swarm reaches the initially set maximum iteration time, the particles stop iteration, and the current globally optimal solution is obtained as the final layout scheme. The two-dimensional irregular layout method based on the improved particle swarm algorithm has good searching capability, and is high in searching speed, good in final solution and good in layout effect.
Owner:ZHEJIANG UNIV OF TECH
A multi-dimensional optimization method for integrated energy system based on improved whale algorithm
InactiveCN109345005AImproved local search capabilitiesImprove local search capabilitiesForecastingArtificial lifeIntegrated energy systemSelf adaptive
The invention discloses a multi-dimensional optimization method of an integrated energy system based on an improved whale algorithm. The method models the integrated energy system, takes the minimum overall operation cost as an objective function, takes the balance conditions of electricity, heat and cold as a constraint condition, and adopts the improved whale algorithm to obtain the global optimal solution of the objective function. The improved whale algorithm is based on the traditional whale algorithm by introducing adaptive weights and Cauchy mutation. On the one hand, the invention improves the global searching ability of the whale algorithm and avoids falling into local optimization; On the other hand, the local searching ability is improved, the convergence speed and precision areimproved, and the stability is good.
Owner:CHINA ELECTRIC POWER RES INST +4
Multi-population simulated annealing hybrid genetic algorithm based on similarity expelling
InactiveCN104615869AMaintain diversityAvoid premature convergenceSpecial data processing applicationsMetapopulationComputer science
The invention relates to a multi-population simulated annealing hybrid genetic algorithm based on similarity expelling. The multi-population simulated annealing hybrid genetic algorithm includes the following steps: coding is carried out; initialization parameters are set; initial populations are created; fitness values are calculated; selecting operation is carried out; interlace operation is carried out; mutation operation is carried out; gene overturning operation is carried out; simulated annealing Metropolis rules are judged; migration operation based on similarity expelling is carried out; optimal storage is carried out; judgment is ended. The migration operation based on similarity expelling particularly includes the following steps: calculating the fitness values of individuals in a source population and a target population; selecting the individual with the largest fitness value from the source population to serve as the individual to be immigrated; conducting similarity calculation; conducting expelling replacement. The multi-population genetic algorithm with simulated annealing operation can improve the local search capability of the multi-population genetic algorithm, and the algorithm can search for approximate solutions even though optimal solutions to a larger extent. The individual similarity judgment is additionally carried out, attention is paid to differences between the individuals, the diversity of populations is maintained, premature convergence of the genetic algorithm is avoided, the solving quality of the algorithm is improved, and the algorithm is closer to the optimal solutions.
Owner:GUANGXI 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
Engineering optimization method of Harris hawks algorithm based on multi-strategy enhancement
PendingCN110728001AExcellent solutionHigh precisionGeometric CADArtificial lifeAlgorithmArtificial intelligence
Owner:WENZHOU UNIVERSITY
Flexible workshop robustness scheduling method based on decomposition multi-target evolution algorithm
The invention discloses a flexible workshop robustness scheduling method based on a decomposition multi-target evolution algorithm. The method comprises the following steps: 1, reading such input information as operation, machine attributes and the like of a flexible operation workshop, defining an optimization object, and setting constraint conditions; 2, initializing parameters of the algorithm; 3, determining an adjacent domain of each subproblem, generating an initial parent group, and determining all Pereto non-dominant solutions from the initial group so as to form an external memory; 4, generating a child group, carrying out mating selection, breeding child individuals by use of an adaptive variation operator and a restoration-based intersection operator, and updating the external memory; 5, by use of the generated child group, updating a current optimal individual of each subproblem, and forming a new parent group; and 6, when it is determined that the individual object evaluation frequency reaches the maximum, outputting the external memory, i.e., a group of Pareto non-dominant flexible operation workshop scheduling solutions, and if the frequency does not reach the maximum, skipping to the fourth step. According to the invention, scheduling tasks in a flexible operation workshop can be rapidly and efficiency realized.
Owner:NANJING UNIV OF INFORMATION SCI & TECH
Reactive power grid capacity configuration method for random inertia factor particle swarm optimization algorithm
ActiveCN104037776ARealize online static voltage support capability evaluationImprove local search capabilitiesForecastingSystems intergating technologiesCapacity provisioningPower grid
The invention discloses a reactive power grid capacity configuration method for a random inertia factor particle swarm optimization algorithm. The reactive power grid capacity configuration method includes steps that I, acquiring system parameters of a WAMS system in real time and setting particle boundary conditions; II, initializing a swarm and determining an adaptive value of the particle; III, dividing iterative stages; IV, updating the speed and position of the particle; V, judging whether the iteration times arrives at the maximum iteration times of a global search stage; VI, judging whether the iteration times arrives at the maximum iteration times of a primary solution stabilization stage; VII, judging whether the iteration times arrives at the upper iteration limit; VIII, iterating till arriving at the maximum times, and outputting an online reactive capacity configuration method. Compared with a standard algorithm and an adaptive mutation algorithm, the reactive power grid capacity configuration method for the random inertia factor particle swarm optimization algorithm enables the optimization precision to be improved and realizes to improve the early global search capability and the late local search precision based on guaranteeing a convergence rate through combining with actual situations of reactive optimization, and the global optimal solution is ultimately obtained.
Owner:STATE GRID CORP OF CHINA +2
Wireless sensor network node deployment method based on particle swarm optimization and mutation operator
ActiveCN107277830AIncrease profitImprove local search capabilitiesNetwork topologiesNetwork planningCoverage ratioParticle swarm algorithm
The invention provides a wireless sensor network node deployment method based on particle swarm optimization and a mutation operator. All sensors are randomly distributed in a target area, the initial coordinates of each sensor are taken as a particle initial position in a particle swarm algorithm, and the coverage rate of a wireless sensor network is taken as a target adaptive value function of the particle swarm algorithm. Through the particle swarm algorithm, a global historical optimal solution is obtained, an optimal value of a particle is obtained according to the optimal solution, and the optimal value is a best position of deploying a sensor in the target area. According to the method, through improving an inertia weight calculation method, the local search ability of the algorithm is improved, through giving a mutation probability to a particle individual, an individual historical optimal solution is recalculated, a condition that a whole algorithm falls into a local optimal solution is effectively avoided, the utilization rate of the sensors is improved, and the cost of constructing a wireless sensor network is reduced.
Owner:YANGZHOU UNIV
Flower pollination algorithm optimization method based on adaptive Gaussian mutation
InactiveCN106485314AImprove local search capabilitiesIncrease population diversityArtificial lifeGaussian mutationMutation
The invention discloses a flower pollination algorithm optimization method based on adaptive Gaussian mutation. Firstly, the population is firstly ranked according to adaptability values and is grouped, and then the position of the worst individual in each group is updated, thereby enhancing the local depth search ability of the algorithm and enhancing the population diversity; and secondly, whether the algorithm falls into the local optimization is dynamically monitored through a bulletin board, and if yes, a Gaussian mutation operation operator is automatically introduced, mutation operation is executed on the global optimal individual, thereby not only improving the ability of the individual jumping out of the local optimization but also enhancing the population diversity and quickening the convergence rate. The method of the invention has better stability, better reliability, quicker convergence rate and higher optimization precision.
Owner:CHANGSHU INSTITUTE OF TECHNOLOGY
Hybrid harmony search-based flexible job shop scheduling method
InactiveCN105512753AImprove local search capabilitiesForecastingManufacturing computing systemsHarmony searchTheoretical computer science
The invention relates to a hybrid harmony search-based flexible job shop scheduling method. The hybrid harmony search-based flexible job shop scheduling method comprises the following steps that: 1) solution vectors of FJSS (flexible job shop scheduling) to be solved are defined; 2) solution vectors in harmony search are converted into codes of feasible solutions of the FJSS; 3) a harmony search algorithm framework is adopted to perform harmony search, at firstly, a plurality of solution vectors are initialized randomly, and the maximum number of iterations is set, and at each iteration, a new solution vector is generated through harmony search; 4) the new solution vectors are converted into corresponding scheduling solutions, and the scheduling solutions are expressed by disjunctive graphs; 5) after the new scheduling solutions are generated, public key operation-based local search which is put forwards is executed with a certain probability based on the disjunctive graphs, and the disjunctive graphs are adjusted through the local search, so that improved scheduling solutions can be obtained, and then, the improved scheduling solutions are converted into solution vectors, and the obtained solution vectors are refined, and the refined solution vectors are adopted to replace the solution vectors before the local search, and the next iteration of harmony search is executed further; and 6) after the execution of the search is completed, an optimal solution vector is decoded, so that a final scheduling solution of the FJSS can be obtained.
Owner:TSINGHUA UNIV
Short-term wind speed prediction method, apparatus and system
InactiveCN107392397AImprove learning ability and generalization abilityImprove local search capabilitiesForecastingArtificial lifeMachine learningHybrid algorithm
The embodiment of the invention discloses a short-term wind speed prediction method, apparatus and system. The short-term wind speed prediction method includes the steps: acquiring the wind speed historical data which is preprocessed; using an experience modal decomposition method to decompose the wind speed historical data to obtain a plurality of data components; inputting each data component to a pre-established BP neural network optimization model to predict, and obtaining each prediction sub-result which is one to one corresponding to each data component; and superposing each prediction sub-result to obtain the wind speed prediction result, wherein the establishing process of the BP neural network optimization model includes the steps: acquiring the training sample data, and inputting the training sample data to the BP neural network; and performing searching optimization processing on the parameters of the BP neural network by means of a particle swarm gravitation searching mixed algorithm, and obtaining a trained BP neural network optimization model. The short-term wind speed prediction method, apparatus and system can reduce the influence of natural wind fluctuation on prediction accuracy during the using process, and can improve the global convergence accuracy to enable the wind speed prediction result to be relatively more accurate.
Owner:GUANGDONG UNIV OF TECH
Photovoltaic cell parameter identification method based on improved Harris Hawkoptimization algorithm
ActiveCN111506856AImprove recognition accuracyAccelerateArtificial lifePhotovoltaic energy generationBiologyFitness function
The invention relates to a photovoltaic cell parameter identification method based on an improved Harris Hawk optimization algorithm. The method specifically comprises the following steps: 1, collecting and processing output current and voltage data of a photovoltaic cell; 2, establishing an engineering model of the photovoltaic cell, and constructing a fitness function; 3, introducing a flexibledecreasing strategy to improve a prey initial energy decreasing mode; 4, optimizing the current population by using a gold sine method, introducing a golden section number, screening out individuals with optimal fitness from the golden section number, and establishing a new population for next iteration; 5, calculating a fitness value according to parameter information carried by individuals in the population; and stopping until the fitness value of the prey meets the precision requirement or reaches the maximum number of iterations, outputting the parameter information of the prey as the optimal parameter value of the photovoltaic cell, drawing a fitting curve and an algorithm iteration curve, and comparing the current and voltage experiment results of the photovoltaic cell with the modelidentification result. The method can replace a traditional photovoltaic cell identification method, and the identification precision of photovoltaic cell parameters is effectively improved.
Owner:YANSHAN UNIV
Network community division method based on simulated annealing genetic algorithm
InactiveCN102663499AThe value of the objective function is largeDivision to achieveGenetic modelsNODALGenetics algorithms
The present invention discloses a network community division method based on simulated annealing genetic algorithm, and mainly solves problems of poor search capability and low division efficiency in present genetic algorithm. The network community division method based on simulated annealing genetic algorithm comprises the following realizing steps: (1) reading in a network diagram; (2) generating an adjacent matrix according to the network diagram; (3) initializing genetic algorithm parameters; (4) decoding chromosomes and calculating objective function values; (5) selecting chromosomes with relatively large objective function values to form a parental population; (6) crossing and varying chromosomes and generating new chromosomes to form progeny populations; (7) initializing simulated annealing algorithm parameters and implementing a local search; (8) obtaining a next parental population and performing iteration; (9) determining whether or not the iterative algebra reaches the largest algebra Gmax; if the iterative algebra reaches Gmax, then the iteration is terminated and the chromosome with the largest objective function value is output, and division of every node in the output chromosome is a final division result of nodes in the community. The network community division method based on simulated annealing genetic algorithm has advantages of strong search capability and high accuracy.
Owner:XIDIAN UNIV
Method for improving salp swarm algorithm
PendingCN111027663AGive full play to the global search abilityAvoid local extremaArtificial lifePattern recognitionAlgorithm
The invention discloses a method for improving a salp swarm algorithm, and aims to improve the salp swarm algorithm to overcome the defects that the salp swarm algorithm cannot perform accurate searchin the later stage of iteration, is poor in population diversity and the like. By adding an attenuation factor, the search range is flexibly controlled and the algorithm convergence speed is increased, and by introducing a dynamic learning strategy, the assistance effect of a follower on optimization is enhanced, higher convergence precision of the algorithm is achieved, and the optimization performance of the salp swarm algorithm is improved. The convergence precision and the convergence speed of the improved salp swarm algorithm are greatly improved.
Owner:TIANJIN UNIV
Wireless sensor network clustering routing method based on harmony search algorithm
ActiveCN106550422AImprove energy efficiencyExtend the life cycleHigh level techniquesWireless communicationWireless mesh networkGlobal information
The invention discloses a wireless sensor network clustering routing method based on a harmony search algorithm. The method comprises the following steps: (1) global information transmission and convergence: acquiring the global information, and transmitting the global information to an aggregation node; (2) the aggregation node performs optimization calculation and configuration of network clustering routing of the entire sensor network through the global information; (3) a common sensor node monitors, collects and processes application information, processes the application information into a data packet, and forwards the data packet to a cluster head node, and the cluster head node performs data fusion and sends the data packet to a next hop cluster head node; and (4) the next hop cluster head node adds its own dump energy information to the data packet after receiving the data packet, and continues to forward the data packet to the next hop cluster head node according to the optical routing in the data packet. By adoption of the method disclosed by the invention, the problems of unreasonable member node allocation, uneven power consumption of the cluster head nodes of a wireless sensor network are solved, the energy efficiency of the network is improved, and the life cycle of the network is prolonged.
Owner:HUAZHONG UNIV OF SCI & TECH
Optimized-extreme-learning-machine-based detection system and method for blast furnace fault
ActiveCN106228184AQuality improvementImprove stabilityCharacter and pattern recognitionArtificial lifeLocal optimumLearning machine
The invention, which belongs to the technical field of blast furnace fault detection, provides an optimized-extreme-learning-machine-based detection system and method for a blast furnace fault. On the basis of an artificial bee colony (ABC) algorithm, an input weight and an implicit strata threshold value in an extreme learning machine (ELM) are optimized, thereby establishing a reasonable and effective classification model; on the basis of Tent mapping, an initial colony with uniform distribution is generated, thereby improving the quality of an initial solution and enhancing stability of the method; good balancing between global searching and local searching is realized by using an adaptive searching strategy; and jumping out of a local optimal solution is realized by using a Tent chaotic local searching strategy. According to the invention, the convergence speed and the optimizing searching precision of the ABC algorithm are improved; the classification precision of the blast furnace fault detection is enhanced; the probability of generating a new solution is increased; involvement in a local optimal solution is avoided; the rapid evolution direction of the algorithm is guaranteed; and the global searching capability is guaranteed and the local searching capability of the algorithm is also improved to a certain extent.
Owner:NORTHEASTERN UNIV LIAONING
RBF neural network-based coal and gas outburst prediction method
ActiveCN107194524AIncrease the differenceSmall lambdaForecastingArtificial lifePredictive methodsGlobal optimal
The invention discloses an RBF (Radial Basis Function) neural network-based coal and gas outburst prediction method. The method comprises the steps of 1, performing dimensionality reduction on feature data of coal and gas outburst, and performing normalization processing on the feature data subjected to the dimensionality reduction to obtain normalized feature data; 2, clustering the normalized feature data by using a K-mean algorithm and calculating the center of an RBF; 3, training an RBF neural network on the normalized feature data, and introducing an optimal expansion factor and an optimal weight when the number of neurons in a hidden layer is determined by an adaptive differential evolution algorithm; 4, increasing the number of the neurons in the hidden layer, and repeating the steps 2-3 to obtain global optimal parameters of the RBF neural network, thereby determining a prediction model of the RBF neural network; and 5, predicting test data by using the prediction model of the RBF neural network. According to the method, the optimal parameter adaptive problem of the RBF neural network can be solved, so that the coal and gas outburst can be quickly and accurately predicted.
Owner:HEFEI UNIV OF TECH
Large-scale symbol regression method and system based on adaptive parallel genetic algorithm
ActiveCN110135584AImprove search capabilitiesImprove local search capabilitiesGenetic algorithmsGenetic programming algorithmProcess module
The invention discloses a large-scale symbol regression method and system based on an adaptive parallel genetic algorithm, and the system comprises a main process module which is used for initializingand calling a CPU thread module and realizing a synchronization barrier and migration operation; a CPU thread module which is used for executing a genetic programming algorithm, realizing EV updatingand calling the GPU adaptive value evaluation module; and a GPU adaptive value evaluation module which comprises a CPU auxiliary thread, a CUDA library function and a CUDA self-defined function and is used for executing adaptive value evaluation. According to the invention, a self-adaptive multi-population evolution mechanism and a parallel computing system of heterogeneous computing resources are introduced into a genetic programming algorithm; effective construction elements are successfully extracted by applying an adaptive multi-population evolution mechanism, so that the performance of agenetic programming algorithm in a complex problem of the multi-construction elements is improved, and by designing a parallel computing system of heterogeneous computing resources, computing resources of a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit) are fully utilized, and the searching efficiency is remarkably improved.
Owner:SOUTH CHINA UNIV OF TECH
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