40 results about "Function optimization problems" patented technology

The invention relates to a disturbance-based elite reverse learning particle swarm optimization implementation method. The technical scheme of the method comprises a first step of initializing a particle parameter, a second step of calculating particle fitness values and obtaining individual extrema and a global extremum, a third step of progressively decreasing inertia weight in a nonlinear manner, wherein a nonlinear progressive decrease manner not a linear progressive decrease manner is used to change the inertia weight so that a convergence speed and convergence precision of an algorithm are improved, a fourth step of determining a particle position updating mode, a fifth step of updating the individual extrema and the global extremum, and a sixth step of determining a particle continuing execution condition. The method is suitable for solving a function optimization problem, and the method has the high convergence speed and the high convergence precision and can effectively prevent from falling into a local optimum.

The invention relates to a method for movie recommendation on the basis of an orthogonal and cluster pruning based improved multi-objective genetic algorithm. An improved algorithm OTNSGA-II is provided aiming at defects in distributivity and convergence of NSGA-II (non-dominated sorting genetic algorithm-II) and can be used for solving various multi-objective function optimization problems. By design of fault multi-objective orthogonal experiment initialization population, distributive deficiency caused by individual nonuniformity is avoided; by application of self-adaptive cluster pruning strategies, a population evolution process is maintained, and inferior individuals in an appropriate quantity are removed to keep convergence and distributivity of the population. By combination with information mining of user behaviors and movie properties, the algorithm is applied to solving of a practical problem of personalized movie recommendation, universality and effectiveness of the algorithm are explained by test comparison with existing algorithms, better recommendation results are obtained, recommendation accuracy rate, recall rate and coverage rate are increased, rich recommendation scheme combinations are provided, and interest points of users can be mined beneficially to provide more reliable recommendation services.

The invention relates to a high-dimensional multi-target set evolutionary optimization method based on preference of a decision maker. According to the method, the objective function of an original optimization problem is converted into an expectation function according to the preferential area of each target given by the decision maker; the expectation function optimization problem is converted into a two-target optimization problem with a set formed by multiple solutions of the original optimization problem as a new decision variable and the hypervolume and the satisfaction degree of the preference of the decision maker as a new objective function; an internal self-adaptive crossing strategy of individuals of the set is designed according to the hypervolume contribution degree of the solutions of the original optimization problem in the set and the satisfaction degree of the preference of the decision maker; furthermore, an individual variation strategy of the set is designed by means of the updating of particles in the PSO algorithm and the idea of a globally optimal solution and a locally optimal solution, so that a Pareto optimal solution set satisfying the preference of the decision maker and meeting the requirement for convergence and distributivity balance is obtained.

The invention discloses an RFID indoor positioning algorithm based on a dual-label array phase difference, and the method comprises the following steps: acquiring a phase difference at the same frequency by using double labels to form an array; processing phase data to eliminate phase ambiguity; calculating the distance difference between the label array and the multiple antennas through the relationship between the phase information and the distance, and obtaining multiple hyperbolic curves for hyperbolic positioning; considering the deflection angle of the dual-label array, transforming a positioning problem into a multi-dimensional function optimization problem by constructing an objective function; and solving a final positioning result by a genetic algorithm. The obtained result has asmall error, the positioning accuracy is high and the positioning effect is good.

The invention discloses an optimization model method based on a generative adversarial network and an application, called GAN-O, the method comprises the following steps: expressing the application (such as logistics distribution optimization) as a function optimization problem; establishing a function optimization model based on the generative adversarial network according to the test function and the test dimension of the function optimization problem, including constructing a generator and a discriminator based on the generative adversarial network; training a function optimization model; carrying out iterative computation by utilizing the trained function optimization model to obtain an optimal solution; therefore, the optimization solution based on the generative adversarial network is realized. According to the method, a better local optimal solution can be obtained in a shorter time, so that the training of the deep neural network is stable, and the method has more excellent local search capability. The method can be used for many application scenarios such as logistics distribution problems which can be converted into function optimization problems in reality, the application field is wide, a large number of actual problems can be solved, and the popularization and application value is high.

In order to improve the convergence speed and optimization accuracy of the cuckoo search algorithm for solving function optimization problems, a method for function optimization based on the improved cuckoo search algorithm is proposed, including: step 1, randomly generating n groups of function self within the specified range variable, and divide the function independent variable into the first part and the second part; step 2, for the first part, use the traditional cuckoo algorithm to update the value of the independent variable; step 3, for the second part, use a set of optimal independent variables found The variable value replaces all the independent variable values ​​in this part, and the cuckoo algorithm is used to update iterations based on the obtained current optimal function value to obtain a new optimal independent variable value. This method introduces a strategy of subgroup division and starting point selection into the traditional cuckoo search algorithm, which effectively improves the convergence speed and optimization accuracy of the algorithm without significantly increasing the computational complexity of the algorithm, thereby optimizing the function processing method.

The invention provides a sensor noise signal inhibition method in a three-point-method rotation error separation process. The method comprises steps that firstly, a sensor mounting angle is reasonably designed to carry out signal acquisition, and a sensor combination signal frequency domain expression in a three point method after noise signal interference is constructed; secondly, an error model of a real rotor contour signal and a contour signal acquired through separation after noise interference is established, and a transfer function of a noise signal in a separation process is acquired through analysis; lastly, an optimization target function is constructed according to the acquired transfer function, a harmony search algorithm based on coordinate rotation is further utilized to carry out optimization solution. According to the method, a sensor noise signal inhibition problem in the three-point-method rotation error separation process is converted into a target function optimization problem, each-order frequency components of the noise signal are effectively inhibited through optimizing the transfer function of the noise signal, and rotation error separation precision is improved.

The present invention discloses a hybrid cuckoo search algorithm. Two different one-dimensional update strategies are introduced into a cuckoo search algorithm. Further, by using the long jump characteristic of Levy distribution, the correct selection between Levy flight random walk and the one-dimensional update strategies is realized by setting a limit value. Thus, the hybrid cuckoo search algorithm not only overcomes inter-dimensional interference and is improved in local search ability, but also increases a probability of jumping out of local optimum, thereby achieving a balance between exploration and development. The hybrid cuckoo search algorithm can be effectively improved in convergence speed and convergence precision, reduces time complexity and have good convergence performancefor solving a function optimization problem.

Provided is a traffic signal timing optimization method based on principal component analysis and a local search improvement orthogonality genetic algorithm. The algorithm is provided by analyzing internal relation among the genetic algorithm, image processing and mode recognition and can be used for solving various function optimization problems. By means of the algorithm, an improvement orthogonality cross operator based on principal component analysis is provided. The operator first conducts PCA projection on the population before cross, individual length is reduced during cross, orthogonal cross operation is implemented on the projection area, the projection is projected to the original space after cross, redundant individual number and calculation expenses caused by redundancy are reduced, algorithm convergence speed is further improved, and the local search strategy is further introduced. The algorithm is applied to single-crossing signal timing optimization. By means of testing comparison with the existing algorithm, the method improves algorithm generality and efficiency, effective timing time is acquired, and the number of the queuing vehicles in front of a crossing is reduced.

An optimization task scheduling method in mobile edge computing comprises the steps: estimating the number of task migration failures according to historical data, modeling a task scheduling problem into an optimization problem about minimization of computing resource allocation variables and task scheduling variables, and converting the optimization problem into a set function optimization problem only about the task scheduling variables; obtaining a primary scheduling strategy suitable for all conditions according to the constructed linear approximation function of the target function, and further obtaining a secondary scheduling strategy according to the constructed sub-module approximation function of the target function when the computing power of the user is weaker than the computingpower of the server; and finally, obtaining an optimized task scheduling strategy through the primary scheduling strategy and the secondary scheduling strategy. According to the method, the delay ofthe computing task can be kept at a relatively low level when possible hardware and software faults occur.

The invention discloses a multi-target community discovering method integrating structure clustering and attributive classification. The method comprises the steps as follows: establishing a network adjacent matrix and an attribute matrix; establishing objective function modularity for measuring structure quality of community division; establishing objective function homogeneity for measuring attribute quality of the community division; initializing a network community division population; using cross and mutation operation to update the community division population; combining a mutated community division population and an external dominance population; finding all dominance community division in a final community division population. The method of the invention designs a function for balancing node attribute classification quality based on Shannon information entropy theory and models an attribute classification problem as an objective function optimization problem. A multi-objective optimization strategy is used to optimize a modularity function for balancing structure clustering quality and a homogeneity function for balancing attribute classification quality to obtain a group of community structures, which are suitable for different applications corresponding to different balances between structure clustering and attribute classification.

The invention relates to the technical field of non-intrusive load decomposition, in particular to a non-intrusive load decomposition method based on an improved differential evolution algorithm. A window function is designed for switching event detection and load steady-state operation time zone positioning. In order to solve the problem that the load identification rate of power load characteristics in a traditional NILD is not high, steady-state current harmonics of load equipment are selected as load identification characteristics. A load identification problem is converted into an objective function optimization problem, and optimization is performed based on a differential evolution algorithm (DE), and an improved DE algorithm of an adaptive cross factor is provided for solving the problems that a traditional DE is prone to falling into local optimization, and the convergence rate in the later period of evolution is low and the like. Experiments show that under different noise backgrounds, the load identification rate and the convergence rate of the improved DE algorithm based on steady-state current harmonics are obviously improved.

The invention discloses a directed sound field adjustment and control method based on wave beam deflection. The method comprises the following steps: 1) collecting sound field information of a controlled sound source, and using the collected information for sound source reconstruction; and 2) constructing an objective function on the basis of the idea of wave beam deflection, obtaining an adjustment and control weight vector through solving the objective function optimization problem, and realizing directed sound field adjustment and control after driving to an active control source. Accordingto the method, the controlled sound source is regarded as an emission sound source, and is combined with the active control sound source to form an "emission array", the active control source emits awave beam for driving to align a maximum recessed area of the wave beam to a to-be-suppressed sound field direction, thus maximum sound field cancellation under multi-row acoustic interference is realized in the direction, and the purpose of directed sound field adjustment and control is achieved. The technology has a potential application value in the fields such as directed low-frequency soundhiding.

The invention discloses a KM (Kermack-Mckendrich) infectious disease model-based function optimization method, i.e., an SIR (Susceptible-Infective-Removed) algorithm. Based on an KM infectious disease model, suppose that N biological individuals exist in a certain ecological system, and each biological individual is represented by n characteristics; the ecological system has an infectious disease and the infectious disease is infected among the N biological individuals; the biological individuals exchange information with one another through an infection operator, a pathological operator, a cure operator, an immune operator and an activity operator; the individual with high PPI (Population Physique Index) transfers information on strong characteristic to the individual with low PPI through the pathological operator and the immune operator, so that the individual with the low PPI index can grow towards a good direction; one individual acquires average characteristic information of some other individuals through the infection operator and the cure operator, so that the probability that the individual gets in locally optimal solution is reduced; the search scope is expanded by improving the activity of the individual through the activity operator; and the algorithm has the characteristics of strong search capacity and global convergence and provides a method for solving a complex function optimization problem.

The invention discloses a mobile user spectrum allocation method based on a cuckoo search algorithm. For the problem that in spectrum power allocation, network throughput is low, firstly, awareness based on an interference distance is built, and a spectrum power allocation problem is converted to a function optimization problem; maximized network throughput is converted to solving a total data volume completed in maximization available time, a target function is built, a spectrum allocation available is mapped to a position of a cuckoo nest, the cuckoo search algorithm is adopted for solving,finally, spectrum allocation with the network throughput higher than that of a genetic algorithm is obtained, and high secondary user effective channel capacity can be obtained.

The invention provides a population survival dynamics optimization method under environment pollution, namely a PSDO-EP algorithm. A population survival dynamics theory under environment pollution is used, an environment system and the solution space of an optimization problem correspond to each other, pollution phenomena exist in the environment system, a plurality of populations live in the environment system, each population corresponds to a trial solution of the optimization problem, one feature of the population corresponds to a variable in the trial solution, the populations change all the time under the effect of environment pollution, strong populations which can resist environment pollution grow, weak populations stop growing, a population survival dynamics model under environment pollution is used for constructing evolution operators and achieving information interchange between environment and the populations and among the populations, during a population evolution process, the populations convert from one growing state to another growing state, searching on the optimal solution of the optimization problem of the populations is achieved, the PSDO-EP algorithm has the advantages of being strong in searching capacity, global convergence is achieved, and a solution is provided for the complex function optimization problem.

The invention discloses a client caching method and system based on a submodule optimization algorithm. The method comprises the following steps: after inputting an access unit by a client, sub-moduleoptimization algorithm processing is carried out to judge whether the access unit needs to be cached or not, an access unit set needing to be cached forms a batch input set in a to-be-cached input set. The caching space is updated according to the data of the batch input set, a cache management mechanism is set on the basis of a three-layer index management unit, a series of operation operators oriented to scenes such as file fragment overlapping, covering and crossing are formulated, and cache units in a complex access mode can be efficiently managed. According to the model, a cache problemis abstracted into a sub-module function optimization problem, the sub-module optimization algorithm is applied to a cache migration strategy, the model provides a synchronous / asynchronous cache replacement / lifting strategy for different application program running modes, and in addition, the model comprises multiple system optimization methods, so that the storage and network communication performance of a client cache is optimized.

The invention discloses a path optimization control method and device for a liquid metal battery pack, and belongs to the technical field of liquid metal battery application, and the method comprises the following steps: S1, building a graph theory model corresponding to a battery module composed of a plurality of liquid metal batteries; s2, designing a double-layer equalization topology corresponding to an inductor and a multi-winding transformer in the graph theory model; s3, calculating the equalization efficiency and the equalization speed of the graph theory model by utilizing the equalization parameter of each section of path in the double-layer equalization topological graph theory model, and taking the equalization efficiency and the equalization speed as constraint conditions; s4, taking the index function of the circuit loss and the equalization time added with the weight as a target function to establish an equalization path optimization model; and S5, solving the equilibrium path optimization model by using an ant colony algorithm to obtain an optimal equilibrium path meeting the objective function and the constraint condition. According to the invention, the battery equalization problem is converted into a function optimization problem with constraint conditions, the optimal equalization path is obtained by using the ant colony algorithm, and the equalization efficiency and the equalization speed can be improved.

The invention discloses an improved particle swarm parameter identification method for a boiler bed temperature system time delay nonlinear model. The method comprises the following steps: constructing a boiler bed temperature system Hammerstein-Wiener time delay nonlinear model; obtaining an identification model of a boiler bed temperature system Hammerstein-Wiener time delay nonlinear model; constructing an improved particle swarm optimization search method, converting an identification problem of a nonlinear system into a function optimization problem in a parameter space, achieving simultaneous estimation of all parameters by using a parallel search capability of particle swarm optimization, and finally, separating linear and nonlinear parameters and time delay. The method also constructs a process and steps of the improved particle swarm iterative identification method, and can be effectively applied to parameter estimation of a boiler bed temperature system Hammerstein-Wiener time delay nonlinear model. The method has a certain engineering practical value.

Provided is a traffic signal timing optimization method based on a principal component analysis improvement genetic algorithm. The algorithm is provided by analyzing internal relation among the genetic algorithm, image processing and model recognition and can be used for solving various function optimization problems. By means of the algorithm, principal component analysis is conducted on population individuals to analyze design cross and a mutation operator. The mutation operator can avoid the cross position where ineffective cross may be generated easily according to similar genes of parent individuals counted by PCA, useless cross is reduced, and algorithm search efficiency is improved. The mutation operator conducts self-adaptation mutation probability adjustment according to the similar genes counted by PCA to protect the good mode and improve the local research efficiency of the algorithm. The algorithm is applied to single-crossing signal timing optimization. By means of testing comparison with the existing algorithm, the method improves algorithm generality and efficiency, effective timing time is acquired, and the number of the queuing vehicles in front of a crossing is reduced.

The invention discloses a method for obtaining interference alignment precoding based on a genetic algorithm, which comprises the following steps: coding each group of precoding matrices and interference suppression matrices of a receiving end in a sequence number coding mode; initializing the precoding matrices and the interference suppression matrices; adopting a fitness function to evaluate thefunction value of the fitness of each chromosome; adopting a roulette mode to randomly select some chromosomes except the chromosome with the highest fitness function value to form a new population;adding the chromosome with the highest fitness function value to the new population; performing cross combination on the two chromosomes which are randomly selected in the new population by adopting adual-daughter-chromosome amphiphilic cross method; carrying out a mutation operation on each bit of each chromosome in the new population by using a variation probability; judging whether the function value of the fitness meets a termination condition or not; and decoding and outputting the precoding matrices and the interference suppression matrices when the termination condition is met. According to the method, a nonlinear multi-objective function optimization problem is solved through the genetic algorithm.

The invention relates to a network topology optimal design method in consideration of business process features. The method comprises the following steps: step one, determining network information according to an engineering application demand, wherein the network information comprises network node amount, network business information, and other information required by the engineering application;step two, establishing a network topology optimal design model in consideration of the business; regarding the network topology design problem in consideration of the business as a target function optimization problem with a constraint condition, wherein the constraint condition is determined according to engineering actual condition, an optimal target is the loaded network performance index of the specific business, and the solution is the optimal network topology structure; and step three: solving the optimum network topology based on the genetic algorithm. The method disclosed by the invention has the advantages: (1) the business process features can be considered while supporting the network topology design, and the network topology with the optimum performance can be obtained; and (2) the acquired network topology in consideration of the business process features can improve the business reliability in the network operation, and has important engineering significance.

Disclosed is a population dynamics optimization method with vertical-structure nutrition chains. The population dynamics optimization method with the vertical-structure nutrition chains is a PDO-NCVS algorithm. A solution space of an optimization problem is regarded as an ecosystem which has three vertical-structure nutrition chain types of an opened nutrition chain, a closed nutrition chain and a branch nutrition chain, the ecosystem is divided into a plurality of different sub-systems, and each sub-system has a specific vertical-structure nutrition chain type. For each sub-system, a plurality of populations live in each sub-system. The populations can not be transmitted among the sub-systems, and information transfer exists in the kindred populations among the sub-systems which have the same vertical-structure nutrition chain types. The populations living in a sub-system are connected in a predator-prey circulating mode or in a resource-consumption circulating mode. Behaviors during a population acts in a sub-system are constructed into evolution operators which are used for constructing evolution strategies of the populations. The algorithm has the advantages of being strong in search capability and having global convergence, and a solution scheme is provided for the solution of a complex function optimization problem.

The invention provides a sensor noise signal inhibition method in a three-point-method rotation error separation process. The method comprises steps that firstly, a sensor mounting angle is reasonably designed to carry out signal acquisition, and a sensor combination signal frequency domain expression in a three point method after noise signal interference is constructed; secondly, an error model of a real rotor contour signal and a contour signal acquired through separation after noise interference is established, and a transfer function of a noise signal in a separation process is acquired through analysis; lastly, an optimization target function is constructed according to the acquired transfer function, a harmony search algorithm based on coordinate rotation is further utilized to carry out optimization solution. According to the method, a sensor noise signal inhibition problem in the three-point-method rotation error separation process is converted into a target function optimization problem, each-order frequency components of the noise signal are effectively inhibited through optimizing the transfer function of the noise signal, and rotation error separation precision is improved.

The present invention is a polymetallic open-pit mine production operation plan preparation method based on the improved gray wolf algorithm. On the basis of the existing production operation plan, the allowable fluctuation range of the quality index of the selected ore is regarded as a constraint condition, and at the same time, the Based on the grade constraints of polymetallic components, a production operation planning model aimed at minimizing ore mining and transportation costs was established, and then the improved gray wolf algorithm was used to solve the model; compared with the original solution method, gray wolf The algorithm has the advantages of high solution accuracy and fast convergence speed, and is very suitable for solving complex function optimization problems under multi-constraint conditions. In the process of solving the optimization model of the polymetallic open-pit mine production operation plan, the operation plan that meets the actual production needs can be quickly obtained. The invention has important guiding significance for improving the utilization rate of mined ores, stabilizing the grade level of polymetallic open-pit mines, and improving the economic benefits of mining enterprises.

The invention relates to the technical field of micro inertial measurement device parameter calibration, discloses an MEMS accelerometer turntable-free calibration method based on an improved fruit fly optimization algorithm, and mainly aims to solve the problem of accelerometer parameter turntable-free calibration by applying an improved swarm intelligence optimization algorithm. An accelerometeroutput model is established according to an MEMS accelerometer error form and a defined coordinate system, and an accelerometer input and output equation is established through multi-position staticobservation. A nonlinear equation set solving problem containing accelerometer calibration parameters is converted into a nonlinear function optimization problem by utilizing a modular observation principle. Directed at the defects that only positive parameters can be searched and the search step length is fixed in a classic fruit fly optimization algorithm, a taste concentration judgment value and the search step length are improved, so that the improved fruit fly optimization algorithm has two properties of global parameter search and variable step length. And the improved fruit fly optimization algorithm is applied to a nonlinear function containing to-be-calibrated parameters of the accelerometer, and optimization solution is carried out on the to-be-calibrated parameters.

Provided is a traffic signal timing optimization method based on a principal component analysis improvement genetic algorithm. The algorithm is provided by analyzing internal relation among the genetic algorithm, image processing and model recognition and can be used for solving various function optimization problems. By means of the algorithm, principal component analysis is conducted on population individuals to analyze design cross and a mutation operator. The mutation operator can avoid the cross position where ineffective cross may be generated easily according to similar genes of parent individuals counted by PCA, useless cross is reduced, and algorithm search efficiency is improved. The mutation operator conducts self-adaptation mutation probability adjustment according to the similar genes counted by PCA to protect the good mode and improve the local research efficiency of the algorithm. The algorithm is applied to single-crossing signal timing optimization. By means of testing comparison with the existing algorithm, the method improves algorithm generality and efficiency, effective timing time is acquired, and the number of the queuing vehicles in front of a crossing is reduced.

A population competition dynamics optimization method with horizontal nutrition structures is a PCDO-HNS algorithm. A solution space of an optimization problem is regarded as an ecosystem which has a plurality of different sub-systems, each sub-system has a specific horizontal nutrition structure type which is one of a normal distribution type, a discrete distribution type, a nearest type, an even type and a monotone decreasing type, populations living in each sub-system mutually compete, and mutual learning behaviors, mutual influence behaviors, mutation occurring behaviors and close keeping behaviors also exist in an accompanying mode. A population competition dynamics model with the horizontal nutrition structures are used for constructing five population evolution operators of the normal distribution type, the discrete distribution type, the nearest type, the even type and the monotone decreasing type, and the operators are used for constructing evolution strategies of the populations. The algorithm has the advantages of being strong in search capability and having global convergence, and a solution scheme is provided for the solution of a complex function optimization problem.