150results about How to "Reduce optimization time" patented technology

Provided is a self-optimizing computer system that can achieve ultimate optimization (improvement in the speed) by preparing a mechanism that can observe the behavior of the program execution in the self-optimizing computer system and optimize dynamically depending on the execution behavior of program. The self-optimizing computer system comprising multiple processing units, characterized in that each of the processing units operates as at least one of an operation processing unit for executing a program, an observation processing unit for observing the behavior of the program under execution, an optimization processing unit for performing an optimization process according to the observation result of the observation processing unit, and a resource management processing unit for performing a resource management process of whole of the system such as a change of the contents of execution.

The invention relates to an optimized design method for modelling of an end wall of a high load fan/compressor. The method comprises eleven steps. As the stage pneumatic load is improved, internal flow of the fan/compressor is more severe, massive secondary flow is easily generated in an area close to the end wall, and a risk of flow stall exists. The modelling of the end wall has important influence on control over the secondary flow in an end area of the high load compressor, and becomes an important research direction. Firstly, an end wall modelling parameter defining method is researched; secondarily, based on an adaptive genetic algorithm and an artificial neural network response surface model, which are developed by the research group, an orthogonal experimental design, end wall parameter definition and a flow field value simulating technology are combined to realize automatic global optimization of the end wall of the compressor; thirdly, the effectiveness of the optimizing method is verified through different test functions; finally, a blade grid of the compressor is subjected to nonaxisymmetrical optimization based on the method. A result shows that the blade grid after modelling has good pneumatic performance.

The invention discloses a side-lobe suppression method for a three-dimensional array antenna pattern based on an improved MOPSO and a convex optimization algorithm. Firstly, three-dimensional array antenna pattern synthesis is modeled and analyzed, and a basic mathematic model of the array antenna radiation pattern synthesis is given, and multi-objective functions of low side-lobe pattern synthesis under constraint conditions of an array radiation direction and first side-lobe suppression are constructed respectively; then, the maximal excitation of a polarization directivity coefficient is obtained by a Lagrange multiplier method and is added in the MOPSO algorithm as preference information, an initial population is generated in the vicinity of optimal solutions, and the model is solved by iterative calculation; a convex optimization model of the low side-lobe pattern synthesis is constructed, a convex optimization tool is utilized to solve the corresponding optimal solutions under different threshold constraints, then the three-dimensional array antenna pattern under the low side-lobe constraint is obtained, and therefore the purpose of side-lobe suppression is achieved. According to the side-lobe suppression method for the three-dimensional array antenna pattern based on the improved MOPSO and the convex optimization algorithm, the optimization performance of the algorithms is improved, and the side-lobe suppression problem of the three-dimensional array antenna pattern can be solved efficiently and quickly.

The invention discloses an MISO system downlink secrecy rate optimization method by means of an intelligent reflection surface. The MISO system downlink secrecy rate optimization method comprises thefollowing steps of: acquiring channel state information of legal users and eavesdroppers; fixing an intelligent reflection surface phase offset matrix, and calculating an optimal base station side beam-forming vector; fixing the optimized beam-forming vector to obtain a local optimal solution of the intelligent reflection surface phase offset matrix; and repeating the alternating optimization process until convergence, and outputting a beam-forming vector and a phase offset matrix obtained by the last iteration. By jointly designing the base station side beam-forming vector and the intelligentreflection surface phase offset matrix, the system secrecy rate is maximized, the calculation complexity can be effectively reduced, the number of iterations can be reduced, the operation time is saved, and the method has practicability and high efficiency in a multi-antenna secrecy transmission system.

The invention relates to a flexible job-shop scheduling optimization method, which applies the Metropolis criterion and the sinusoidal adaptive step length to a firefly algorithm so as to optimize andsolve a discrete problem. On the basis of building a mathematical model, an initial solution population of a discrete combination problem is randomly generated, then local search in an individual domain is performed according to the Metropolis criterion in simulated annealing to generate a new individual, the internal energy difference between the new individual and the original individual is calculated, the new individual is accepted according to a certain probability, and global search is performed on each generation by using the discrete firefly algorithm with the sinusoidal adaptive steplength until an optimal solution is searched. The method can better search an optimal solution of the FJSP (Flexible Job-Shop Scheduling Problem) in the global space and has better search precision, search efficiency and stability, thereby having important significance and significant engineering practical application values for solving discrete problems such as job-shop scheduling.

The invention relates to a dynamic response topological optimization method implemented by application of improved bi-directional evolutionary structural optimization (BESO) to an equivalent static load method. In the method, improved BESO is applied to the equivalent static load method to form an optimized iteration method for replacing an original variable density method, and establish a new convergence condition in order to improve a dynamic response topological optimization flow. By adopting the method, optimization time of a design circulation stage of which k is greater than 1 can be reduced effectively, and CPU (Central Processing Unit) total time needed by optimization in two examples is reduced by 21.8 percent and 25.3 percent compared with an original method; the method has the characteristics of high computation speed and simple optimization flow; along with the increase of external load acting points and the expansion of the optimization scale, design circulation times needed by optimization are increased, and the method has greater advantages.

The invention provides an electromechanical cooperative design method of a skin antenna based on parallel Bayesian optimization in order to reduce calculation amount, improve design quality and optimize efficiency. Determine the design variables and initial design space, carry out initial sampling, extract all sample points in the sample point database and their corresponding response values, select Bayesian algorithm to construct the surrogate model of the objective function, use the pseudo-expectation enhancement criterion as the collection function to select multiple candidate solutions, and judge the degree of satisfaction of the constraint conditions. If the number of calculation times of the electromechanical coupling model reaches the set number or satisfies the convergence criterion, the iteration is stopped and the optimal solution is outputted. A large amount of optimization time is saved for complex problems, and only one sample point is randomly selected for initial sampling, so the optimization process is repeatable.

The invention relates to the technical field of wireless mobile communication, specifically to a dual band network automatic telephone traffic and quality balancing method based on measurement report, which is characterized in that the method optimizes network parameters by a configuring data processing sub-system, an interference mode building sub-system, a basic data management sub-system, a network analyzing sub-system, an antenna feeder parameter optimizing sub-system, a re-chosen and switched parameter optimizing sub-system, a neighboring area optimizing sub-system and a network property assessing sub-system. The method can reflect network condition truly along with high working efficiency, reduce cost of repeatedly ''optimizing-assessing-optimizing'', shorten optimizing time, reduce workload, evenly consider all cells in the optimized zone, automatically balance all parameters and guarantee the optimized result. The method provides a total solution, so the total performance of the local area wireless network is the best in optimizing RF parameters, optimizing the re-chosen and switched parameters as well as in optimizing neighboring areas.

The invention discloses a dynamic scheduling method of a track type automatic guiding vehicle based on segmentation optimization of a genetic algorithm. The method comprises the following steps: performing segmentation optimization on a parking sequence of the track type automatic guiding vehicle at various parkable positions according to state parameters of numerical control machine tools and preset processing parameters; encoding: encoding the parking positions of the track type automatic guiding vehicle in the future N times into a gene string with a length N; decoding: using a ratio of a workpiece number completed in a process when the track type automatic guiding vehicle moves for N steps to the consumed time as gene string fitness; and performing crossover, mutation and retention ofelite gene string operations on the genetic algorithm, obtaining an optimal gene string through multiple rounds of evolution, and using the optimal gene string as the movement mode of the track type automatic guiding vehicle in the future N times. The algorithm re-optimizes the movement trajectory of the RGV in the future N times when finding that the CNC is faulty or restores from the fault, so that an RGV scheduling scheme can be automatically adjusted according to the change of the system parameters, and CNC random faults and fault restoration can be adaptive.

The invention relates to a method for optimizing a mask layout pattern comprising at least one structural feature. First a desired layout pattern is provided. Based on the desired layout pattern, an optimized reference diffraction coefficient is provided. After selecting an initial mask geometry having polygon-shaped structures, initial diffraction coefficients are calculated. A difference based on the reference diffraction coefficient and initial diffraction coefficients is used to optimize the initial geometry in order to provide a mask layout pattern.

The invention provides a composite energy storage system energy optimization and allocation method of an electric automobile and belongs to the field of composite energy storage system energy allocation. The problem that an existing composite energy storage system energy allocation method is long in operating time and poor in allocation effect. Firstly, a convex optimization method is adopted to generate a comparison table of the total output powder of an electric automobile after optimization and the output power of a super-capacitor bank; secondly, a two-dimensional array is established according to the comparison table, a whole automobile controller calculates the total toque required by the electric automobile in action operation according to the acquired speed of the electric automobile, front and rear motor rotation speeds and the opening degree of an accelerator pedal and calculates the real-time required powder Pd of the electric automobile according to the required total torque and the speed of the electric automobile; finally, the real-time output powder Psc of the super-capacitor bank is looked up from the two-dimensional array according to the real-time required powder Pd; according to the real-time required powder Pd, a two-way DC/DC converter is controlled so as to complete the energy allocation of the electric automobile. The composite energy storage system energy optimization and allocation method is used for energy allocation.

The invention provides a method for economic dispatch of virtual power plant based on distributed gradient algorithm, comprising the following steps: obtaining data information of adjacent energy sources through distributed scheduling, combining a gradient algorithm for iterations and updating, satisfying both equality constraint and inequality constraint, and performing power exchange with a main power grid. With the method, it is possible to reduce the erection and maintenance costs of communication lines. In addition to that, with small storage capacity and simple operation, optimization time can be considerably shortened and communication costs are reduced.

A method of optimising a sequential combinatorial process comprising an interchangeable sequence of events comprises using a master model to model a selection of the possible sequences, and using information derived from the master model in a surrogate model that approximates the master model with a much shorter computation time. The surrogate model calculates all the possible sequences using an algorithm to select from the information calculated by the master model that which most closely matches the events of a present sequence, following a prioritised system so that the best match is used wherever possible. All results from the surrogate model are compared so that the modelled sequence that gives the result closest to a desired optimum result for the process can be identified, and potentially applied to the process. Accuracy can be enhanced by running the optimum sequence through the master model as a check, and further by adding the optimum sequence to the information used by the surrogate model in future calculations. Any sequential combinatorial process, in which the quality of the end result of the process depends on the order in which events in the process are performed, can be optimised in this way, including manufacturing process such as machining, cutting, shaping, forming and/or heat treating a workpiece, flow of material through a factory or oil or gas through a pipeline network, chemical and material science mixing processes, computational biology modelling, and fleet management.

The invention discloses a building multi-performance oriented design optimization method, which comprises the steps of 1, determining a building design decision target and potential influence factorsthereof as an optimization target and an optimization variable respectively, and defining constraint conditions so as to construct an optimization model; 2, obtaining a building performance sample library with statistical representativeness, wherein the step comprises construction of an optimization variable sample matrix, setting of a building physical model, construction of a building performance output matrix and construction of a building multi-dimensional input and multi-dimensional output sample matrix; 3, constructing a simplified relation model between the optimization variable and theoptimization target by adopting a machine learning algorithm, and taking the simplified relation model as an agent model; and 4, taking the agent model as a fitness function of an optimization algorithm, executing building multi-objective optimization, and obtaining a Pareto optimization result. According to the method, the multi-objective optimization process of the building performance can be more scientific and standard, calling of EnergyPlus software in the optimization process is reduced through the coupling agent model and the multi-objective genetic algorithm, and the optimization timeis shortened.

The invention provides a milling spindle speed sinusoidal modulation parameter optimization method, comprising the following steps: carrying out dynamics modeling of a variable-speed milling system, and establishing a variable-delay second-order differential dynamics equation; establishing a mapping relationship between a spindle speed sinusoidal modulation parameter and a delay variable, and calculating out the delay; carrying out state space transformation of the dynamics equation to get a transformed state space equation; dispersing the state space equation at variable step in two adjacent Floquet cycles; determining the stability of the milling system through a variable-step numerical integration approach; establishing a constraint optimization model by taking maximum milling efficiency as the goal and chatter-free milling and spindle speed change limit as constraints; and using an intelligent optimization algorithm to get an optimized variable-speed milling sinusoidal modulation parameter. By using the optimized sinusoidal modulation parameter in variable-speed milling, the efficiency of milling is improved greatly.

A method for designing a ship body form line based on sensitivity comprises the first step of carrying out three-dimensional modeling of the ship body form line, namely, a ship body curved surface is modified based on a ship body Non-Uniform Rat ional B-spline expression and the radial basic function interplation technology; the second step of carrying out forward calculating, namely, the performance index of an initial ship type of a transformed ship body curved surface is worked out through CFD software; the third step of carrying out reverse predicating, namely, all control points of an initial parent type are sequentially disturbed, CFD calculation is carried out on each disturbed ship type, and the corresponding performance index is worked out; the fourth step of detecting the stopping condition, namely, the first step is executed repeatedly if the obtained performance index cannot meet the stopping condition, iteration is carried out repeatedly until the difference of the performance index of a new ship type and a set value is smaller than a convergence value or the set times of iterations are achieved, and the final ship type is the required optimal ship type.

The invention discloses a method for accelerating operating speed of a GPU through dead code removal. By the aid of the method, the implementation and compiling efficiency of a large-scale GPU kernel program can be improved. The technical scheme includes that firstly, a state detection table is established for all functions in the large-scale GPU kernel program; basic information of functions is recorded, and the state detection table is initialized; static analysis is conducted for the GPU program; and then the GPU kernel program is operated, information during operation of the GPU kernel program is recorded, states of all function detection table fields in the state detection table are updated, dead codes are marked and finally certified, and dead codes are deleted according to a dead code set D which is obtained finally. According to the method, dead codes which are not implemented during operation are removed, so that the code size of the GPU kernel program is reduced, the assembly code size which is generated finally is also reduced, the hit rate of single instruction multiple data (SIMD) instruction scheduling in the GPU can be improved, and the operation efficiency of the large-scale GPU kernel program can be greatly improved.

The invention discloses a swarm intelligence optimization method for dynamic hydraulic balance of a chilled water pipe network of a central air conditioner. The method is characterized in that intelligent computing nodes CPNs are set in air conditioning cabinets, bypass valves and water pump equipment in the chilled water pipe network; all the intelligent computing nodes CPNs are interconnected according to an actual physical topological connection relationship to form a network communication system of a swarm intelligence architecture; corresponding distributed computation is completed according to the adjustment task, and finally the dynamic hydraulic balance of the chilled water pipe network is achieved with the minimum system total power; the intelligent computing nodes CPNs of the airconditioning cabinets compute through information interaction to obtain the minimum system water supply and return pressure difference and the total flow meeting the flow requirements of all the airconditioning cabinets at the tail end of the system, and compute to obtain the opening degrees of water valves of surface air coolers of the air conditioning cabinets; and the intelligent calculatingnodes CPNs of water pumps compute to obtain an optimal operation strategy of a parallel water pump system according to the obtained system water supply and return pressure difference and the total flow requirement, and finally the purposes of the optimization of the dynamic hydraulic balance of the chilled water pipe network, the energy conservation and the consumption reduction are achieved.

The invention relates to a configuration optimization method of a double-planet-row power split hybrid power system, which comprises the following steps of generating a configuration library accordingto a clutch constraint condition and a possible connection relationship between components; removing configurations which do not meet configuration constraint conditions, and establishing a general energy management strategy of each configuration; obtaining a preliminary optimization configuration library based on a longicorn beard search algorithm; establishing a candidate scheme set screening module; obtaining an optimized configuration of each preliminary optimized configuration based on a particle swarm optimization algorithm and a candidate scheme set screening module; and screening based on the candidate scheme set screening module to obtain an optimal configuration. Compared with the prior art, all feasible configurations of the double planet rows are comprehensively and optimallydesigned, and engineering requirements of different user design objectives can be met; the comprehensive performance evaluation indexes of the economy, the dynamic property, the comfort and the reliability of the configuration are used as an optimization objective function, and the particle swarm optimization algorithm is combined to realize the quick optimization of the optimal scheme of the comprehensive performance of the configuration.

The invention relates to an optimization method for distance between headstock bearings of large ship used crankshaft lathe, comprising the following steps: using PRO/ENGINEER to carry out the three-dimension geometric modeling; entering PRO/MECHANICA submodule of PRO/ENGINEER to definite the physical attribute of the model, the boundary condition and the load; carrying out the meshing to the model; carrying out the finite element simulation analysis; changing the distance between mainshaft bearings and the like to figure out the finite element in order to obtain analysis results; and comparing the analysis results to obtain the best distance between bearings in order to finally increasing the precision of crankshaft lathes. The method mainly uses PRO/ENGINEER and PRO/MECHANICA submodule thereof to carry out the three-dimension modeling, and carries out the finite element analysis under the seamless connecting condition. Because of using P method to deal with finite cells, the headstock can be automatically folded for stimulating the integrated transformation of the headstock of the large ship used crankshaft lathe with high-precision, and the time for optimization can be greatly shortened.

The invention discloses a rapid optimization method for the operating performance of a Linux system. The rapid optimization method includes the steps that a current-performance-state information value of a system is obtained; according to the matching rule of a preset performance-state information value and a corresponding operating condition, the current-performance-state information value is matched, and a current operating condition of the system is determined; the system is subjected to performance optimization operation corresponding to the current operating condition. According to the rapid optimization method, after the current-performance-state information value of the system is obtained, the current operating condition solely matched with the system is determined according to the established rule, and the corresponding performance optimization operation is carried out according to the current operating condition; compared with the prior art, coverage performance optimization operation of all pieces of hardware of the system is avoided, hardware with performance bottlenecks in the system is purposely optimized, the optimization process is simplified, the optimization time is shortened, and the optimization accuracy and the optimization efficiency are improved. The invention also discloses a rapid optimization system for the operating performance of the Linux system. The described advantages of the rapid optimization system are achieved.