103 results about "Hybrid genetic algorithms" patented technology

The invention proposes a steelmaking-continuous casting scheduling method utilizing a priority policy hybrid genetic algorithm. The method comprises the following steps: establishing a production scheduling plan target function; establishing a constraint condition set; performing iterative calculation on the target function by utilizing the priority policy hybrid genetic algorithm; and calculating decision variables. The calculation of the decision variables specifically comprises the steps of performing model initialization; calculating a feasible solution: designing a segmented combined real number code composed of casting time information of a continuous casting machine and information of a heat machining device, randomly generating operation time according to a distribution law, and obtaining a time conflict-free scheduling plan by backward inference calculation and conflict elimination methods; and performing population genetic optimization: quantitatively describing a matching relationship among machining devices in reality by using processing weight assignment of a task executable device, and introducing the matching relationship to genetic operation in the form of a device selection priority policy to perform population evolution. The method can solve the problem of uncertainty of device selection and operation time in production to obtain an optimized executable production scheduling plan.

The invention relates to a dynamic adjustment-based express delivery distribution optimization method. The method includes the following steps that: express delivery dynamic information is obtained in real time, wherein the express delivery dynamic information contains the locating information of express delivery vehicles, goods delivery point information, goods taking point information and real-time traffic information; with minimum total distribution costs adopted as an objective function, a mixed integer programming model is adopted to construct a current distribution path according to the express delivery dynamic information, wherein the minimum total distribution costs contain the transportation costs of the vehicles, the fixed costs of the enabling of the vehicles, additional costs caused by the exceeding of maximum travel and penalty costs caused by the violation of time window constraints; and a simulated annealing hybrid genetic algorithm is adopted to optimize the current distribution path. According to the dynamic adjustment-based express delivery distribution optimization method of the invention, the current distribution path is optimized and dynamically adjusted according to the express delivery dynamic information, and therefore, the speed of express delivery distribution is improved, customer demands can be quickly responded, the distribution costs of an enterprise can be decreased, the utilization rate of the vehicles can be improved, urban traffic congestion can be reduced, and the operation efficiency of express delivery business can be benefitted.

The invention discloses an iron-making and steel-making continuous casting integrated dispatching system, and belongs to the planning and dispatching field of iron-making and steel-making continuous casting production. The dispatching system comprises system implementing conditions and four sub-modules, wherein the system implementing conditions comprise equipment state and data acquisition programs, a database server, a database management program, database software and a client application program; and the four sub-modules comprise a torpedo ladle and foundry ladle dispatching planning and blast furnace area-converter area molten iron planning matching model module, an iron-steel interface molten iron dispatching framework module, a molten iron pretreatment-continuous casting dispatching module and an information communication module. Iron-steel interface torpedo ladle dispatching is implemented by monitoring the equipment state, judging the abnormal condition of the production, recording the operation performance, combining the setting parameters of dispatching personnel and establishing a blast furnace area-converter area molten iron planning matching model; static dispatching of a molten iron pretreatment-continuous casting section is generated by adopting a heuristic algorithm, and dynamic dispatching is implemented by using a hybrid genetic algorithm; and quick and flexible on-line dispatching of the molten iron pretreatment-continuous casting section is implemented.

The invention belongs of the field of Iaas (infrastructure as a service) cloud computation, and relates to a hybrid genetic algorithm based dynamic cloud-computing virtual machine scheduling method. The method includes the steps: monitoring load information of physical machines and virtual machines in the cloud computing environment, analyzing a load cycle and changes of each host, and determining a cycle of load changing; computing a virtual machine placement combination appearing in cloud computation through a hybrid genetic algorithm with multiple fitness degrees. According to three optimization objectives, an optimized virtual machine placement strategy is computed to serve as a final result; the algorithm is executed periodically, the virtual machines are reasonably placed through dynamic migration of the virtual machines, resource utilization rate is increased, and resources are saved. By the method, the problem that resource utilization rate in a current cloud computing center can be solved, and the requirement on automatic management of the current cloud computing center is met.

The present invention relates to design method of optimizing water quality model parameters in improved mixed genetic algorithm, and belongs to the field of water quality model parameter calibration optimizing method. The design method includes the following steps: preparing data for water quality model parameter calibration optimizing design; establishing and optimizing improved mixed genetic algorithm; improving adaptability degree function; mixing with simplex algorithm serially; running the algorithm program, inspecting the simulating result of different algorithms and completing the optimization after the error reaching the specified standard. The present invention can calibrate model parameters fast accurately, and raise the application efficiency of comprehensive water quality model in various water body researches.

An optimal design method of a superconducting magnet used for a magnetic resonance imaging (MRI) device comprises the following steps: obtaining current distribution maps of a primary coil and a screened coil according to magnetically confined conditions and then obtaining the initial cross section of the screened coil based on the current distribution maps; with the magnetic field of the screened coil as a background magnetic field, obtaining the current distribution map of the primary coil again according to magnetically confined conditions, thereby obtaining the initial cross section of the primary coil; based on the initial cross sections of the primary coil and the screened coil, optimizing coil section parameters by adopting hybrid genetic algorithm of simulated annealing to obtain the optimal coil geometries. By the method, the primary and the screened coil can be simultaneously designed.

The TCP / IP network performance evaluation prediction method comprises: analyzing bandwidth, time delay and missing package rate; using modified BPNN to decide network performance on condition with a certain load. This invention integrates several indexes comprehensively, can predict network load capacity and optimizes business for proper transmission control.

The invention discloses a method for friction compensation of a ball screw feeding system. On the basis that the actual friction phenomenon of the system is analyzed, segmented models of friction torque in speed sections are provided, friction in the low-speed section is modeled through a Steck Berry model, and friction in the high-speed section is modeled through a logarithmic model. Unknown parameters of the friction models are recognized through a hybrid genetic algorithm, the friction compensation amount at the real-time speed is estimated based on the recognized friction models, and friction compensation of the system is achieved in a compensation amount feedforward mode. Through the segmented friction model, friction in the ball screw feeding system is accurately described, the parameters of the friction models are accurately recognized through the designed hybrid genetic algorithm, motion accuracy of the ball screw feeding system is improved based on the feedforward compensation mode of the friction models, and the method can be widely applied to various systems which comprise ball screw feeding mechanisms.

Provided is a multi-objective optimization method based on a Gaussian process simultaneous MIMO model. According to the method, samples are obtained in an experiment design method, a mapping relation between design variables and responses to be studied is approximately set by utilizing the Gaussian process simultaneous MIMO model, multi-objective optimization is carried out on the Gaussian process simultaneous MIMO model in a Gaussian variation hybrid genetic algorithm, an ant colony algorithm and the like, then Pareto front related to design variable combinations is obtained, furthermore, distribution quality of solution sets in the Pareto front is judged, finally, one design variable combination in the Pareto front is selected according to specific demands for high-accuracy analysis and solution, and physical experiments are carried out when obtained results are satisfactory. According to the multi-objective optimization, experiment design, the high-accuracy analysis and solution, an agent model technology and an optimization algorithm are integrated and applied to the optimization design, therefore, time consumption and computation cost of the optimization design are reduced greatly, and work efficiency is greatly improved.

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.

The invention discloses an underwater auxiliary oil recovery control system and a dynamic positioning method thereof. Focusing on an uncertainty problem that a motion of the underwater auxiliary oil recovery robot is interfered by ocean current, combining the manufactured underwater auxiliary oil recovery robot, the invention establishes a dynamical model based on a fluid dynamic numerical simulation identification parameter method and performs six-degree-of-freedom dynamic positioning analyzing. A position and a heading of the underwater auxiliary oil recovery robot are obtained by a sensor;a state of the underwater robot is estimated in real time by adopting an adaptive unscented Kalman particle filtering algorithm based on a hybrid genetic algorithm-Markov chain Monte Carlo method (GA-MCMC); a fast terminal reaching law is introduced into a non-singular fast terminal sliding mode control to compensate thrust, so that influence caused by interference such as the ocean current is reduced; and then a force and torque distribution strategy is designed according to a positioning error. The method has a good dynamic positioning effect, and can quickly adjust the dynamic distributionstrategy after being interfered in order to reduce the interference impact caused by random ocean current.

The invention discloses an isomorphism identification method of a mechanism kinematics chain based on a pseudo-crossover hybrid genetic algorithm, comprising the following steps: forming a corresponding mechanism topological graph according to a structure of the mechanism kinematics chain; setting genetic codes of an adjacent matrix; taking adjacent matrices of two mechanism topological graphs tobe judged to determine a target function and calculating a target function value and the fitness of each individual; then adopting a pseudo-crossover operator, randomly selecting two individuals froma population which is randomly generated, and randomly generating two crossover points to carry out air extension; selecting two individuals from an intermediate population which is generated by the extension to carry out primary local optimization; and finally, combining with a local searching operator to form the pseudo-crossover hybrid genetic algorithm. The invention can carry out operation and simulation on isomorphism identification of the mechanism kinematics chain so as to accurately carry out the isomorphism identification on the kinematics chain with high efficiency.

The invention discloses a virtual machine dispatching method based on a hybrid genetic algorithm, and belongs to the technical field of cloud computing and virtual machines. The virtual machine dispatching method comprises the following steps of by setting a virtual machine dispatching cycle, using a genetic algorithm to solve a global optimum mapping relation between a virtual machine and physical servers at the beginning of the cycle, in the dispatching cycle, carrying out load monitoring on each physical server, using an optimum adaptive method to determine a new dispatching scheme when a hot point or a new virtual machine request appears, and thus realizing load balancing of a physical machine cluster. The virtual machine dispatching method can achieve the optimum global load balancing, the hot point is quickly eliminated, and the new virtual machine is deployed.

The invention discloses an on-line measurement device and measurement method of a grounding resistor. The on-line measurement device comprises an on-site measurement device and a background algorithm processing center. The on-site measurement device comprises a power supply module, a variable-frequency power supply module, a weak signal conditioning module, a data acquisition module, a main control chip, a man-machine interaction module and a ground loop; the variable-frequency power supply module, the data acquisition module, the power supply module and the man-machine interaction module are connected with the main control chip; and the variable-frequency power supply module and the weak signal conditioning module are connected with the data acquisition module. The ground loop comprises a voltage jaw and a current jaw. The background algorithm processing center adopts a hybrid genetic algorithm to perform data processing, and then a grounding resistance value of a measured electrode is obtained. After the structure and the method are adopted, on-line real-time detection can be achieved, and a grounding lead does not need to be disconnected; and a grounding equation set is constructed through the grounding resistance value obtained through measurement, and a calculation of the grounding resistance value is achieved through an application of the hybrid genetic algorithm, so a measurement error is reduced, and the measurement is convenient and accurate.

The invention provides a metro crew working scheduling method based on the hybrid genetic algorithm. The method includes: dynamically dividing working diagram data into daily task crew shifting cards according to train working diagram data and restrain conditions and performance requirements of working of crew working drivers in metro operation. The metro crew working scheduling method has the advantages of distributing operation tasks through a metro system, generating reasonable task sequences, ensuring reasonable alternation of off days and working tasks of crew members and avoiding messy manual task division.

The invention discloses a flight simulation original state compound genetic balancing method. The flight simulation original state compound genetic balancing method comprises the following steps of initializing an aircraft constant straight line flat flight kinetic model and a constraint equation; step 2, obtaining an initial elevator deflection angle, motor thrust, an aircraft attack angle and an aircraft pitch angle; step 3 enabling a formula balancing result to be served as an initial value of a genetic algorithm, executing a genetic optimization algorithm and obtaining an optimal elevator deflection angle, the motor thrust, the aircraft attack angle and the pitch angle; step four solving the optimal elevator deflection angel and the motor thrust according to a mixed genetic optimization algorithm, reasoning backward to solve the displacement of a steering column and a throttle lever, configuring the steering column and the throttle lever and assigning the obtained aircraft attack angle and the pitch angle to the aircraft kinetic simulation model.

The invention discloses a multi-level urban sewage water reuse-and-recycle configuration data's dynamic processing method based on multi-objective hybrid genetic algorithm, comprising: 1) obtaining the basic information data of multi-level water resources relationship; establishing a multi-level data configuration relationship between urban centralized sewage treatment plants and centralized water regeneration plants; 2) obtaining basic information data and other data information about the urban centralized sewage treatment plants and the centralized water regeneration plants; 3) according to the above basic information data, establishing a multi-objective data configuration model which considers the conditions of water quality balancing, water supply capability, water transporting capability, water requiring capability and risk constraints wherein the objective function is a multi-objective function with the largest total water resources utilization, the maximum benefit of water resources reuse and the minimum per capita water deficit in sub-areas; and 4) using the multi-level urban sewage water reuse-and-recycle configuration data's dynamic processing method based on multi-objective hybrid genetic algorithm to seek data configuration schemes for different scenes. The method of the invention can meet the reuse-and-recycle data configuration requirement for urban multi-objective and multi-level sewage water.

The invention relates to a design method for the equivalence a hinged corrugated pipe by coupled variable cross-section beams in order to simplify the finite element analysis work for the complicated pipeline system design of the hinged corrugated pipe. Firstly, deducing the relation between force and displacement when transversal force is exerted on the free end of either variable cross-section beam of which any interface is rectangular based on the assumption that a cantilever beam has small deflection, and considering the elasticity and the elastic plastic deformation of either variable cross-section beam; secondly, proposing a method of simplifying a hinged corrugated pipe model and a method of utilizing finite element calculation to obtain the rigidity data of the fixed part of the simplified hinged corrugated pipe in two directions; thirdly, proposing a method of utilizing a mixing genetic algorithm to optimize parameters of the variable cross-section beams and proposing a special gradual shrinkage method aiming at the mixing genetic algorithm so as to further obtain geometric parameters and material parameters of the two variable cross-section beams; and finally, coupling the two variable cross-section beams at the free ends by constraint equations and connecting the roots of the variable cross-section beams with flanges by establishing auxiliary discs.

The invention discloses a wave beam forming method of ascending MIMO-SDMA, which relates to the field of wireless communication. The invention provides a wave beam forming method based on a New-Memetic algorithm. Aiming at the defect of the traditional wave beam forming method for working out an optimal weight value based on a gradient method (such as a minimum mean square error algorithm and a linear constraint minimum variance algorithm), the method improves the processes of intersection and variation on the basis of the Memetic algorithm (a mixed inheritance algorithm), thereby enhancing the convergence speed and the local searching capability of the algorithm. The method can search the weight vector optimal value of an antenna array, carry out the self-adapting zero control of an interference direction, also enable a maximum gain main lobe to be directed at the direction of a desired signal and enhance the signal-noise ratio of the system.

The invention provides a hybrid genetic algorithm for solving a multi-objective flexible job-shop scheduling problem. The algorithm takes three objectives of the completion time, the production cost and the equipment utilization rate in flexible job-shop scheduling into consideration, and aims at the circumstances that algorithms at present are difficult in decoding and long in computation time because of possession of a complicated coding method and that the genetic algorithm is faced with problems of global near optimum and insufficient search capability in local search. In allusion to the problems, the invention provides a new matrix chromosome coding method which almost does not need to perform decoding. In addition, the algorithm provided by the invention combines the global search capability and local search of the genetic algorithm, the search capability of the algorithm is enhanced, and a feasible solution is found more easily. The hybrid algorithm is high in practicability, and can be well applied to flexible job-shop scheduling.

The invention relates to a modeling method for a micropore structure in a bionic bone scaffold. The method comprises the following steps that: a negative model of the micropore structure in the bionic bone scaffold is solved on the basis of a multi-constraint knapsack problem model by using an ellipsoid as a base element, using the minimum accommodating box of a bone scaffold model as a constraint space and using a hybrid genetic algorithm; and then a bionic bone scaffold model comprising the micropore structure is built through a Boolean subtraction operation between the scaffold model whichdoes not contain micropores and the negative model of the micropore structure. Under the condition of ensuring relatively high void ratio and superficial area of the micropore structure in the bionicbone scaffold, by the method, single shape of the internal micropores and uneven distribution of the micropores are overcome and the torsion resistance of internal communication pore canals in the bionic bone scaffold is increased, so that the performance of the bionic bone scaffold model is improved and the bionic bone scaffold model has relatively high bioactivity.

The invention discloses a method for solving flexible job-shop scheduling based on a hybrid genetic algorithm. The method is specifically implemented according to the following steps of 1, defining aflexible job-shop scheduling coding mode as a double-layer coding mode; 2, defining a fitness function, and taking the shortest time for solving and finishing machining as an optimization target; 3, initializing parameters and populations in a flexible workshop scheduling problem by adopting a genetic algorithm and a whale swarm algorithm, and randomly generating an initial population; 4, solvingflexible job shop scheduling through a genetic algorithm; 5, solving flexible job shop scheduling through a whale swarm algorithm; and 6, realizing iterative optimization of the whale swarm algorithmon the genetic algorithm through coding recombination, and outputting an optimal scheduling scheme. According to the method, the search depth of the algorithm is increased, and solving precision and solving stability are enhanced.

The invention discloses an indoor underwater target positioning method based on a genetic algorithm, wherein an underwater target positioning problem is converted to an optimization problem, and furthermore the optimization problem is solved through applying a hybrid genetic algorithm, thereby obtaining a positioning result. An objective function, a fitness function and a constrained condition which are resolved in the genetic algorithm are determined according to a specific indoor underwater object positioning system. Compared with the prior art, the indoor underwater target positioning method has advantages of greatly improving object positioning precision, realizing high convergence reliability and relatively high convergence speed, and obtaining relatively low effect of a measurement error to positioning precision. Furthermore the indoor underwater target positioning method can be expanded for being applied in other systems for positioning the object based on range finding information.

3 The present invention relates to an iron and steel product bulk cargo ship stowage method. It is characterized by that it utilizes mixed genetic algorithm, and adopts a heuristic method to define concrete arrangement mode of cargo in every cargo hold so as to meet the cargo loading quality requirement. Said genetic algorithm can use heuristic method to calculate stowage index and define the distribution of cargo in every cargo hold so as to meet the capacity limitation of every cargo hold, so that a reasonable cargo stowage scheme can be made up.

The invention provides a casting production scheduling method and system, and the method comprises the steps: building a multi-objective weighted scheduling model according to the processing characteristics of a casting at a previous section, wherein an objective function of the multi-objective weighted scheduling model is the minimization of the average vacancy rate of a sand box and the minimization of the maximum completion time; solving the multi-objective weighted scheduling model through a hybrid genetic NEH algorithm to obtain a front workshop section scheduling scheme; establishing a flexible flow shop scheduling model according to the flexible machining characteristics of the castings in the later working section, wherein the objective function of the flow shop scheduling model isthe minimum maximum completion time; and solving the flow shop scheduling model through a whale swarm algorithm to obtain a rear workshop section scheduling scheme. According to the scheme, the problem of low casting production efficiency is solved, the production efficiency and the resource utilization rate are effectively improved, and integrated refined management and control are facilitated.

The invention discloses an enterprise order processing method under an individualized customization demand, relating to the enterprise order processing method under the information communication technology. The enterprise order processing method under the individualized customization demand lists and analyzes a constraint theory, an entropy concept and an ant colony algorithm in detail to determine application of the order production priority by targeting determination on order production priority by a manufacture enterprise under individualized demand environment, and combines with a practical production operation mode of the enterprise to point out limitation of a traditional method. The enterprise order processing method puts forward a mathematic model determined by the order production priority in a multi-node dynamic production system, and, on the basis of that, proposes to fuse a simulation annealing algorithm and a multi-colony genetic algorithm to generate a hybrid genetic algorithm and solves the problem of order production priority determination. The hybrid simulation algorithm not only can overcome limitation of the multi-colony genetic algorithm, but also can use advantages of the simulation annealing algorithm to comparatively successfully process an order processing model in the multi-node dynamic production system, and thus can guide enterprise practical activity.

The invention provides a method for optimizing and controlling the little-cutting fluid processing technology in the machining process. The method comprises the following steps that the machining performance parameters of a machine tool are set when a machining device cuts a workpiece, a multi-objective optimization model with the machine tool performance in the machining process as the constraint condition and with the cost and the cutting fluid amount as optimization objectives is established, a little-cutting fluid processing technology parameter optimization model is established through the multi-objective optimization model and constraint, a multi-objective evaluation function and constraint are converted into a single-objective evaluation function, the single-objective evaluation function is optimized through the mixed genetic algorithm to obtain the cutting parameter value when the amount of the cutting fluid is minimum, and the cutting parameter optimization is finished. The amount of the cutting fluid in the machining process is more accurate through the method, the manufacturing cost is reduced, and support is provided for little-cutting fluid processing technology planning and technology improving in the machining process.

The embodiment of the invention discloses a path determination method. The method comprises the steps that all the received orders are grouped, and the grouped orders are determined as the initializedorders; the shortest transport path of the initialized orders is determined according to the hybrid genetic algorithm HGA; at least two times of processing is performed on the initialized orders according to the preset change rules so as to obtain each changed order; the shortest transport path of each changed order is determined according to the HGA; and the shortest transport path of each orderis determined according to the shortest transport path of the initialized orders and the shortest transport path of each changed order. The embodiment of the invention also discloses a path determination device.