527 results about "Hybrid algorithm" patented technology

The invention provides an adaptive digital pre-distortion technique and device which is suitable for OFDM projector. The device comprises an OFDM baseband signal module for orthogonal frequency division multiplexing technique, a digital pre-distortion synthetic process component, an analog and digital converter DAC, a radio frequency projector, a wideband high power amplifier, i.e., W-HPA, and a feedback circuit. Wherein, a digital pre-distortion internal core, which is structured in polynomial mode, implements pre-distortion process to the digital signals which have undergone signal pre-process; the estimate and uploading to an adaptive digital pre-distortion filter is implemented, based on hybrid algorithm which is the combination of training-sequence-based RLS+LMS algorithm or hybrid algorithm which is the combination of training-sequence-based QRD-RLS+NLMS algorithm. The invention can efficiently reduce the performance degradation of a wideband power amplifier caused by memory effect, improve the projecting performance of a base station system, and improve the linearity and the efficiency of the wideband power amplifier.

The invention discloses an interactive type image segmentation and fusion method based on a grabucut algorithm. The interactive type image segmentation and fusion method based on a grabucut algorithmcomprises steps of adopting a pyramid down-sampling to reduce a resolution of an image, using few typical pixel points to estimate a GMM parameter, using an interactive type technology to mark a foreground and a background of the image, performing watershed segmentation, performing modification on a mask image obtained from segmentation and transmitting the modified the mask image to a grabucut for fine segmentation, performing morphological processing on the segmented image, continuously performing pixel transformation and morphological processing on a processed image to obtain a tripartite graph, using the trimap graph and a source graph as inputs, using a shared matting algorithm to obtain an alpha channel image of the foreground image, using the alpha channel image, the source image and a new background image as inputs and using an alpha blending transparent mixed algorithm to obtained a fused image.

The invention discloses a distributed power source contained power system multi-target reactive-power optimization method in the field of power system reactive-power optimization. The technical scheme includes: 1, deducing a model of a wind-driven generator in power flow calculation; 2, initializing power grid parameters and grid-connected parameters of a distributed power source; 3, constructing an individual vector formed by system reactive-power optimization control variables, and initializing species; 4, performing the power flow calculation according to the initialized species and grid parameters after grid-connection of the distributed power source, and calculating objective function values; 5, performing multi-target optimization by utilizing the harmony search hybrid algorithm based on artificial bee colony; and 6, finishing the optimization process and outputting optimized results. The distributed power source contained power system multi-target reactive-power optimization method is a hybrid optimization algorithm ABS-HS which integrates the advantages of global search of the artificial bee colony (ABC) algorithm with local search of the existing harmony search (HS) algorithm, so that efficiency and robustness of the algorithm are improved.

The invention relates to an AGV optimization scheduling method based on mixed particle swarm optimization. First of all, a mathematics model is abstracted from the work process of an AGV, and an object function of a scheduling scheme is determined, and secondly, the model is solved by use of the mixed particle swarm optimization based on a genetic algorithm, a stimulated annealing algorithm and ant colony optimization, and an optimization scheduling scheme is generated. According to the invention, a contrast analysis is made between the mixed particle swarm optimization and standard particle swarm optimization through examples, and the variation operation of the mixed optimization employs an ant colony optimization thinking mode, ensures intersection of individual best and group best in an intersection operation process, ensures the feasibility of the mixed particle swarm optimization and has the validity for solving large-scale scheduling tasks.

The invention discloses an optimization method for solving an omni-channel logistics distribution problem. In a first stage, a Lagrange relaxation technology is used to solve a LAP (Location-allocation Problem) problem. In a second stage, adaptive large-scale neighborhood search is used for solving a multi-vehicle-type vehicle path problem, a feasible solution can be searched in a large range in a solution space through one group of simple destruction and reconstruction algorithms, and a situation of falling into local optimum can be effectively avoided. Meanwhile, a simulated annealing acceptance criteria realized in the adaptive large-scale neighborhood search can guarantee the quality of a solution and the convergence of the algorithm, a result is output after the algorithm is executed for an appointed iteration number, a time constraint requirement can be met, and a good vehicle distribution scheme is solved for enterprises in limiting time. The simpleness of the Lagrange relaxation technology is combined with the efficiency of the adaptive large-scale neighborhood search, so that the integral solving efficiency of a mixed algorithm is high, and the problem of omni-channel logistics distribution can be effectively solved.

The invention relates to a hybrid genetic simulated annealing algorithm for solving a job shop scheduling problem. The job shop scheduling problem is solved through the algorithm. The hybrid genetic simulated annealing algorithm aims to solve the problems that the genetic algorithm is poor in local searching capability but high in capability of overall search process grasping, the simulated annealing algorithm has high local searching capability but knows less of conditions of the entire searching space and is inconvenient for enabling the searching process to enter the most promising searching region and the like. The genetic algorithm and the simulated annealing algorithm are combined for adopting the long points while overcoming the weak points, and the hybrid GASA is proposed. According to the algorithm, genetic operations such as selection, crossing, variation and the like are performed on populations to generate new populations, then each individual in the new populations is subjected to the simulated annealing, results are taken as input of genetic operation in the next steep, and the whole operation process is subjected to repeated iteration till certain end condition is satisfied.

The invention discloses a wind and light storage generating unit capacity optimal configuration method based on the rated capacity. The method comprises the following steps that firstly, a model is established according to the distribution situation of the local wind resource and the local light resource; secondly, a storage battery device is controlled according to the principle about utilizing the renewable energy sources to the maximum degree and performing constant output, and a coordinated operation strategy of the system is formulated; thirdly, a target function is set to show the expenditure in the life cycle of a generating unit and, and the expenditure is set to be minimal; fourthly, the constraint condition of capacity optimal configuration is determined; fifthly, a fuzzy logic control method is utilized for dynamically adjusting an energy converting model of an energy-storing storage battery so that the energy converting model can achieve rapid convergence; sixthly, based on the target function and the constraint condition, the target function of the generating unit and capacity proportion optimal values of all parts are resolved according to an improved iteration and self-adaptation genetic mixed algorithm. According to the method, a power grid can conveniently assess the generating capacity of the generating unit, and the power grid can easily formulate the dispatching plan and improve the accepting degree for the renewable energy sources.

The invention provides a self-adaptive genetic particle swarm hybrid algorithm optimization method. The self-adaptive genetic particle swarm hybrid algorithm optimization method includes: calculatingthe density and the radius of a center region of a parent population in a genetic algorithm, and distinguishing whether the parent population is in the overall centralized distribution, the local centralized distribution or the uniform distribution; performing a selection operation of the genetic algorithm, and selecting a parent individual to be evolved; establishing computational formulas of thecrossover probability and the mutation probability according to the three distributions of the parent population; performing crossover and mutation operations according to the established crossover and mutation probability formulas so as to achieve chromosome recombination and gene mutation, and forming an offspring individual; selecting a part of individuals with high fitness from a part of offspring individuals to perform the particle swarm algorithm to form offspring particles, and combining the offspring individuals and the offspring particles into an offspring population and saving the optimal individual thereof. The invention adaptively adjusts crossover probability mutation probability parameter values in the genetic particle swarm hybrid algorithm, so that the convergence speed and the convergence precision are greatly improved.

The invention relates to a short-term load predicting method of a power grid. The method comprises the steps: step 1, acquiring historical data and pre-treating the data; step2, decomposing the historical load sample data into a plurality of different-frequency sub-sequences by using wavelet decomposition; step 3, performing single-branch reconstruction to each sub-sequence; step 4, dynamically choosing training samples and establishing a neural network predicting model optimized by a vertical and horizontal intersection algorithm; step 5, predicting each sub-sequence 24 hours in advance by using the optimal neural network predicting model; and step 6, superposing the predicted value of each sub-sequence to obtain a whole prediction result. The inherent defects of the neutral network can be overcome by optimizing BP neutral network parameters by a brand-new swarm intelligence algorithm, that is, the vertical and horizontal intersection algorithm instead of the traditional algorithm; the burr problem caused by the impact load processing is solved by the wavelet decomposition, the precision declining resulting from the removal of the effective load in the burr pre-treatment is solved and the predicted value of the hybrid algorithm is more approximate to the actual measured load value.

The invention relates to a method for programming a transmission network based on a determinacy two-layer programming mode. The programming mode takes a transmission network investment cost as an economy target, and takes load shedding sum of a regular operation and a single fault operation of the system as a reliability target. An underlayer target is the reliability target; an underlayer restriction is an operation restriction of the regular operation and the single fault condition of the system; an upperlayer gives priority to the economy target; the underlayer reliability target is added to the upperlayer target in a way of penalty function, and the upperlayer restriction is an awaiting frame line number restriction. An improved arithmetic mixed with a niche genetic algorithm with a primal-dual interior method together is adopted to calculate the mode; the niche genetic algorithm is used for processing a integer variable of the upperlayer programming and has a global optimization; the primal-dual interior method is adopted to have a quick calculation to improve the arithmetic speed and a convergence. The invention is able to add the reliability issue to the economy programming in a restriction way and realize an economy optimization of the programming proposal under a high reliability condition.

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.

There is provided a traffic placement method in a communications network, the communications network comprising a plurality of nodes, the nodes being connected to one another by links, the method comprising selecting a (possibly non-strict) subset from a given set of traffic flow demands and calculating a plurality of paths for the selected demands under consideration of a set of constraints using an algorithm hybridisation combining backtrack search with local consistency techniques (BT+CS) and guiding search by the use of one or more probe generators, that is, search techniques that solve a routing sub-problem or an arbitrary relaxation of the traffic placement problem. By using a hybrid algorithm that integrates other solvers (search techniques) into BT+CS through the use of probe generators, a more powerful search strategy can be achieved compared to BT+CS or the individual search techniques.

The invention relates to a radar imaging method of an electrically large size target in the ocean clutter environment. The radar imaging method is that the radar imaging of the electrically large size target in the ocean clutter environment is achieved by establishing a surface-box complex target scattering model in a multi-interference environment and adopting electromagnetic simulating calculation through a numerical and analytical hybrid algorithm and a rapid back projection imaging mode. The electrically large size in the radar imaging method means that the ratio of the physical dimension to the wavelength of the target is larger than ten. The working frequency range of a radar is generally in a high frequency area, for example, the L waveband frequency ranges from 1GHz to 2 GHz, and the S waveband frequency ranges from 2GHz to 4 GHz. Some military targets, such as fighters, invisible planes and aircraft carriers in the L waveband and the S waveband belong to the electrically large size. According to the radar imaging method, the radar imaging of the electrically large size target in the ocean clutter environment can be achieved by means of geometric modeling of complex targets, the electromagnetic simulating calculation, the rapid back projection imaging algorithm and Kaiser window edge filtering.

A flexible job shop order insertion dynamic scheduling optimization method is a solution method aiming at the delay problems caused by the order insertion in the job shop batch dynamic scheduling, andcomprises the steps of on the basis of establishing a mathematical model of the task sequence optimization and the order batch distribution, researching a batch selection strategy, adopting an example simulation mode to obtain the reasonable sub-batch number, at the same time, according to the simulation and calculation of the typical examples, giving a recommending value of the batch number; secondly, based on the three-layer gene chromosomes of the processes, the machines and the order distribution number, taking the minimum maximum time of completion and the delay period as the optimization targets; and finally, adopting a mixed algorithm of a particle swarm optimization algorithm and a genetic algorithm to improve the speed of evolution of the sub-batch number towards an optimal direction, thereby effectively reducing the tardiness quantity. The method is good at reducing the delay period in the job shop dynamic scheduling, and for the conventional genetic algorithm, enables the convergence speed and the stability to be improved substantially, at the same time, fully combines the actual production statuses of the intelligent job shops, greatly promotes the dynamic scheduling solution, and has the great application value in the engineering.

The invention discloses an ORP (optimal reactive power) method of a distribution network of an electric power system in the technical field of ORP of the distribution network of the electric power system. The method comprises the following main steps: introducing an accelerated evolution operation and an investigation operation in an ABC (artificial bee colony) algorithm to a basic differential evolution operation; and judging whether conditions of convergence of a hybrid algorithm are met, and ending the optimization and outputting the optimal result if the conditions of convergence are met.The hybrid algorithm for solving the ORP problem exerts the advantages that the operation is simple, robustness is good and the like, of the differential evolution algorithm, and can be used to shorten the running time of the algorithm and improve the probability of finding out the global optimal value.

A solar energy grid-connected inverter control system based on LCL filtration comprises a photovoltaic array, an electromagnetic interference filter, namely an EMI filter, a power module, an LCL filter, an isolation transformator TB1, a sample circuit, a filter circuit and a controller which includes a maximum capacity tracking MPPT unit. MPPT adopts an intelligent algorithm based on a hybrid algorithm of a simulated annealing method and a genetic algorithm. The hybrid optimization algorithm takes the operation process of the genetic algorithm as the main process and also combines a simulated annealing mechanism that is used for further adjusting optimized instruction data. The control system has the advantages of good stability, strong performance and high efficiency and reliability.

The invention relates to a method for reactive power optimization of an electric power system on the basis of a clone-particle swarm hybrid algorithm. The method comprises the following steps of: solving a trend by adopting a Newton-Laphson algorithm; superimposing network losses of each branch to obtain the network losses of a whole system; and introducing a clonal operator of a clonal algorithminto a standard particle swarm algorithm. The clone-particle swarm hybrid algorithm has stronger capacity of searching the globally optical solution; and compared with a method for the reactive poweroptimization of the electric power system on the basis of the standard particle swarm algorithm, the method for the reactive power optimization of the electric power system on the basis of the clone-particle swarm hybrid algorithm obtains a smaller system network loss value, and can reduce the running cost of an electric network to a greater degree.

The invention provides a distribution method of container quay berths and shore bridges. By adopting a rolling type plan distribution method, berth and shore bridge distribution models based on multi-objective planning are constructed; the models are based on a continuous quay wall line and are more closer to the actual berth conditions of a quay; a hybrid algorithm on the basis of combining a heuristic algorithm and a parallel genetic algorithm is adopted, and the performance of the hybrid algorithm is evaluated by a distribution simulation system of the container quay berths and the shore bridges; when a berth and shore bridge distribution scheme is generated, the simulation system simulates the distribution scheme, acquires corresponding performance indexes, compares with other schemes, and determines whether the scheme is better; and a method combining simulation and a gene repair technology is adopted to repair infeasible schemes, thereby being favorable for reducing the time in port of a ship, and reducing the horizontal transport distance when the ship is loaded or unloaded, the energy consumption of the shore bridges, and the fine that the quay pays to a ship owner, and further reducing the loading and unloading cost on the quay, improving the service quality of the quay and realizing the purpose of the invention.

The invention discloses a parameter selection method for a support vector machine based on a hybrid bat algorithm. Regularization parameters and RBF kernel parameters have great influences on the learning performance and computation complexity. On the basis of analyzing the advantages and disadvantages of some classical parameter selection methods, an intelligent optimization algorithm is introduced to perform optimization on the parameters. The bat algorithm has the advantages of concurrency, high convergence speed and strong robustness. The bat algorithm is firstly utilized to perform optimization on the SVM parameters, then crossover, selection and mutation operators of differential evolution algorithm are introduced in allusion to a defect of early maturing of the bat algorithm, the position is further adjusted according to the three operators in each iteration process by using a bat individual, the search ability of the algorithm is enhanced, the algorithm is avoided from prematurely falling into a local optimal solution, and finally the SVM parameter selection is optimized by using an improved DEBA algorithm to obtain an excellent effect.

The invention discloses an addressing and capacity configuration method used for a UPFC. A UPFC addressing and capacity configuration model with transient stability constraints is established, a difference evolution algorithm serves as a framework, and the installation position and the capacity of the UPFC are optimized; in addition, a centrality correction interior point method is adopted for carrying out optimization and fitness assessment on continuous variables of each difference evolution unit. The difference evolution algorithm is simple in operation, high in searching capability, fewer in adjusting parameters and suitable for mixed optimization, and the interior point method is good in convergence and strong in robustness, so that the provided mixed algorithm can well solve the problems of UPFC addressing and capacity configuration, the method is highly efficient and convenient, and the application prospect is good.

The embodiment of the invention provides an unmanned vehicle path planning method and device. According to the method, high-precision map information is analyzed, repulsion is calculated for each gridaccording to map obstacle information after the map is rasterized, the repulsion serves as the searching cost, the obstacle avoidance efficiency of a hybrid A* algorithm can be effectively improved,and the generated trajectory conforms to vehicle dynamics constraints. Meanwhile, the generated trajectory is smoothed by using a gradient descent algorithm, and finally an optimal trajectory is output. Vehicle kinematics constraints are considered, so that the planned path has drivability; a potential field is calculated for each grid, so that the path searching time can be reduced to a certain extent; the potential field method enhances the tolerance of sensing errors to a certain extent, and improves the robustness of path planning; different repulsion levels are allocated to different types of obstacles, and repulsion weights are dynamically set for the boundary width of the lane-level road; and the gradient descent smoothing module is adopted to smooth the generated path, so that thepath is more suitable for vehicle driving.

The invention discloses an opportunity constraint planning-based commercial park comprehensive energy system optimization scheduling method. The method comprises: firstly, constructing a commercial park comprehensive energy system model, and on the basis of modeling of energy conversion of all elements in a park, considering uncertainty of new energy output and load, and establishing a commercialpark energy optimization scheduling model based on opportunity constraint planning. The model aims at minimizing the operation cost. An improved immune genetic algorithm and a stochastic simulation hybrid algorithm are adopted for solving, quantitative indexes are established for accompanying unbalance risks in an opportunity constraint planning model, reference is expected to be provided for comprehensive energy system operation scheduling for balancing economy and reliability, and the method has important guiding significance for actual scheduling of the system.

The invention mainly provides a bus stop site selection and layout optimization method based on passenger trip spatial distribution. The bus stop site selection and layout optimization method based on the passenger trip spatial distribution mainly comprises construction of a bus stop site selection optimization model and a solution algorithm thereof. The bus stop site selection optimization model takes minimization of walking distance of all the residents for taking buses as a goal and considers realistic constraint factors such as maximum walking distance between a resident dense point and a bus stop and upper and lower bounds of distance between adjacent stops, the solution algorithm of the bus stop site selection optimization model is a novel hybrid algorithm and combines advantages of a bacterial foraging optimization algorithm and a group random search algorithm, and according to problem characteristics, individual bacterium coding, initial bacterial colony generating, individual bacterium evaluation function generating and bacterial foraging operations are redesigned. The bus stop site selection and layout optimization method based on the passenger trip spatial distribution scientifically and reasonably determines bus stop positions by combining a real road topological structure according to trip spatial distribution characteristics of residents nearby a route, so that resident trips are facilitated, and bus operation efficiency is also improved.