644results about How to "Improve global search performance" patented technology

The invention discloses a method for solving a multiple-depot logistics transportation vehicle routing problem. The method comprises steps as follows: inputting multiple-depot problem basic parameters based on real-time traffic information, establishing a multiple-depot logistics transportation scheduling mathematic model based on the real-time traffic information, adopting a clustering analysis method, introducing the particle swarm optimization algorithm to adjust and optimize ant colony algorithm pheromones, optimizing ant colony algorithm heuristic factors with the particle swarm optimization algorithm, solving an optimal distribution route, and establishing a mathematic model according to the multiple-depot logistics transportation vehicle routing problem based on the real-time traffic information; taking distances between clients and parking lots as main factors, performing area division on the clients and the parking lots with the clustering analysis method, and converting a multiple-depot problem into a single-depot problem; introducing the particle swarm optimization algorithm to improve the ant colony algorithm to solve the model. The method has the better global and local optimization capacity and has higher efficiency and stability when solving the multiple-depot problem.

The invention discloses a plan method for rapid coverage track search coordinated by multiple UAVs, and belongs to the field of UAV track planning. Key searching objects, namely points, lines and surfaces, are extracted from a gray area according to prior information of a battlefield environment and geometric characteristics of an object existing area; the access sequence of the key searching objects is determined via an integer-dual-coded genetic algorithm; according to the key searching objects and the distribution sequence of the UAVs, a local shortest connection track from the end point of a present object coverage search track to the start point of a next object coverage search track is obtained via a Dubins path and a greedy strategy, the coverage search track of the next search object is obtained, and the UAV coordinated coverage search track is obtained. The coordinated rapid coverage search track of multiple UAVs in the special gray area can be planned, the coverage time is short, the robustness of the algorithm is high, and a regional full-coverage search track can be replaced effectively.

The invention discloses a combined cold heat and power supply microgrid multi-objective dynamic optimal operation method; characteristics of translatable electrical load are firstly considered in an optimization process, then schedulability of source side and energy storage system are considered, contribution in each period in three kinds of controllable units serves as optimization variables, minimum system operation cost and minimum pollutant emission control expense serve as optimal operation targets, and a mathematical model of current multi-objective optimal operation problem is established; an excellent particles leading multi-objective particle swarm optimization algorithm is adopted to solve the optimization problem, that is, a single objective genetic algorithm is utilized to respectively find two points including minimum system operation cost and minimum pollutant emission control expense, and the two points serving as excellent particles is utilized to lead an optimal direction of the multi-objective particle swarm algorithm; the invention provides an effective multi-objective dynamic optimal operation method, and the method is significant for improving energy source comprehensive utilization efficiency of a multiple energy coupled system and promoting renewable energy source development.

The invention discloses a parking system path planning method based on an improved ant colony algorithm, and aims at solving the problem of AGV vehicle access path planning in an intelligent parking garage so that vehicle accessing can be completed in the shortest possible time, utilization rate of parking places can be enhanced, time of waiting for vehicle accessing can be reduced for social members and automatic management of parking equipment can be realized. The concrete planning steps are that an AGV working environment model in the intelligent parking garage is created by adopting a grid method; the conventional ant colony algorithm is optimized and improved by introducing of new node state transfer probability and an updating strategy of combination of local and global pheromones; and simulated testing is performed on the AGV vehicle access path planning process by applying the improved ant colony algorithm and the result is outputted. The method has high global search capability and great convergence performance, and can effectively enhance path search efficiency, shorten search path length and reduce the number of path turnings and can also enable the AGV to effectively avoid obstacles in the complex operation environment so as to search the optimal collision-free path.

The invention provides a power distribution network fault positioning method based on improvement of a binary particle swarm algorithm, the conventional binary particle swarm algorithm is improved, and the method is applied to positioning of power distribution network faults. The method comprises following steps: firstly, determining parameters including the particle swarm scale and the maximum iteration frequency etc.; then forming an expectation function of a switch according to fault information of the switch, and constructing a fitness function of power distribution network fault positioning; initializing a particle swarm, setting particle positions, and setting the speed of the particles as 0; calculating the fitness values of the particles according to the fitness function, and setting an initial global extremum; updating an individual extremum and the initial global extremum; updating the speed and position of the particle swarm; and stopping calculation when reaching the maximum iteration frequency, and outputting the global optimal position of the particle swarm, namely the practical fault state of each feed line section of a target power distribution network. According to the method, the problem of premature convergence of the conventional method can be overcome, and the convergence and the stability of the algorithm can be further improved.

The invention discloses a multi-target reactive power optimization method for an electric system, which belongs to the field of reactive power optimization for electric systems. The method includes: modifying the Memetic algorithm to adapt to multi-target optimization, applying the modified Memetic algorithm to the problem of multi-target reactive power optimization for the electric system, and working out a Pareto optimal solution of the multi-target problem; and judging whether algorithm convergence conditions are met or not, and if yes, completing optimization and outputting optimization results. The multi-target reactive power optimization method has the advantages that the algorithm for solving the problem of multi-target reactive power optimization is provided, the method is more suitable for solving the multi-target problem while giving play to existing advantages of the Memetic algorithm which integrates local searching and evolutionary computation and has high global search capacity and the like, and searching efficiency is improved while algorithm robustness is improved.

The invention relates to a method for positioning a mobile node of a wireless sensor network based on a crossed particle swarm. After the deployment of anchor nodes is finished, the mobile node enters a greenhouse area, and broadcasts and sends a positioning request to all anchor nodes in a network; the mobile node receives the broadcast information of normal anchor nodes, measurement distance between the mobile node and the corresponding anchor node is obtained by calculation, and the measurement distance is corrected by using error coefficients of each anchor node; the mobile node substitutes the information of the normal anchor nodes and the corrected distance into a crossed particle swarm positioning algorithm for optimal calculation; and the algorithm outputs a cluster optimal position as an estimated coordinate of the mobile node. The coordinate of the mobile node is accurately estimated. The method is high in convergence speed, low in calculation complexity, perfect, and high in positioning performance.

The invention provides a method for determining an optimal route of the airway of an unmanned aerial vehicle. According to the method, the threat of an operation area is more sufficiently considered, more efficient global searching ability is achieved and a more accurate flying route is provided for the unmanned aerial vehicle. The method comprises the following steps: by adopting a quantum encoding mode, changing the state of a basic quantum bit by using a quantum rotating gate and a quantum not-gate, and further updating the position of a bat individual. Because of the diversity of the quantum state, a quantum bat algorithm (QBA) is relatively high in global searching ability and an available or even optimal route avoiding the threat and limiting conditions can be found for the unmanned aerial vehicle. The experiment result shows that the quantum bat algorithm is an effective and stable method for solving the airway route planning problem of the unmanned aerial vehicle, and the search performance of the quantum bat algorithm is superior to that of other swarm intelligence algorithms.

The invention discloses a method for solving a logistic transport vehicle routing problem with soft time windows. According to the method, for the purpose of solving the problem of the logistics transport vehicle routing problem with the soft time windows on the basis of real-time traffic information, a time window punishment mechanism is employed and a mathematic model is established; and the model is solved by use of a self-adaptive chaotic ant colony algorithm, and the searching optimization capability of the algorithm is improved through self-adaptive updating of algorithm information elements and chaotic self-adaptive adjustment of algorithm parameters. According to the invention, the method better matches logistics distribution in realistic production life, the problem is solved by use of the self-adaptive chaotic ant colony algorithm, the optimization search capability is better, a search process is effectively prevented from partial optimum, the diversity of solutions and the global searching optimization capability are improved, the global updating strategy is improved, an elite strategy is introduced, and positive feedbacks of information elements released by high-quality ants are properly improved; and the upper limits and lower limits of the information elements and the information element increments are arranged so that overlarge differences of the information elements on a path are reduced, and the classic vehicle routing searching optimization problem is solved by use of the self-adaptive chaotic ant colony algorithm.

The invention discloses a cascade hydropower station group optimized dispatching method based on an improved quantum-behaved particle swarm algorithm. The problems that local optimum happens to the quantum-behaved particle swarm algorithm at the later iteration period due to premature convergence for the reason that population diversity is decreased, and an obtained hydropower station group dispatching scheme is not the optimal scheme are mainly solved. The hydropower station group optimized dispatching method based on the improved quantum-behaved particle swarm algorithm is characterized by comprising the steps that first, power stations participating in calculation are selected, and the corresponding constraint condition of each power station is set; then, a two-dimensional real number matrix is used for encoding individuals; afterwards, a chaotic initialization population is used for improving the quality of an initial population, the fitness of each particle is calculated through a penalty function method, the individual extreme value and the global extreme value are updated, an update strategy is weighed, the optimum center location of the population is calculated, neighborhood mutation search is conducted on the global optimum individual, the positions of all the individuals in the population are updated according to a formula, and whether a stopping criterion is met or not is judged. The hydropower station group optimized dispatching method based on the improved quantum-behaved particle swarm algorithm is easy to operate, small in number of control parameters, high in convergence rate, high in computation speed, high in robustness, reasonable and effective in result, and applicable to optimized dispatching of cascade hydropower station groups and optimal allocation of water resources.

The invention discloses an RBF neural network optimization algorithm based on an improved sparrow search algorithm. RBF initial parameters are optimized through the improved sparrow search algorithm,so that the sea clutter prediction precision is further improved, and the purpose of suppression is achieved. An elite reverse learning strategy is introduced, a current optimal solution is selected as an elite individual, and a reverse solution of the elite individual is generated, so that the global search capability of the algorithm is enhanced. Self-adaptive Gaussian variation is adopted to perform variation on an optimal solution and perform greedy selection, and in addition, a position updating mode for sparrow investigation early warning is also improved. And the population is promotedto evolve towards the optimal solution direction, so that the problem that sparrows are easy to fall into local optimum in the convergence process of low fitness in the sparrow search algorithm is avoided to a certain extent. The ability of the improved sparrow search algorithm to jump out of the local optimum is enhanced, and the convergence speed and precision of the RBF network optimized by theimproved sparrow search algorithm are further improved.

The invention relates to a two-stage scheduling method of parallel test tasks facing a spacecraft automation test, which belongs to the field of parallel tests. The method comprises the following stages: in the first stage, the test tasks, task instructions and tested parameters are analyzed and determined, a constraint relation between the tasks is defined, a time sequence constraint matrix and a parameter competitive relation matrix are established, the tasks and the constraint relation between the tasks are changed into undirected graphs, a parallel task scheduling problem is changed into a minimum coloring problem in the sequence of the tops of the graphs, a method based on the combination of a particle swarm and simulated annealing is used for solving, and then a test task group with the maximal degree of parallelism is obtained; in the second stage, the obtained test task group with the maximal degree of parallelism is distributed on limited test equipment, and then an optimal scheduling scheme is obtained. According to the two-stage scheduling method, the constraint relation among a plurality of test tasks is quickly established, the independence between the test tasks is analyzed, the degree of parallelism of the test tasks is increased, the optimal scheduling of the tasks on the equipment is realized when constraint conditions are satisfied, and the test efficiency is improved.

The present invention discloses a multi-parameter multi-object chaotic particle swarm parameter optimization method. The method comprises the steps of (1) determining a target function and a parameter to be optimized, (2) initializing an algorithm, (3) calculating the target function corresponding to each individual in a population, (4) updating an individual history optimal solution, (5) updating a particle velocity and position, (6) updating a global optimal solution set, (7) updating a global optimal solution, and (8) carrying out result judgment. Compared with a common random initialization method and an existing chaotic logistic mapping particle swarm initialization method, according to the method of the invention, the performance of global optimization is improved and the stability is good, compared with a common multi-object weighted optimization method, the Pareto optimal solution technology is employed by the method, and the problem of difficult weight selection in the multi-object method is solved.

The invention discloses an improved fuzzy neural network bus intelligent scheduling method based on a chaos theory, and belongs to the field of intelligent transportation. According to the improved particle swarm bus intelligent scheduling method based on the chaos theory, advantages and complementarity of various algorithms are fully utilized, a series of improvement measures are also introduced, such as conjugate gradient optimization, and inertia factor and constraint factor of the particle swarm algorithm etc., the mechanism and the search performance are researched from the theoretical and practical perspectives, problems of poor global search capability and premature convergence of the conventional optimization algorithm are fundamentally solved, the diversity of population can be obviously increased, the global search capability is obviously improved, the problem of fuzzy information can be effectively dealt with, the convergence speed is fast, and a new high-efficiency method is provided for bus intelligent scheduling.

The invention relates to the technical field of wind power prediction, and discloses a wind power short-term prediction method. The method uses wind speed as an input, adopts a regression model of a least square support vector machine to predict output power of a wind power plant, and parameters of the regression model of the least square support vector machine are optimized by adoption of a chaotic particle swarm algorithm. The wind power short-term prediction method provided by the invention introduces chaotic motion characteristics into an iterative process, uses ergodicity of chaotic motion to improve a global searching capability of the algorithm in a searching process, overcomes the defects that the particle swarm algorithm is easy to fall into a local extreme point and is slow in convergence and low in precision in a later period of evolution, effectively solves the problem of prematurity of the particle swarm algorithm, can ensure global optimum, and achieves a better prediction effect; the method uses the least square support vector machine to predict, avoids the problem of solving quadratic programming, converts the prediction problem to a process of solving a linear equation set, and the solving process is greatly simplified; and the method adopts single wind speed as input data, and thus a prediction model is simpler.

The invention discloses an aquaculture water quality short-time combination forecast method on the basis of multi-scale analysis. The method includes the steps that water quality time sequence data are acquired online and repaired; through empirical mode decomposition, the selected water quality time sequence sample set data are decomposed into IMF components and residual rn components, wherein the IMF components and the residual rn components are different in frequency scale; the IMF components and the rn components are classified, a manual bee colony optimization least square support vector regression machine, a BP neural network and an autoregressive sliding average model are respectively selected for forecast according to classifying features, and finally, all results are weighed and summed to obtain a water quality time sequence forecast result. According to the method, the original water quality time sequence data are decomposed into the components different in time frequency through the empirical mode decomposition, and change conditions in original water quality sequences can be mastered more accurately; advantages of the manual bee colony optimization least square support vector regression machine, advantages of the BP neural network and advantages of the autoregressive sliding average model are complemented and combined, and thus performance of a combined forecast model is effectively improved.

The invention relates to a temperature compensation method and system for a silicon micro-accelerometer based on an improved PSO (Particle Swarm Optimization) optimized neural network, and the temperature compensation method and the temperature compensation system are designed for improving temperature compensation precision. The method comprises the following steps: acquiring a training sample ofPSO optimization and a BP (Back Propagation) neutral network; constructing the BP neutral network on the basis of the training sample; using an optimal extreme point optimized by an adaptive weight PSO as an initial weight value and a threshold value of a BP neutral network model; introducing mutation operation into a PSO algorithm, updating the particles, then reinitializing the particles at a certain probability and expanding a population search space which is continuously reduced in iteration by the mutation operation; establishing the BP neutral network by calling the parameters, realizing real-time temperature compensation of the silicon micro-accelerometer and outputting a compensation result. The temperature compensation method and the temperature compensation system disclosed by the invention have the advantages that the problems of solving of an optimal compensation result and temperature globality are solved, and finally improved compensation precision and global improvementof the silicon micro-accelerometer are realized.

The invention relates to a PID controller parameter optimizing method, and especially discloses a PID controller optimizing design method based on a particle swarm membrane algorithm. The method comprises that an optimal particle is obtained from a swarm, different dimension values of the particle are successively assigned to to-be-optimized parameters Kp, Ki and Kd, a control system model is operated, performance indexes output by the system are obtained, and it is determined whether the evolution rule is used again to search for the optimal particle so that the optimal control effect can be obtained. The PID controller optimizing design method combines membrane computing with traditional PSO (Particle Swarm Optimization), fully utilizes the partition function and transport communication rules, can obtain a set of more proper PID control parameters, and finally enables a controlled system to achieve the optimal control effect. According the PID controller optimizing design method, the controlled system can obtain the set of more proper PID control parameters and achieves the optimal control effect under the condition that the controller structure is not changed.

The invention discloses a reconfigurable assembly line sequencing method based on an improved genetic algorithm. The method comprises the following steps of: determining a population size according to a minimum production cycle of a reconfigurable assembly production line, and executing genetic encoding according to a standard of taking a chromosome as a full array of all tasks; calculating the idleness of the minimum reconfigurable assembly line, the quantity of unfinished work, the uniform parts use rate and the minimum production adjustment cost of the individual; executing a grading operation, executing a Pareto solution set optimization filtering operation, calculating the fitness of each grade, executing genetic operations according to the fitness, executing an elite reservation strategy, and obtaining a Pareto optimal solution set and a corresponding objective function value by judging whether convergence is realized or the pre-set maximum number of iteration is achieved. In the method, three major factors influencing the optimized sequencing of the reconfigurable assembly line are comprehensively considered, a plurality of technologies are used in the genetic operation, population diversity is ensured, algorithm prematurity is avoided, and global optimal search ability of the algorithm is enhanced.

The invention discloses a building load forecasting method and device based on an improved IHCMAC (Hyperball Cerebellar Model Articulation Controller) neural network model. The method comprises the steps of: simulating the actual operation of a building to obtain building cold / heat load data and influencing factor data; determining input variables of the model according to the degree of correlation between the influencing factors and the building cold / heat load; clustering the input variables according to a particle swarm-K mean clustering algorithm to obtain values of L clustering centers, i.e., model node values, and defining a Gaussian kernel function for each node; and updating the weights of the nodes via a weight training algorithm to obtain a building load forecasting value of the model. The method has the advantages of fast convergence, high learning precision and strong generalization ability, and can provide a decision basis for energy-saving optimization control of a building system.

The invention discloses an Internet of Things intrusion detection method based on machine learning, and belongs to the field of Internet of Things safety. The method comprises the following steps of preprocessing data, dividing a data set and carrying out data dimension reduction, constructing a least squares support vector machine, carrying out sparse processing on the least squares support vector machine, forming a base classifier, constructing a base classifier based on a neural network, carrying out intrusion behavior detection and carrying out prediction experiments. According to the method, the computational complexity is reduced by adopting a least squares support vector machine algorithm, a pruning technology and the like; an improved evolutionary strategy optimization model is adopted to get rid of extreme points, the optimal effect of the model is achieved, and the judgment accuracy can be improved. The method has the characteristics of small computational amount, low false alarm rate and high detection accuracy, and is suitable for intrusion detection in the Internet of Things.

The invention discloses a method for selecting a multi-objective immune optimization multicast router path, which mainly solves the problem of the optimization of multicast routers. The method has the following steps of: (1) determining an optimized target to generate a network model, and setting running parameters to generate an initial population; (2) eliminating an individual path loop; (3) calculating the individual target value to generate a current non-dominant population; (4) judging finishing conditions, if the finishing conditions are met, the current non-dominant population is output, or else, carrying out step (5); (5) calculating the individual crowding distance of the current non-dominant distance to generate an active population; (6) carrying out cloning and local searching operation on the active population; (7) carrying out recombination, variation and local search operation on the cloned population; (8) combining the current non-dominant population with the populations obtained in the steps (6) and (7) to eliminate the individual path loop; (9) calculating the individual value to refresh the current non-dominant population, and carrying out the step (4). The invention has the advantage of providing a flexible optimization scheme and is suitable for selecting the multicast router path.

The invention discloses an improved particle swarm optimization (PSO) algorithm of solving the zero-waiting flow shop scheduling problem. Firstly, parameter initialization and population initialization are carried out, wherein initial workpiece sequences are generated, then a factorial encoding method is used to map all permutations to integers to form an initial population, and finally, a feasible initial velocity set is randomly generated; particles are moved; the population is updated through an original PSO population updating strategy, a new population is mapped to corresponding workpiecesequences, and work completion time of each new workpiece sequence is evaluated; an improved variable neighborhood search (VNS) algorithm is used for a local search, and results obtained by the search are used for replacement; a population adaption (PA) operator is used to increase diversity of the population; and checking of a termination condition is carried out, if the termination condition ismet, a process is stopped, and values of variables and corresponding sequences are returned to be used as a final solution, and otherwise, particle velocity is continuously updated. The method has the advantages of improving a particle swarm optimization algorithm, improving global search capability, and avoiding too early convergence.

The invention discloses an electric vehicle photovoltaic charging station optimization scheduling method considering a user behavior, that is, an electric vehicle supplies power to a power grid only when an electricity price is higher than the discharge loss of a vehicle-borne storage battery. Therefore, according to measured data, a B-spline curve is used to establish mathematical models of the influence of a discharge depth and an ambient temperature on the cycle life of a storage battery through the two steps of preliminary fitting and partial correction, the influences of a discharge depthfactor and a temperature factor on the cycle life of the storage battery are comprehensively considered, and the discharge loss corresponding to each discharge behavior of the vehicle-borne battery is obtained. On the basis, with the output of an energy storage system and the interaction power with a large power grid as the optimization variables, with minimum operating cost of the system as an optimization goal, the day-to-day optimal scheduling model of the system is established, and the model is solved by using an adaptive genetic optimization algorithm. The invention has a certain significance for extending the service life of the electric vehicle storage battery and promoting the development of renewable energy.

The invention discloses an auto-disturbance rejection position servo system optimization design method based on an improved CPSO. By aiming at problems of permanent magnet synchronous motor servo systems on high position control precision, fast response, and stable performance, a double-loop control structure is adopted, and a PMSM auto-disturbance rejection position servo control system is established. By aiming at a parameter setting problem of an auto-disturbance rejection position controller, the improved Chaos Particle Swarm Optimization (CPSO) is provided. By adopting the CPSO, a position of a particle is initialized according to cubic chaotic mapping, and an index self-adaptive way having adjustable parameters is used to adjust inertia weight in a non-linear way, and at the same time, the position of the particle is updated by adopting a chaos and stability alternate way, and therefore the convergence rate and the global optimization ability of the CPSO are effectively improved, and the CPSO is used for the optimization of the auto-disturbance rejection position controller parameters. By combining with a fitness function including position control requirements, the optimization design of the PMSM position servo control system is realized, the position control precision and the response speed of the servo system are improved, and a strong disturbance rejection ability is provided.

The invention proposes a traffic flow prediction method based on firefly algorithm and RBF neural network. The method comprises: performing normalization to the sample data so that the input data and output data are on the same order of magnitude; initializing the firefly algorithm parameters; utilizing the random method to initialize the firefly populations and encoding each individual in the populations; using the firefly algorithm to train the RBF neural network to obtain the best individual in the populations; decoding the best individual in the populations to obtain the trained RBF neural network; and utilizing the trained RBF neural network to predict the traffic flow data sample. Compared with the traditional traffic flow prediction method, the method of the invention makes full use of the advantages of the firefly algorithm in the RBF neural network training so that the RBF network possesses a more accurate prediction capability, achieves even faster training efficiency and better generalization capability. The invention belongs to the traffic transportation information engineering technology field and can be used for the predictions of road traffic flows in an intelligent traffic system.

The invention discloses a gray level image segmentation method based on multi-objective fuzzy clustering, relating to the technical field of image processing and mainly solving the problem of lower accuracy rate of gray level image segmentation. The gray level image segmentation method comprises the steps of: after graying an image, randomly generating a plurality of clustering centers according to a generated grey level histogram, and constituting the clustering centers into a parent antibody population. The gray level image segmentation method is characterized in that a dense separation effectiveness function as an evaluation criteria is combined with a fuzzy optimization function in a fuzzy C-mean value method to form a multi-objective optimization problem, the whole parent population is iterated for multiple times by adopting an immune clone multi-objective evolutionary algorithm, simultaneously searched from multiple directions, and calculated in parallel so as to finally acquire an optimum clustering center, and a classifying result is output. Therefore, the detail information in the gray level image is effectively reserved, the wrong fraction is reduced, the gray level image segmentation precision is improved, and a good platform is provided for subsequent operation of gray level image segmentation. The gray level image segmentation method can be used for extracting and obtaining the detail information of the gray level image.

The invention discloses a closed loop control method comprising a multi-microgrid power distribution network. A micorgrid is subjected to equivalence processing to establish a power distribution network closed loop optimization control model which takes the microgrid into the account; active and reactive output of the microgrid is used as a control variable; the minimum voltage difference of two sides of a switching-on point is used as a control target; a constraint condition comprises power balance constraint, node voltage upper and lower limit constraint, the limiting value of the active and reactive output of the microgrid and the maximum allowing tide constraint of a line; the genetic algorithm of a belt sparse block migration strategy is combined to carry out optimization regulation on the operation state of each microgrid; and the voltage difference of two sides of a closed loop point can be effectively reduced so as to reduce closed loop current and improve the success rate of the loop closing operation. The closed loop control method has the advantage of comprehensive model, the microgrid controllability can be fully utilized, and the theoretical basis can be provided for the thermal conductivity loop closing operation after the microgrid is accessed into the power distribution network.