38 results about "Dominance relation" patented technology

The invention discloses a method and system for multi-target reactive power optimization of electric power systems. The method comprises the following steps of: establishing a multi-target reactive power optimization model; generating positions of N initial bird nests by utilizing Kent chaotic mapping, taking the positions of the N bird nests as initial populations, calculating a fitness value of each bird nest, establishing an external file set according to a Pareto dominance relation, updating the positions of the bird nests according to self-adaptive weights, updating the external file set according to the dominance relation and calculating a congestion distance to control the capacity of the file set; carrying out a differential evolution operation on each bird nest and updating the external file set; and when an iteration termination condition is satisfied, outputting an optimum Pareto optimal solution set. According to the method and system, a plurality of target functions are considered, so that the disadvantages that the traditional method is used for converting a plurality of targets into a single target and is difficult to determine the weight coefficients are optimally overcome; an improved cuckoo search algorithm is high in convergence rate, high in precision and good in individual diversity; and the obtained optimal solution set has favorable diversity and uniform distributivity, and can be well adapted to solving the multi-target reactive power optimization problems of the electric power systems.

The invention discloses a power system multi-target differentiation planning method based on an improved harmony search algorithm. The method is based on the differentiation planning basic principle and requirement, economical efficiency and reliability are quantized according to additional cost and lost benefits in the whole life cycle, and an optimized multi-target differentiation planning model is built; an optimizing criterion is formulated according to the Pareto dominance relation, and an optimal differentiation planning scheme is selected from a Pareto solution set with the maximum benefit-cost ratio as the goal. The newly-developed harmony search algorithm is applied to solution of the model, chaotic mapping, dynamic parameter setting, improved pitch adjustment strategies and a harmony optimizing information sharing mechanism are introduced to solve the 0-1 planning problem, and search performance is improved. According to the method, economical efficiency and reliability can be comprehensively given into consideration, multi-target differentiation planning of a power grid is achieved, and the method is of guidance significance in building a core backbone net rack and building a strong power grid.

The invention relates to an azimuthal anisotropic elastic impedance based crack detection method. With a Connolly elastic impedance equation and a Ruger equation serving as the basis, anisotropic elastic impedance and crack parameters and the dominance relation formula between crack density and crack azimuth are deduced. A set of elastic impedance based crack inversion process is established, and anisotropic parameter information is added into inversion well logging constraint. Numerical model testing is performed in the method, the inversion result is roughly identical to the theoretical value, and the inversion density shows high noise immunity. The method has the advantages that anisotropic elastic impedance can be used, the crack density and the crack azimuth are calculated simultaneously, good application effects are provided in actual data, and high practicality is provided.

The invention discloses a hyper-heuristic algorithm-based satellite task planning method. The method includes the following steps that: 1, the priority dominance relation data of a target are obtained; and 2, a tabu search algorithm or a simulated annealing sub-heuristic algorithm is adopted to search priority combinations so as to obtain a planning result. According to the method of the invention, the sub-heuristic algorithm is adopted to search the combined application of the heuristic rule information of multi-angle priorities, and therefore, a super-heuristic algorithm-based and efficiently robust satellite imaging and transmission joint planning method can be provided.

The invention discloses a multi-objective optimization scheduling method suitable for coastal region water resources. The method comprises the steps of generalizing a water resource system according to the characteristics of a coastal region; determining an objective function and constraint conditions, and establishing a multi-water-source multi-objective multi-user coastal region water resource optimization scheduling model; using a multi-objective particle swarm optimization algorithm to solve a multi-objective optimization problem based on Pareto dominance, and constructing a non-inferior solution set of a coastal region water resource optimization scheduling model by judging a dominance relation; and acquiring an optimal scheme by taking the minimum Euler distance from the ideal solution as the criterion. According to the method, the current situation of engineering type and water quality type mixed water shortage in the coastal region is taken as the background; The invention provides an optimal scheduling method for combined water supply of a plain reservoir, a plain river network and a long-distance water transfer project, which conforms to the characteristics of the coastalregion, realizes a water resource scheduling pattern of quality-based water supply and excellent water utilization, and has practical significance for improving the situation of water resource shortage of the coastal region.

The invention discloses a dynamic Skyline query method based on cloud computing. The method is used for performing global Skyline lattice computing for dynamic Skyline query of mass data. The whole computing process is a DAG execution process, and obtained data points serve as a candidate set for dynamic Skyline computing; during dynamic Skyline computing, the data points in the candidate set are scanned, and the data points are converted into coordinates to convert dynamic Skyline computing into static Skyline computing with a query point being an origin of coordinates; in the DAG execution process, a Tez-based MapReduce operation is started, a Map function is utilized for parallel performance in all nodes, and the result is cached; next, another MapReduce operation is started to check the Skyline lattice dominance relation among the data points after conversion, Skyline computing is performed, and the result is returned. A polling mode is adopted to perform global Skyline lattice computing, Skyline computing is performed after effective pruning, and therefore a large amount of computing is saved.

The invention provides a method for computing multi-subspace Skyline queries and belongs to the orientation of data management and queries in the computer field. According to the method, firstly, in case of co-existence of multiple subspace Skyline queries in a database system, a sub-space cubic group structure is designed, and on the basis of the structure, a computation method capable of simultaneously processing multiple subspace Skyline queries, namely an MSSCA algorithm, is designed; the algorithm is capable of effectively solving the multi-subspace Skyline query problem. In the implementation process of the algorithm, a method of sharing Skyline result sets of child spaces is utilized thoroughly to directly put points that must be Skyline results of a parent space into the result set, and therefore, the times of judgment is reduced; besides, the algorithm is also capable of further reducing the times of dominance relation judgment by use of such methods as maximum value pruning and summating filtering; as a result, the efficiency is effectively improved. The method disclosed by the invention is capable of dealing with co-existence of multiple subspace Skyline queries in the database system and guaranteeing the efficiency of the algorithm by use of a series of sharing and filtering methods; in short, the method has a great practical application value.

The invention discloses a Pareto set individual ranking method aiming at the high-dimensional multi-objective optimization problem. According to the method, when the qualities of population individuals are compared, a new reference point population is introduced; by comparing the Pareto dominance relation between the population individual and a reference point, the quality relation among the population individuals, namely the individual fitness degree, is indirectly obtained; meanwhile, the reference point also obtains a fitness degree value according to the dominance relation between the reference point and the population individuals; therefore in the process of designing and evolving a multi-objective optimization algorithm, the individual population and a reference point population are subjected to concerdent evolution and are mutually promoted, and eventually the convergence of the algorithm is improved; the experience shows that the method is still effective in the high-dimensional multi-objective optimization problem.

The invention relates to a method for obtaining an optimal scheduling scheme for processing a part based on an improved multi-target Grey Wolf algorithm. The method comprises the following steps: firstly carrying out formalization description of mathematical signs on production scheduling problems of a multi-target operation workshop; then building constraint conditions to be met in an optimization process; then determining targets to be optimized and building a corresponding multi-target optimization function; then designing the improved multi-target Grey Wolf algorithm based on combination of a mean Frechet distance similarity matching principle and a Pareto dominance relation; and finally carrying out iterative operation and outputting an optimal scheme set. Through the method, the problems of low solving efficiency and poor searching performance caused when solving scheduling of the multi-target operation workshop by only using a Grey Wolf algorithm in the prior art can be solved.

The invention discloses a smart medical call query system based on dynamic Skyline query in a mobile cloud computing environment and belongs to the field of smart medical and space big data processing. A distributed dynamic Skyline algorithm based on Spark is employed; after users send data requests to a cloud center service system through a mobile smart client, the cloud center service system collects individual multi-factors requests of the users, then extracts data from a big data set of an existing medical mechanism, filters and sorts the data according to space positioning and selected multiple attributes of the users, thereby obtaining an ordered medical space result data set, then scans and compares dominance relations of data points to obtain dynamic Skyline and inverse Skyline results, and returns the results to corresponding terminals. In a medical multi-dimensional big data query process, medical space big data control is carried out through space grid pruning and continuous network medical data monitoring, so the corresponding medical mechanism data required by the users can be obtained rapidly.

The invention discloses an aluminum electrolysis preference multi-target optimization algorithm based on the angle dominance relation. Firstly, modeling is conducted on the aluminum electrolysis production process through a recurrent neural network; then, an expected target value is set by a decision maker; and then, a production process model is optimized through a preference multi-target quantumindividual group algorithm, and a set of optimal solutions, meeting expectation of the decision maker best, of all decision variables and the current efficiency, the bath voltage, the perfluorocompound emission amount and per-ton aluminum energy consumption which correspond to the optimal solutions are obtained. Through variation, crossing and selecting operation in a differential evolution algorithm, the decision variables are subjected to preference optimizing, thus the optimal value of technological parameters in the aluminum electrolysis production process is determined, the current efficiency can be effectively improved, the bath voltage is lowered, the greenhouse gas emission amount and per-ton aluminum energy consumption are reduced, and the purposes of energy conservation and emission reduction are achieved while preference of the decision maker is met.

The invention provides an aero-engine assembly task priority ordering method. The method includes the steps that firstly, according to a correlation dominance relation of task attributes, an attribute hierarchical model is established, indexes are quantitatively expressed, and after the attribute value of each task is processed, a standardized decision matrix is obtained; secondly, when index weights are determined, various weight calculation methods exist, on the basis, a multi-type weight balancing method with the minimum sum of the deviation square and the maximum sum of weighing index values serving as targets is raised, and therefore balance weights of the indexes are obtained; finally, the balance weights and the standardized decision matrix are used, the improved TOPSIS method with the Euclidean distance replaced by the contact distance is adopted for ordering row vectors in the weight standardized decision matrix, so that a priority order result of the tasks is obtained. According to the method, multiple types of weight information is comprehensively considered and balanced, information loss caused by single-type weight assignment can be reduced, and meanwhile, the improved TOPSIS method can overcome the defects of a traditional method.

The invention discloses an alliance relationship service combination selection system and method, and belongs to the field of computer services. The system comprises service basic particle layer modules: a service request input module, a resolver module, a service information and function index module and a function selection module; service business particle layer modules: a service finding module, a parameter setting module, an index module, a service information extraction processing module, a parameter and service information association module, a smart evolution algorithm optimizing module, and the like. The selection method comprises the following steps: generating a service alliance constraint relationship; performing redundancy elimination and equivalence partitioning on alliance relationship data; determining an alliance relationship among combination services according to a found dominance relation and an equivalent set, and solving a fitness value; and solving a problem by using a swarm intelligence optimization method. Through adoption of the alliance relationship service combination selection system and method, the efficiency in getting an optimal combination way is high, and the selection scheme is optimal.

The invention provides a method and device for evaluating SaaS service quality. The method includes: constructing the stepped hierarchical structure of an evaluation system according to a dominance relation between different elements of the evaluation system and a relation between quality evaluation indexes in the evaluation system; acquiring a scale value for comparing the importance of other elements dominated by a certain element in pairs in the stepped hierarchical structure according to a preset rule, and forming a judgment matrix according to the scale value; and calculating the importance degree of an element to be required by using a normalized feature vector corresponding to the maximum characteristic root of the judgment matrix, and determining the service quality of the elementto be required according to the importance degree of the element to be required. The method and device for evaluating SaaS service quality solve a technical problem that a method for evaluating SaaS service quality in the prior art is too wide in evaluating criteria, poor in establishment integrity and systematicness of an index system, and inaccurate in service quality evaluation.

A method for generating and optimizing physical system architecture by modeling a problem in the form of a model. The physical architectures (AP₁, AP₂, . . . , AP_N) are generated using the model and algorithm that searches for a function with viable and valid combination of components. The physical architectures are evaluated according to a plurality of attributes or criteria using analysis modules based on the model. A part of the physical architectures is selected based on dominance relations. New physical architectures (AP′₁, AP′₂, . . . , . . . , AP′_N) are generated by applying genetic operators to the physical architectures (AP₁, AP₂, . . . , AP_N). The results are synthesized by retaining a part of the physical architectures.