14results about How to "Improve solution quality" patented technology

The invention relates to the technical field of hydropower dispatching, and discloses a multi-agent water-wind-light short-term power generation optimization dispatching method considering space-time coupling constraints. Firstly, on the basis of drainage basin characteristics, a hierarchical decomposition strategy is adopted, and overall centralized optimization solution of the water-wind-light complementary power supply system is converted into partial multi-agent collaborative optimization scheduling, so that the solution efficiency is improved; secondly, in combination with a multi-agent and unified optimization operation theory, constructing a double-layer nested short-term water consumption minimum model considering wind power and photovoltaic power prediction output, so as to realize intelligent modeling; and finally, an improved alternating direction multiplier method is adopted, and interaction among multiple agents is coordinated based on a modification strategy of a flow balance and electric quantity matching principle, so that the solving quality is improved. According to the method, the solving efficiency and precision can be effectively improved, and a solid theoretical basis and a feasible thought are provided for short-term power generation dispatching of a large-scale water-wind-light complementary power supply system.

The invention discloses a robot task allocation method based on an improved multi-target genetic algorithm, and relates to the technical field of robot task allocation, and the method comprises the following steps: building a multi-target robot task allocation problem model, and initializing model parameters; and optimizing the multi-target robot task allocation problem model by adopting an improved multi-target genetic algorithm to obtain a task allocation result. According to the method, the population is divided into a plurality of sub-populations, improved crossover mutation operation, population knowledge sharing interaction operation and fusion selection operation are introduced, an improved multi-target genetic algorithm is formed and applied to optimization of a multi-target robot task allocation problem model, and the problems that an existing robot task allocation technology is poor in solving quality and high in efficiency can be effectively solved. The solving speed is slow, and the diversity of solution sets is poor.

The invention discloses an underwater wireless sensor network target coverage deployment method based on submarine topography, and the method comprises the steps: considering the underwater wireless sensor network target coverage problem of submarine topography, and constructing an integer nonlinear programming model INLP; and solving the integer nonlinear programming model INLP by adopting a Harris eagle optimization algorithm. According to the method, the submarine topographic features are creatively combined, the loss of the underwater acoustic signal is calculated by adopting the underwater acoustic simulator BELLHOP3D, and the transmission success rate of the link is further derived, so that the transmission quality of the underwater communication system is effectively improved. By constructing the integer nonlinear programming model, the deployment positions of the underwater sensor nodes are optimized, the coverage rate and the uniformity are maximized, and the problem that the influence of complex submarine topography on signal transmission cannot be fully considered in a traditional underwater network deployment method is solved.

The invention discloses an emergency logistics two-stage path optimization method based on truck-unmanned aerial vehicle cooperation, and relates to the technical field of emergency logistics path optimization, the method is based on a truck-unmanned aerial vehicle cooperation two-stage path optimization algorithm, the method is composed of a self-adaptive K-means clustering algorithm and a variable neighborhood simulated annealing algorithm, the calculation efficiency is high, the solving quality is high, and the method is suitable for large-scale popularization and application. And the method has a relatively high practical value for the emergency logistics transportation optimization problem with relatively strict timeliness requirements. The method has high universality and transportability, can better deal with the scene that the road is interrupted and emergency materials are affected to arrive in time under the emergency situation, can provide reference for the government and decision consultation department to formulate an emergency logistics transportation mode, improves the emergency rescue efficiency, and ensures the credibility of decision.

This invention belongs to the field of dynamic path optimization and discloses a dynamic path optimization method for rider-drone collaborative delivery based on order bundling. It includes constructing a dynamic path optimization model with the objective function of minimizing platform costs and customer satisfaction reduction costs. Platform costs include drone dispatch costs, drone delivery costs, rider dispatch costs, rider delivery costs, and rider salaries. The total delivery optimization period is divided into several order delivery plan re-optimization cycles using time windows. At the beginning of each order delivery plan re-optimization cycle and upon the arrival of a new order, an adjustment to the order delivery plan is triggered. This invention improves the delivery system's responsiveness and overall efficiency in dynamic environments by designing a dynamic optimization mechanism that considers order bundling and a two-stage order bundling strategy. Sensitivity analysis, comparative experiments, and ablation experiments based on simulation examples of different scales are conducted to verify the effectiveness of the proposed method.

The application relates to the cross technical field of quantum computing and nuclear fusion plasma profile reconstruction, in particular to a tokamak plasma current density reconstruction system based on a coherent Ising machine. The technical scheme comprises an electromagnetic diagnosis data acquisition unit and a Bayesian posterior modeling processor. The electromagnetic diagnosis data acquisition unit is used for collecting external electromagnetic measurement data of a tokamak device in real time to form a measurement vector. The Bayesian posterior modeling processor is connected with the electromagnetic diagnosis data acquisition unit and is used for constructing a posterior precision matrix and a linear term coefficient vector based on a conditional autoregressive prior model and electromagnetic measurement likelihood probability. The application organically fuses Bayesian inference modeling, spatial block decomposition, block coordinate descent iteration control and coherent Ising machine quantum solution, realizes high-precision, uncertainty-quantized and rapid reconstruction of a tokamak plasma current density profile under the constraint of existing quantum hardware bit capacity.

The invention discloses a high-speed rail passenger and freight train working diagram optimization method, system and device, a medium and a product, and the method comprises the steps: firstly determining an optimization target, including minimizing the total travel time of a freight train, the operation deviation of the passenger train and the cargo delivery delay time; thirdly, constructing a mathematical model, and defining decision variables, an objective function and constraint conditions to form a high-speed railway passenger and freight train working diagram optimization model; on the basis of the model, a rolling time domain decomposition strategy is adopted, and a global scheduling problem is segmented into a local optimization problem of a plurality of time windows. And for each time window, obtaining a local optimal solution by using a linear programming solver so as to ensure that the dispatching of each train accords with the set constraint. Then, global search is carried out on the local solutions through a simulated annealing algorithm, the departure time and the operation plan of the train are optimized, and local optimum is avoided. And finally, a variable neighborhood local optimization method is applied, and the quality of the whole scheme is further improved by finely adjusting the solution.

The invention discloses a data set construction method, electronic equipment and a storage medium. The method comprises the steps of obtaining a preset open source model and a plurality of original data sets corresponding to a plurality of to-be-solved problem types; determining a plurality of initial supervision data sets corresponding to the plurality of original data sets and a plurality of problem word vector representations corresponding to the plurality of original data sets based on the plurality of original data sets and a preset open source model; cleaning the plurality of initial supervision data sets to obtain a plurality of intermediate supervision data sets; determining a plurality of to-be-mixed data sets in the plurality of intermediate supervision data sets based on the plurality of problem word vector representations; and merging the plurality of to-be-mixed data sets to obtain a target supervision data set. By utilizing the technology provided by the invention, high-quality supervision data can be formed to perform supervision fine tuning, and the robustness and generalization ability of a problem solving model are improved.

The invention discloses an aviation composite material solidification scheduling method and system considering multilayer space and time window constraints, and the method aims at multilayer board equipment space constraints, workpiece time window restrictions and batch processing demands in aviation composite material solidification production, and minimizes the maximum completion time as a target function. A single-machine batch scheduling model fusing multilayer space placement, laminate capacity and time window constraint is constructed, and meanwhile, an evolutionary algorithm combined with adaptive neighborhood search is adopted for solving; the improved strategies such as population initialization with time compatibility and random combination, a multi-dimensional neighborhood search operator and double-layer self-adaptive neighborhood selection are designed according to the characteristics of the composite material solidification scheduling, the global search and local optimization capability of the evolutionary algorithm is improved, and the solving quality and efficiency of the composite material solidification scheduling under the complex constraint are remarkably improved. The method can be widely applied to complex manufacturing scenes with multi-layer processing space, time window constraint and batch processing characteristics, such as aviation composite material forming and heat treatment furnace scheduling.

The application discloses a kind of considering multi-layer space and time window constraint's aviation composite curing scheduling method and system, the method is for the space constraint of multi-layer board equipment in aviation composite curing production, workpiece time window limit and batch processing demand, with minimizing maximum completion time as objective function, construct the single machine batch scheduling model of fusion multi-layer space placement, layer board capacity, time window constraint, while solving by using the evolutionary algorithm of combining adaptive neighborhood search, population initialization of time compatibility and random combination is designed for composite curing scheduling characteristics, multi-dimensional neighborhood search operator, double-layer adaptive neighborhood selection and other improvement strategies, improve the global search and local optimization ability of evolutionary algorithm, significantly improve the solution quality and efficiency of composite curing scheduling under complex constraint.The application can be widely applied to aviation composite forming, heat treatment furnace scheduling and other complex manufacturing scenarios with multi-layer processing space, time window constraint and batch processing characteristics.

The application discloses a kind of two-way feedback type's warehouse location planning method and system, comprising: constructing multi-objective optimization model, determine weight space and variable space;Optimize weight vector is searched in weight space using particle swarm algorithm and is passed to genetic algorithm;Optimal warehouse location allocation and path sequence are searched in variable space using genetic algorithm;Elite solution set is extracted from optimal variable population and evaluated, generates the first feedback information from variable space to weight space;The statistical characteristics of current weight particle swarm are analyzed, generate the second feedback information from weight space to variable space;According to the search direction of first feedback information adjustment particle swarm algorithm, according to the second feedback information adjustment crossover probability and mutation probability of genetic algorithm;The history optimum of particle swarm and genetic algorithm is updated, records global optimal solution pair;Repeat iteration until convergence, output global optimal solution.The application improves the adaptability and robustness of warehouse location planning to dynamic environment by two-way feedback cooperative evolution.

The invention belongs to the technical field of optimal control of a pressure delay osmotic energy-photovoltaic-solid oxide fuel cell and wind energy hybrid energy system, and particularly relates to an optimal control method for a hydrogen-electricity coupling system based on improved giant armadillo optimization. Based on a giant armadio optimization algorithm and a triangular topology aggregation optimizer, a key parameter, namely a random solution, is introduced in an exploration stage, an optimal solution is removed, the influence of a current solution on position updating is reduced, the convergence speed and the global search capability of the algorithm are improved, the solving quality is greatly improved, and the algorithm is more effective than a classical GAO algorithm, and has a wide application prospect. The real-time tracking and accuracy based on the IGAO algorithm are improved, and when the MPPT control problem is solved, the method has higher calculation accuracy and shorter operation time, can be used for a pressure delay osmotic energy-photovoltaic-solid oxide fuel cell and wind energy hybrid energy system to obtain higher power generation, and is beneficial to the development of the energy field.

The invention discloses a contract maximum demand optimization method based on user side energy storage, relates to the technical field of contract maximum demand optimization, and solves the technical problem that in the prior art, when a user sets the maximum demand of a contract, a set value is often not accurate enough because the user depends on previous use experience and lacks a relatively scientific and systematic decision basis. The method comprises the following steps: acquiring energy storage data related to user side energy storage; constructing a double-layer mathematical model based on the energy storage data; wherein the double-layer mathematical model comprises an upper layer model and a lower layer model; solving the upper-layer model based on a golden section method to obtain a solution of the upper-layer model; solving the lower-layer model based on a Lagrange relaxation method to obtain a solution of the lower-layer model; and based on the solution of the upper-layer model and the solution of the lower-layer model, the maximized monthly income is obtained through calculation, and the technical problem is solved.