6results about How to "Improve solution quality" patented technology

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 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.