45 results about "Quantum evolutionary algorithm" patented technology

The invention discloses a short-term wind power prediction method in the technical field of wind power prediction. Wind speed information and corresponding wind power data are acquired in 30 hours before a prediction day, and noise elimination treatment is carried out to the collected initial data by ridgelet transform, so that a sample set is formed by the processed data; an optimal sample is selected by quantum-inspired evolutionary algorithm; and the selected optimal sample is used as a training sample of a direct-push support vector machine for carrying out training, and the wind power prediction is carried out by utilizing the trained direct-pushing support vector machine. The short-time wind power prediction method has better adaptability and higher detection precision in the aspect of predicting short-term wind power.

Provided is a protein conformation space optimization method based on quantum evolutionary algorithm. The protein conformation space optimization method comprises the following steps: based on a framework of quantum evolutionary algorithm, with Rosetta Score3 as the optimum objective function, based on an amino acid sequence coarse-grained expression model, converting an energy calculation model into a dihedral angle optimization space energy model; encoding a dihedral angle individual expression of the amino acid sequence by means of real phase encoding; improving prediction precision by implementing the operation of quantum mutation through fragment assembly; by adopting quantum rotation gate, quantum updating individual population to achieve the purpose of partial adjusting the angle; through iterative evolutionary process, the algorithm will produce protein conformation with lower energy and reasonable structure. The protein conformation space optimization method has the advantage of quick acquisition of conformation of high prediction precision in the application of protein structure prediction.

The invention relates to the technical field of electric power system configuration and discloses a substation capacity planning method based on the mixed quantum evolutionary algorithm. According to the technical scheme, the substation capacity planning method comprises steps of substation data collection, substation capacity configuration and result output. The substation capacity configuration comprises the steps that firstly, a system is initialized; secondly, the state of a population Q (t) is observed and an observation state population P (t) is produced; thirdly, local search is performed on individuals in the observation state population P (t); fourthly, decoding is performed, and a variable optimal solution is obtained; fifthly, fitness evaluation is performed on an objective function; sixthly, the optimal individual and relevant information are stored; seventhly, end conditions are judged; eighthly, the population is updated. According to the substation capacity planning method based on the mixed quantum evolutionary algorithm, the advantages of strong capacity in global optimization and a fast convergence rate of the quantum evolutionary algorithm and the advantage of strong capacity in local search of the tabu search algorithm are combined, therefore, an optimized capacity configuration scheme of a substation can be fast and accurately obtained, and an output result can be fast and accurately obtained.