5 results about "Chaotic search" patented technology

The invention provides a two-dimensional path planning method based on a differential evolution and flower pollination hybrid algorithm, and belongs to the field of robot path planning and intelligent optimization algorithms. Comprising the following steps: reading a two-dimensional environment map, and identifying an obstacle and a free space area; the parameters are initialized; generating an initial path population by adopting a method based on chaos search and opposite learning, and coding paths into a two-dimensional coordinate sequence; performing path optimization on the large population by adopting an improved differential evolution algorithm; carrying out iterative optimization on the small population by adopting an improved flower pollination algorithm, dynamically adjusting the switching probability and improving a position updating formula; executing similarity removal operation to keep population diversity; and if a termination condition is satisfied, outputting an optimal path, otherwise, continuing iteration. Through the global search ability of the differential evolution algorithm and the local fine search ability of the flower pollination algorithm, exploration and development are balanced, the efficiency and optimality of path planning are improved, and the path quality and the algorithm stability are improved.

The application discloses a multi-source energy optimal configuration method of a hypersonic aircraft, and energy configuration with the lowest fuel consumption and the largest thrust is obtained by using a multi-objective adaptive covariance matrix and a chaotic search group algorithm (MOACCGA), so that the improved method of the adaptive covariance matrix is adapted to strong nonlinear changes of a system, the improved method of the chaotic search is used to avoid falling into local optimization, accurate distribution of multi-source electric energy of the hypersonic aircraft in a dynamic environment is realized, and the technical problem of energy optimal configuration of the hypersonic aircraft is solved.

The invention discloses a heat supply network operation optimization method and system based on load prediction, and relates to the field of heat supply network operation optimization, and the method comprises the steps: collecting multi-source data of heat supply network operation, carrying out the load prediction, and generating a load prediction result; calculating mutual information and information entropy according to the load prediction result, and generating a dynamic feedback signal; constructing a causal relationship graph of the heat supply network based on a load prediction result, analyzing the influence of load change on system operation variables by using intervention calculation, and generating a causal intervention strategy; fusing the dynamic feedback signal with a causal intervention strategy, calculating a fusion weight according to the mutual information value and the intervention effect intensity, and generating fusion input according to the fusion weight; the fusion input is used as an initial state, Logistic mapping is adopted to carry out chaos search, and a multi-target optimization strategy is generated; and integrating the multi-objective optimization strategies through a reinforcement learning algorithm, and outputting a heat supply network operation control scheme. The heat supply network energy efficiency and the system stability are improved, and self-adaptive optimal configuration of scheduling parameters is achieved.

The application relates to the technical field of traffic accident tracing, and discloses a method for tracing traffic accidents by unmanned aerial vehicle (UAV) inspection in complex scenes, which comprises the following steps: establishing a UAV cluster cooperative control system to collect data at the accident site; performing multi-source deep data fusion on the collected data by means of a deep spatiotemporal feature network; inputting the fused data into a causal reasoning enhanced accident tracing analysis model constructed based on an improved Harris hawk optimization algorithm to generate an accident tracing analysis report. The causal reasoning enhanced accident tracing analysis model constructed based on the Harris hawk optimization algorithm improves the global search capability in a high-dimensional feature space and improves the accuracy and interpretability of the tracing result by means of adaptive weight adjustment and optimization of a chaotic search strategy.

The application provides a multi-element two-stage adaptive accumulation prediction method for a regional integrated energy system; firstly, a new preprocessing module based on ensemble learning is provided to preprocess original input data, provide a reliable data basis for TAPM, and determine key input variables of multi-energy load prediction; then, the training results of four predictors are adaptively integrated in the first stage of TAPM to enhance the generalization ability of the model, wherein a new collaborative atomic chaotic search algorithm is provided to train the predictor hyperparameters and intermediate data set of TAPM, so that the adaptive accumulation of the predictor in TAPM is realized; finally, peak load prediction correction in the power consumption peak period is carried out in the second stage of TAPM; the energy cascade utilization efficiency is improved, the regional integrated energy system is widely concerned, the multi-energy load prediction is very crucial for the planning of the regional integrated energy system, and effective decisions can be made by managers for the carbon peak target.