11 results about "Glowworm" patented technology

1. Name of the designed product: side skirt (firefly hydrogen modification). 2. Use of the designed product: automobile side skirt. 3. Design points of the designed product: in shape. 4. Picture or photo best indicating the design points: perspective view 2.

The invention discloses a Bayesian network structure learning method based on an improved MMHC algorithm, and relates to the technical field of Bayesian networks, the MMPC algorithm obtains a data set, determines a father-child node set of each variable through a conditional independence test and correlation judgment, and obtains an undirected graph; converting the undirected graph into a directed graph; performing random operation on the directed graphs to obtain a series of directed graphs and an initial population of a firefly algorithm; the fitness value of each firefly in the population is calculated, and the positions of the fireflies are updated; performing local optimization operation on the fireflies after position updating; and after multiple iterations, selecting the firefly with the highest fitness value in the population as a final Bayesian network structure. The improvement measures of the invention help to enhance the overall performance of the algorithm.

The application provides a design method of glowworm swarm optimization algorithm based on a ternary optical computer, relates to the field of glowworm swarm optimization algorithm, and improves the operation speed and communication mode of the algorithm by using the parallelism of the ternary optical computer, and different parallel strategies are used for the algorithm in different stages to reasonably allocate resources, wherein the inter-process communication between processors can also make the population individuals in different processors communicate with each other, and the mutation and cross fusion are performed on the basis of each other, the application is based on the characteristics of the ternary optical computer, such as the huge potential, parallelism and bit allocation, and the classical glowworm swarm optimization algorithm is improved, an improved glowworm swarm optimization algorithm based on the ternary optical computer is designed mainly by means of chaotic mapping randomization of the initial population position, introduction of adaptive step length, and balance of boundary adjustment, and the like, and the experimental results show that the improved glowworm swarm optimization algorithm based on the ternary optical computer has enhanced optimization ability and increased convergence speed.

The application relates to the technical field of mobile self-organizing network and flight ad hoc network communication, and discloses an AODV routing method based on Hello message statistical characteristic mapping luciferin. The strategy aims at the problem that the traditional AODV protocol is prone to misjudging the communication gray area as an effective link in a high dynamic network, the Hello message timestamp is extended to perceive link jitter, and multi-dimensional features such as a successful receiving rate and signal strength are combined to carry out normalization processing, so as to construct an instantaneous fitness function; a bionic glowworm swarm optimization mechanism is further introduced to dynamically update luciferin values with historical memory; finally, pruning is implemented according to the luciferin threshold value in the routing discovery stage, so that unstable links are blocked.

The application discloses a method and system for predicting deep hydrocarbon source rock organic matter enrichment law based on glowworm algorithm optimized GBDT, and belongs to the field of hydrocarbon source rock TOC prediction. Specifically, the method comprises the following steps: obtaining logging curve data; preprocessing the obtained logging curve data; based on the preprocessed data, establishing a machine learning model (the core lies in the hyperparameter optimization of the model, and the glowworm algorithm is particularly used in the optimization process); and using the established TOC prediction model to predict the total organic carbon content. The application combines machine learning and intelligent optimization algorithm and predicts the organic matter enrichment law of deep hydrocarbon source rock, which can greatly improve the prediction accuracy and precision, and reduce the exploration cost and risk.

The invention discloses a computing resource allocation method and device, terminal equipment and a storage medium. The method comprises the steps that task information of a to-be-unloaded computing task is acquired; according to the task information, preliminarily generating a plurality of allocation schemes of computing resources for executing the computing task; taking the plurality of distribution schemes as an initial firefly population, and executing a plurality of rounds of iterative updating operation on the positions of fireflies by using a firefly algorithm until an updating ending condition is met; according to the first brightness value of each historical position of the firefly population in the multi-round iteration updating operation, the first historical position of the first firefly is determined, and the second brightness value of the first firefly at the first historical position is larger than at least part of the first brightness value; and according to the allocation scheme corresponding to the first firefly at the first historical position, allocating the computing resources to execute the computing task. And the efficiency and reliability of edge computing task execution are obviously improved.

The application provides a traffic trunk coordination signal timing optimization method, system, device and medium, belongs to the field of traffic control, and comprises the following steps: a dynamic model of traffic signal control is established by using a Markov decision process; a value function is obtained according to the traffic signal control model, and precise description of signal timing is realized. The value function is introduced into the brightness function and position updating function of the glowworm swarm optimization algorithm, and an adaptive light intensity absorption coefficient and a dynamic random disturbance factor are introduced to improve the glowworm swarm optimization algorithm, so that an improved glowworm swarm optimization algorithm is obtained, and the global search ability of the glowworm swarm optimization algorithm is improved. The adaptive neuro-fuzzy inference system is optimized and trained by using the improved glowworm swarm optimization algorithm, and a signal timing optimization model is obtained. According to the adjustment amount of the signal timing parameter output by the signal timing optimization model, adaptive intelligent control of the traffic signal timing is realized. The method can be applied to complex and variable traffic trunk environments, and can efficiently and accurately provide signal timing optimization strategies for traffic trunks.

The application provides a power source site selection and capacity determination method, and relates to the technical field of power system optimization, which comprises the following steps: obtaining operation parameters of a unit, determining a first constraint set of the unit and a second constraint set of power source site selection and capacity determination according to the operation parameters; obtaining a distribution network parameter set, constructing a target function of power source site selection and capacity determination according to the distribution network parameter set and the operation parameters; determining an initial glowworm population based on a configured target rule, and performing glowworm algorithm iteration optimization based on the initial glowworm population to determine an optimal result of the target function under the condition of meeting the first constraint set and the second constraint set, so that the distribution network containing the distributed power source can be safely, reliably and economically operated.

The application discloses a method and device for calculating nominal electric field of direct current transmission based on a firefly algorithm, and relates to the technical field of high-voltage engineering and computational electromagnetics. The method comprises the following steps: obtaining transmission line parameters, setting the number of simulation electric charges, the number and coordinates of matching points and verification points for each conductor, and calling a preset simulation electric charge method calculation framework to solve simulation electric charge quantity vectors and target functions; encoding all simulation electric charge coordinates into optimization variables, randomly generating an initial population and calculating brightness through the firefly algorithm, iteratively optimizing simulation electric charge positions, and finally obtaining a terminal generation optimal simulation electric charge position vector, which is substituted into the calculation framework to calculate a terminal generation optimal simulation electric charge quantity vector, and then the ground nominal electric field is solved. The optimal position of the simulation electric charge is searched through the firefly algorithm, the calculation precision of the ground electric field of the direct current transmission line is improved, and reliable technical support is provided for the ground nominal electric field evaluation under the static electric field condition of the direct current transmission line.

The invention belongs to the technical field of power systems, and relates to a hydropower station unit load distribution method and system based on a power system. The method comprises the following steps: acquiring operation state data of a hydropower station in real time; establishing a mapping relation model of output power and flow, and determining an operation constraint condition; establishing a mathematical model taking the minimum total water consumption rate of the power station as an optimization target; solving the mathematical model by adopting a firefly algorithm; calculating the brightness of each firefly in the population; random disturbance is introduced in the moving process, a solution falling into a vibration area range is punished and corrected in the iteration process, and a global optimal power distribution scheme is output; according to the method, a firefly algorithm is used for mining tiny efficiency differences among different units, maximization of water energy utilization is achieved, and an optimal power distribution scheme is output by monitoring operation condition changes in real time; according to the invention, the maximization of water energy utilization is realized, and the risk that a unit enters a vibration area by mistake to operate under a low-load working condition is effectively avoided.

The application discloses a method for determining the chloride ion permeability of concrete in an underground structure, comprising the following steps: S1, collecting recycled aggregate concrete component data and permeability indexes; S2, dividing the data collected in step S1 into a training set and a test set; S3, establishing a chloride ion permeability prediction model by using an adaptive neural fuzzy inference system; S4, inputting the training set in step S2 into the chloride ion permeability prediction model in step S3, and optimizing and adjusting the chloride ion permeability prediction model parameters by using a chaos-based glowworm algorithm; S5, inputting the test set in step S2 into the optimized chloride ion permeability prediction model, testing the chloride ion permeability prediction error, and obtaining a trained chloride ion permeability prediction model; and S6, predicting the chloride ion permeability by using the trained chloride ion permeability prediction model. The method overcomes the defects of long time period and high test cost of the prior art, and realizes accurate prediction of the chloride ion permeability of concrete.