175 results about "ANT" patented technology

The invention discloses an unmanned aerial vehicle path planning and obstacle avoidance optimization method based on an improved elite colony algorithm, and the method comprises the following steps: carrying out three-dimensional grid environment modeling and obstacle generation, and obtaining a discrete model of a whole three-dimensional space according to a layering + two-dimensional grid method; elite strategy and path generation: ants select paths by using a random proportion strategy, set a transition probability function and introduce a fluctuation coefficient to prevent excessive concentration of path weights caused by pheromone volatilization; dynamic volatilization rate self-adaptive adjustment: carrying out self-adaptive adjustment according to pheromone distribution, and constructing a dynamic volatilization rate model; path cost calculation and dynamics constraint are integrated, a multi-objective planning method is applied, the path length and flight stability are optimized at the same time, and a path cost function is constructed; and performing path post-processing and smooth optimization, including processing a balanced path to improve the flyability and performing collision detection, thereby establishing an unmanned aerial vehicle dynamics constraint model, and ensuring the flight safety on the premise of path optimization.

The invention relates to the field of software engineering, in particular to a chaotic engineering application scene experiment implementation method based on an ant colony algorithm. Carrying out structured processing and time sequence modeling on the collected complex original data; and importing the processed information into an ant colony algorithm, determining parameters such as the number of ant colonies, pheromone concentration and evaporation rate during initialization, initializing a pheromone matrix, and assisting decision making by using resource use conditions and related service information. In algorithm execution, ants select paths according to a probability formula, pheromone and heuristic index weights are determined according to fault injection history and service information, a pheromone matrix is adjusted according to volatilization, addition and update rules after traversal, and the steps are repeated until conditions are met. The algorithm outputs an adjacent matrix containing information of pheromones, nodes and sides, and is used for analyzing a fault path, influence, propagation and generation of a visual chart. The method can improve the experiment efficiency, accuracy and dynamic adaptability, and improve the reliability and toughness of the system.

The invention discloses an AGV path planning method based on construction of a time cost function and improvement of an ant colony algorithm, and relates to the technical field of AGV intelligent guide robots, and the method comprises the steps: S1, constructing a grid environment map, and setting initialization parameters; s2, placing a plurality of ants at a starting point of the grating environment map, wherein each ant starts to independently search a path; s3, when all ants walk to the target node, side pheromone updating and pheromone volatilization are carried out according to the time cost of the walking path of each ant, whether the current number of iterations reaches the preset maximum number of iterations is judged, if yes, the next step is carried out, and if not, the S2 is repeated until the preset maximum number of iterations is reached; and S4, selecting the path with the optimal time cost as the final path according to the time cost of the path on which the ant walks in each iteration. According to the invention, the accuracy of path planning is improved, and the total operation time of the AGV is reduced.

The invention provides an intelligent message pushing method and system, and the method comprises the steps: collecting the social behavior data and personal attribute data of a user in a social network; dividing the users into a plurality of groups by using the social behavior data and the personal attribute data based on an ant colony algorithm; analyzing a social relation and an interaction mode among users in the group; collecting message resources in the social network, and classifying and labeling messages; performing collaborative filtering processing on the to-be-pushed message, determining a target user group, and generating a message recommendation list; and pushing the recommended message to users in the target user group according to a preset pushing strategy. The user social behavior data and the personal attribute data are converted into the ant feature vectors based on the ant colony algorithm, similar feature vector ants are gathered through pheromone updating and path selection mechanisms, different user groups are formed, the user group division accuracy is improved, and user requirements are more accurately grasped.

The invention discloses a dam termite nest three-dimensional imaging method and system based on a ground penetrating radar, and the method comprises the steps: obtaining the three-dimensional ground penetrating radar data of a target region, and carrying out the data preprocessing, and obtaining the preprocessed three-dimensional ground penetrating radar data; performing feature extraction processing and encryption processing on the preprocessed three-dimensional ground penetrating radar data to obtain a multi-channel radar data body; based on the U-shaped network structure, constructing an ant nest imaging three-dimensional deep learning network model; and performing data imaging processing on the multi-channel radar data volume based on the ant nest imaging three-dimensional deep learning network model to obtain an ant nest imaging result based on the three-dimensional ground penetrating radar. According to the embodiment of the invention, the complex features of the ant nest and the dam structure on the three-dimensional ground penetrating radar image can be more accurately captured, and then the accuracy of ant nest imaging is improved. The method can be widely applied to the technical field of termite nest detection.

The invention relates to a transformer substation unmanned aerial vehicle three-dimensional path planning method based on an improved ant colony algorithm. The method comprises the following steps that an electric power unmanned aerial vehicle inspection three-dimensional environment model is generated, and self-adaptive grid division is carried out; performing path search on the initialized ant colony in the electric unmanned aerial vehicle inspection three-dimensional environment model after grid division; each ant individual in the ant colony selects a next node according to a dynamic transition probability calculation model, the ant individual determines the next node which actually moves through a variable step size search behavior, and path information of each ant individual from a starting point to an ending point is recorded; when all the ant individuals arrive at the end point, screening the optimal ant individual of the iteration, executing the artificial ant foraging behavior, and updating the pheromone concentration of each path in the space; iteration is repeated until the preset number of iterations is reached, iteration is ended, and the path with the highest pheromone concentration is selected as the final path of the unmanned aerial vehicle.

The invention relates to the field of path planning, in particular to an ant colony algorithm improvement method and system for unmanned aerial vehicle path planning, and the method comprises the steps: initializing a path diagram and an ant colony, carrying out the iteration through employing the ant colony algorithm, recording a complete path and a sub-path of an ant, and calculating the optimal degree of the path according to the distance and wind direction factors, comprehensively evaluating the sub-path quality; the pheromone volatilization amount and release strategy are adjusted according to the optimization degree, the high-optimization-degree sub-path participates in pheromone release, and the rest of the sub-paths only participate in volatilization; and updating the pheromone concentration in each iteration, after the maximum iteration number is reached, selecting the path with the highest optimal degree as the optimal path, converting node coordinates of the path into GPS coordinates, uploading the GPS coordinates to the unmanned aerial vehicle, and completing path planning. According to the method, the pheromone updating strategy is improved, and only the sub-paths with high optimization degree participate in pheromone release, so that the algorithm can be prevented from falling into a local optimal solution, the global search capability is enhanced, and the unmanned aerial vehicle path planning efficiency and accuracy are improved.

The invention discloses a chaos annealing ant colony-based adversarial blocking timeliness emergency material transportation toughness intelligent decision-making method, and relates to the technical field of intelligent emergency scheduling and optimization. The method comprises the steps of 1, extracting scene data and converting the scene data into model parameters; step 2, constructing a mathematical model for survivability optimization; 3, initializing algorithm parameters and generating an initial path; 4, dynamically adjusting the temperature and the pheromone volatilization rate; 5, generating an adversarial blocking perception path and implementing detour repair; 6, updating pheromone distribution based on an annealing criterion; and step 7, locking the critical path and outputting an optimal transportation scheme. According to the method, timely delivery of materials can be guaranteed in a complex road network blocking and emergency demand scene, the timeliness and toughness of an emergency transportation system are improved through a chaos annealing mechanism and an adversarial blocking simulation technology, the method can be widely applied to the fields of natural disaster rescue, public health event response and the like, and a scientific basis is provided for emergency decision making.

The invention relates to the technical field of machine path planning, and discloses an island ant colony path planning method for an unmanned trolley, which comprises the following steps: after generating a grid map, decomposing global path search into path exploration between adjacent islands; non-uniform pheromone initialization is adopted between adjacent islands, local paths between the adjacent islands are searched respectively, and the position of a next grid is selected in the searching process by combining an improved node transition probability with a heuristic function and pheromone concentration; and when global pheromone distribution is updated, an optimal path is rewarded, and a worst path is punished. And combining the local paths of the island pairs to obtain global paths, and selecting the global path with the shortest length as the optimal driving path. According to the method, a segmented island strategy is adopted, the complexity of single search is reduced, the early random probing process is shortened through non-uniform initialization pheromones, the convergence speed is increased, the number of deadlock ants is effectively reduced, and the precision, convergence and stability of path planning are improved.

The embodiment of the invention discloses a battery pack composite material upper cover parameter determination method and device, electronic equipment and a medium. The method comprises the following steps: enabling a preset ant to search in a preset layer thickness range and a preset layer angle range according to an initialized pheromone concentration and initialized heuristic information to obtain an initial path; inputting each target position point of the initial path into the composite material upper cover finite element model to obtain a first-order modal frequency; updating heuristic information of the target position point, and updating pheromone concentration of the target position point according to the first-order modal frequency; and enabling the preset ant to continue searching iteration according to the updated heuristic information and pheromone concentration of each target position point and the heuristic information and pheromone concentration of other position points to obtain final parameters of the battery pack composite material upper cover. According to the scheme, the optimal upper cover parameter is searched and determined based on the improved ant algorithm, so that the optimal upper cover parameter is quickly and efficiently determined for actual industrial application.

The invention belongs to the technical field of robot path planning, and provides a mobile robot three-dimensional path planning method based on an adaptive ant colony algorithm, and the method comprises the steps: determining a starting node and a target node in a rasterized three-dimensional search space; iteratively searching the optimal path from the starting node to the target node, and outputting the optimal path from the starting node to the target node obtained by the last iterative search; each iterative search comprises the following steps of: A, putting all ants to an initial node; b, generating an attenuation factor which is in negative correlation with the number of iteration search times, and adjusting heuristic information among the nodes by using the attenuation factor; c, each ant selects the next node according to the heuristic information between the nodes and the pheromone concentration between the nodes; d, updating the pheromone concentration of the road section passed by the ants; the invention further discloses a three-dimensional path planning device for the mobile robot, a computer program product and the mobile robot. According to the method, the convergence and efficiency of path search are improved, and the three-dimensional path planning time is shortened.

The improved ant colony algorithm integrating global information enhancement and pheromone suppression strategies is provided based on the problems existing in a traditional ant colony algorithm and used for solving the path planning problem of the welding robot, the actual contribution of nodes to the path quality is comprehensively considered by introducing the global information enhancement strategy, and the path planning precision of the welding robot is improved. The guiding effect of high-quality nodes on ant path finding is improved, and then the search quality of the algorithm is improved. Meanwhile, the pheromone suppression strategy enables the algorithm to find out inferior fragments from historical information, and interference of inferior nodes on ant path finding is reduced. Simulation results show that the GIEPI-IACO algorithm has a remarkable effect on solving the welding robot path planning problem, and the overall performance of the GIEPI-IACO algorithm is superior to that of a basic ant colony algorithm and an existing improved ant colony algorithm.

The invention discloses a geomagnetic matching navigation algorithm based on an improved ant colony algorithm, and belongs to the field of geomagnetic navigation, control and guidance. The algorithm is implemented by the following steps: acquiring initial position information, detecting a magnetic measurement value of data by a geomagnetic sensor, and resolving a magnetic course angle; initializing parameters of the ant colony algorithm; putting the ant at an initial position, and transferring the ant to a next node according to a state transition probability formula until the ant reaches a magnetic measurement sequence; calculating and recording a global similarity measurement function of each ant, and updating pheromones; when the maximum number of iterations is reached, the optimal ant path is output, and the optimal geomagnetic navigation trajectory can be obtained. According to the method, approaching to the optimal solution is realized by utilizing the search randomness and positive feedback behavior of the ant colony algorithm, the optimal track is obtained through the algorithm, and the improvement of the geomagnetic navigation matching precision is realized.

The invention discloses a collaborative inspection method and device based on heterogeneous multi-machine combination, terminal equipment and a storage medium, and the method comprises the steps: constructing an undirected connected graph of an inspection region according to a collection point and a supply point, constructing a plurality of ant colonies according to the number of robots, initial positions and initial resources, and carrying out the construction of a plurality of ant colonies according to the undirected connected graph; and performing a plurality of population exploration operations on the ant colonies to enable each ant colony to generate a plurality of path sets, determining a target path set from the plurality of path sets, and finally determining the inspection task of each inspection robot according to the target path set. Therefore, the problem of endurance of the robot is solved by constructing the undirected connected graph together with the supply points and the collection points, and the calculation efficiency of the algorithm is improved by constructing a plurality of ant colonies to perform population exploration operation together in a concurrent mode, so that the problem that when large-scale inspection is performed in current electric power intelligent inspection, the algorithm is difficult to carry out is solved. And the defects of insufficient computing power and insufficient endurance of the robot are overcome.

The invention belongs to the related technical field of underwater gravity matching navigation, and discloses a submersible vehicle route planning method, application and equipment based on an improved ant colony algorithm, and the method comprises the steps: (1) constructing an adaptability index matrix based on gravity adaptability characteristics, and carrying out the normalization and information entropy weighting to obtain comprehensive adaptability characteristics; (2) constructing an environment grid model according to the comprehensive adaptability characteristics; (3) taking a free space as a search area, carrying out path search based on an environment grating model and an improved ant colony algorithm, and further obtaining a track node sequence meeting the adaptability constraint, the path continuity and the shortest path target to complete track planning; wherein the improved ant colony algorithm is obtained by introducing an elite ant strategy to enhance the globally optimal path pheromone of the ant colony algorithm and adaptively adjusting a pheromone concentration factor, a heuristic function factor and a pheromone volatilization coefficient in the ant colony algorithm by using a particle swarm optimization algorithm. The search efficiency and the automation degree are improved.

The invention discloses an SOP outlet voltage control method in a low-voltage distribution area flexible internet, and the method comprises the steps: taking a current SOP outlet voltage as an initial value, setting the parameters of an ant colony algorithm, including the number of ants and the number of iterations, carrying out the path search of the ant colony based on the SOP through employing a spanning tree algorithm, and generating a radial topology. And performing load flow calculation on the radial topology, and performing power balance judgment and node voltage constraint verification based on a load flow calculation result to ensure that the power is balanced to meet the node voltage constraint so as to ensure the stable operation of the power system. Searching and iteration of a topology scheme are carried out by utilizing an ant colony algorithm, through continuous iterative optimization, a radial topology which meets constraint conditions and has optimal SOP outlet voltage is found, and the radial topology is taken as a final control result. According to the invention, the control precision and the response speed of the SOP outlet voltage can be improved.

The invention belongs to the technical field of data processing, and particularly relates to a supplier management method and system based on risk assessment, and the method comprises the steps: obtaining the risk indexes of a supplier according to the quality risk, delivery risk, financial risk and compliance risk of the supplier; according to the upstream and downstream relationship between the suppliers, constructing a risk propagation directed graph, and further obtaining a risk propagation index between any two suppliers; further introducing an ant colony algorithm to construct a purchase path, and in each iteration process, comprehensively considering self risk indexes of selectable suppliers, risk propagation indexes from existing nodes in a current path to the selectable suppliers, and risk and cost-oriented pheromone distribution in a historical path, so as to dynamically calculate the selection probability of each selectable supplier; and the ants are guided to gradually construct an optimal path meeting purchasing requirements. According to the invention, the objectivity and accuracy of risk identification are improved, and the early warning capability of enterprises for systematic risks is improved.

The invention relates to the technical field of real estate, and discloses an ant colony algorithm and neural network combined house price data prediction method, which comprises the following steps: S1, multi-source data acquisition, S2, data preprocessing, S3, ant colony algorithm parameter optimization, S4, neural network dual-stage training and S5, adaptive optimization feedback. According to the house price data prediction technology combining the ant colony algorithm and the neural network, neural network model parameters are optimized by adopting the ant colony algorithm; in the house price prediction model, the parameters are combined to form a path, and ants traverse different parameter combination paths; along with iteration, pheromones on a better parameter combination path are continuously accumulated and enhanced, and ants are guided to explore more; in this way, the parameter space can be fully traversed, local optimum caused by parameter random initialization of a traditional neural network is avoided, and therefore the model can obtain a better parameter combination, and the prediction precision is remarkably improved.

The invention discloses an earth and rockfill dam termite density estimation and termite channel inference method, and belongs to the technical field of earth and rockfill dam safety monitoring. The method comprises the following steps: processing acquired multi-source monitoring data to generate a standardized multi-source data set; performing phase-preserving parallel frequency domain time mapping on the dielectric constant time sequence data to generate time synchronization data in which transient characteristics are retained; respectively extracting instantaneous density response characteristics representing termite activity intensity and termite channel branch probability characteristics indicating termite channel branch tendency by using the time synchronization data and the resistivity space distribution data; and based on the two types of features, constructing and solving a density-path mutual feedback objective function, and obtaining a termite instantaneous density distribution and termite channel branch prediction result. According to the invention, through the time mapping and density-path mutual feedback inversion framework, collaborative optimization of multi-physical field information is realized, and the precision of density estimation and the reliability of ant channel inference are significantly improved.

The invention discloses an efficient routing algorithm for offshore buoy networking. The problem that existing offshore buoy networking information transmission is poor in real-time performance and stability is mainly solved. According to the scheme, the method comprises the following steps: inputting the position of an offshore buoy network, constructing a communication link connectivity model, and abstracting a network link topology by means of a graph theory representation mode; comprehensively considering network efficiencies such as time delay, packet loss rate and network load balancing, and determining link cost between two buoy nodes; aiming at the defect that the ant colony algorithm is easy to fall into local optimum, two-point improvement is carried out on the basic ant colony algorithm. The method comprises the following steps: firstly, during construction of initial pheromones, in a first iteration process of an algorithm, 20% of ants in a population are selected, a path with the minimum cost is selected based on a neighbor algorithm, and the remaining ants adopt random selection paths, so that the convergence of the algorithm is improved, and meanwhile, the diversity of the population is ensured; and a second point, aiming at the problem that a large number of ants are easy to walk to a repeated path when the ant colony is converged to the local optimum in the later stage of iteration, adding repeated check operation in an algorithm updating process. According to the specific operation, when 20% of ants select repeated paths, the ants of the repeated paths select new paths in a random mode, the path search space is expanded, and the algorithm performance is improved. According to the method, the efficient route of buoy networking communication can be dynamically planned, and the real-time performance and the stability of the network are met.

The invention relates to a deliverable network route optimization method and system, and belongs to the technical field of communication operation, and the method comprises the steps: obtaining a deliverable network first route through employing an ant algorithm; key cross node parameters in the first route of the deliverable network are extracted, and semantics of the entity words in the context are obtained based on the key cross node parameters; and taking semantics as dynamic parameters, orderly carrying out federated learning according to the service association weights to obtain target semantics, and determining a deliverable network target route based on the target semantics. According to the method, the problem that an optimal detection agent deployment scheme is obtained according to a detection agent deployment strategy in the integrated delivery process is solved, and the detection agent deployment scheme is possibly locally optimal but not globally optimal.

The invention discloses a method for determining a service starting sequence, electronic equipment and a storage medium, and relates to the field of servers, the method comprises the following steps: constructing a directed acyclic graph according to a dependency relationship between services and / or processes in a server; on the basis of the ant colony algorithm, N times of iterative operations are executed through the M ants according to the directed acyclic graph to obtain a target service starting sequence, and the ith iterative operation of the N times of iterative operations comprises the steps that on the basis of the ant colony algorithm, the service starting sequence is constructed through each ant in the M ants according to the directed acyclic graph, determining a server starting total time length corresponding to the constructed service starting sequence; the target service starting sequence is the service starting sequence with the minimum server starting total duration in the service starting sequences constructed by each ant in the N iteration operations. By the adoption of the scheme, the problems that the server starting efficiency is low and the starting time is long due to the fact that an existing service starting sequence is determined through a predefined static method are solved.

The invention discloses a flood avoidance transfer multi-objective optimization analysis method considering road congestion constraints. The method comprises the following steps: constructing a road network undirected graph; calculating the shortest distance from each node to the placement area; initializing ant colony and road network parameters, and retaining pheromone concentration and road network congestion degree of the last round of training; traversing all ant colonies, if traversing is not completed, letting k = k + 1, and traversing all ants in the ant colony k; for each ant, selecting a next node according to a heuristic function, pheromone concentration and crowding degree until the ant reaches a resettlement area or an empty node; and judging whether a training termination condition is met or not, if so, outputting a pheromone concentration and placement area matching result, and performing result diagnosis. According to the invention, based on the ant colony algorithm framework, transfer time shortening and transfer batch reduction are taken as optimization objectives, and the problem of multi-objective optimization of transfer area and placement area matching and path selection under the constraint of road congestion is solved.

The invention relates to the technical field of path planning, in particular to a path planning method based on an improved ant colony algorithm, which comprises the following steps: establishing a grid map, and initializing parameters of the ant colony algorithm; determining an initial value of pheromone concentration according to the distance between the nodes in the grid map and the initial node and the distance between the nodes in the grid map and the target node; ants are placed at the initial node for multiple times, each ant starts from the initial node, the next node where the ant moves is selected according to the pheromone concentration and heuristic information, and the taboo table and the current pheromone concentration are updated; emptying the taboo table after the tracking of all ants is completed, and recording the paths of all ants; updating a pheromone volatilization factor based on a proportional relation between the average path length and the minimum path length; after the maximum number of iterations is reached, a final planned path is obtained according to the recorded path through which the ants walk; the convergence speed and reliability of the ant colony algorithm can be improved.

The invention discloses a ship segment / block hoisting optimization scheduling method and system considering total group site arrangement. The ship segment / block hoisting optimization scheduling method comprises the steps of obtaining information of each segment / block of a ship and information of a total group site; regarding each segment / block as a node, creating a directed graph according to a constraint relationship between a front process and a rear process of the segment / block, and forming a feasible transfer set; according to the pheromones and the heuristic factors, calculating the transition probability of ants among different nodes; for the selected subsections / blocks, the hoisting starting time and the hoisting ending time are determined according to the sequential arrangement or reverse arrangement identification and the available time period of the total assembly site of the subsections / blocks; in the optimization process of the ant colony algorithm, each ant forms a complete scheduling sequence in combination with the transition probability of the ants among different nodes and the hoisting starting and ending time of each segment / block; calculating a target function value corresponding to a scheduling sequence formed by ants by using a time calculation function, and performing pheromone updating on the path of the ant colony; and outputting a scheduling scheme until the algorithm converges.

The present invention provides a global path planning method for a mobile robot based on an improved potential field ant colony algorithm. The method includes: S1, constructing a grid map; S2 includes: S21, initializing a taboo table; S22, obtaining comprehensive heuristic information of each ant's neighborhood node, the comprehensive heuristic information is obtained according to the potential field force, the distance between the neighborhood node and the starting node and the target node, and the obstacle coefficient between the nodes; obtaining the selection probability of the neighborhood node, selecting the next node, and updating the taboo table; S23, obtaining the shortest path of this iteration; S24, updating the path pheromone concentration, and updating the pheromone volatility coefficient; S3, outputting the shortest path in T iterations. Introducing the potential field force into the comprehensive heuristic information improves the search efficiency, introducing the distance from the starting node to the neighborhood node and the distance from the neighborhood node to the target node into the heuristic information improves the global search capability, and adopting the dynamic pheromone volatility coefficient improves the convergence speed.

This application provides a load resource scheduling method and related devices. The method includes: obtaining load resource parameters; constructing an objective function based on the total operating cost, comprehensive voltage deviation, and local consumption rate; the constraint conditions of the objective function include: peak-valley fluctuation constraint, linear power flow constraint, and load response quantity balance constraint; based on the load resource parameters, combining with the Chebyshev chaotic mapping algorithm, using the improved ant lion algorithm to solve the objective function to obtain a scheduling plan; performing scheduling processing on the load resources based on the scheduling plan. In the embodiments of this application, an effective scheduling strategy is formed by constructing an objective function. The objective function is restricted by multiple constraint conditions, which ensures the feasibility and rationality of the scheduling plan. By optimizing and solving the objective function, the scheduling cost of the system can be effectively reduced, and at the same time, the utilization efficiency of electric energy can be improved. Further, resource scheduling processing can be better carried out, and the stability and economy of the power grid can be enhanced.

The invention discloses a multi-target multi-robot task allocation method based on a collaborative ant colony, and the method comprises the steps: constructing a multi-target multi-robot task allocation model, and carrying out the design of a collaborative ant colony algorithm; three ant colonies are set in the ant colony algorithm and have three optimization objectives of minimizing the maximum completion time of all the tasks, minimizing the average movement time of all the robots and minimizing the average waiting time of all the tasks, and a multi-objective multi-robot task allocation model is solved; optimal solutions on the three optimization targets are selected through local search, and pheromones of the three collaborative ant colonies are globally updated; and outputting an optimal solution set of the multi-target multi-robot task allocation model. Compared with a traditional multi-target method, the method is stable in performance on the task allocation problem of multiple robots of different scales, and a series of high-quality feasible solutions can be obtained within a short time.

The invention discloses an improved electric connector contact resistance prediction method based on an ant lion algorithm, and the prediction method is based on a classic BP neural network, and introduces an optimization algorithm based on the ant lion predation behavior in the nature. According to the contact resistance prediction method under the condition of fretting wear of the electric connector, a BP neural network model (ALO-BP neural network model for short) is optimized by utilizing the global search capability of an ant lion algorithm. Comprising the following steps: initializing a population, introducing random walk of ants to realize global search, and ensuring the diversity of the population and the optimization performance of an algorithm through a roulette strategy and an elitist strategy; comparing fitness and elite ant lion positions; the fitness is calculated again, and the positions of the ants and the ant lions are updated; judging whether a constraint condition or the maximum number of iterations is met or not, and giving a weight and a threshold value to the BP neural network by the global optimal value under the condition that the constraint is met; and optimizing the BP neural network to obtain an optimal model, and performing reliability prediction on the contact resistance value. According to the invention, the optimization performance, the operation efficiency and the prediction precision of the BP neural network can be improved.

The invention discloses a multi-logistics vehicle collaborative scheduling method based on a guided customer selection ant colony system, and belongs to the technical field of computing intelligence. According to the method, a feasible solution scheme is constructed for a multi-logistics-vehicle cooperative scheduling optimization problem by maintaining ant teams with the same quantity as the logistics vehicles, wherein each ant is responsible for constructing a service path of one logistics vehicle. After each ant randomly selects the initial customer of the respective service path, selecting the customer which is finally served for each ant based on a final customer selection method of the minimum service cost; then, the ant colony system fuses the current customer information and the customer selection mechanism of the customer information of the final service, and constructs the service path of the logistics vehicle path by path and customer by customer, so that the constructed path is more directional; and finally, in combination with 2-opt and a point insertion local search strategy, optimizing service paths of all logistics vehicles. According to the method, the optimal multi-logistics-vehicle cooperative scheduling scheme is constructed, and meanwhile, the service cost between the logistics vehicles can be well balanced.