134 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 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 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 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 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 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 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 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.

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-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.

The invention belongs to the technical field of intelligent optimization algorithm and new energy high-rate charging cross fusion, and provides a new energy vehicle high-rate charging pile task allocation method based on an ant colony algorithm. The method comprises the following steps: S1, pre-searching and filtering high-magnification charging piles of which the danger probability is greater than a threshold value to form a candidate set; s2, defining a high-magnification charging task set and a high-magnification charging pile set by adopting a graph model; s3, constructing a comprehensive cost function, and determining a weight coefficient of the comprehensive cost function; s4, the high-magnification charging pile carries out multiple rounds of searching; s5, performing local search optimization on the optimal solution obtained by each ant; and S6, matching optimization is carried out, and distribution is completed. According to the method, the problems of safety and efficiency in a traditional method are effectively solved by preprocessing and filtering the charging piles with too high danger probability, applying a multi-round search strategy, simulating pruning operation and applying a local optimization algorithm.

The invention relates to the field of logistics, in particular to a pick-up optimization method and system for intelligent logistics, and the method comprises the steps: obtaining a to-be-processed pick-up task set, courier information and real-time road network data; in path construction of each iteration of the ant colony system, according to path pheromone concentration, comprehensive heuristic information, courier area proficiency and task urgency, calculating the transfer probability of ants from a current node # imgabs0 # to a next non-accessed node # imgabs1 #; after each iteration is completed, calculating a space-time volatilization factor according to the historical traffic efficiency and the real-time congestion index, and updating global pheromones by using the space-time volatilization factor; performing pheromone enhancement on the optimal path based on the path quality of the current iteration optimal solution; the path quality is a scalar value of the total duration of the comprehensive evaluation path, the time window satisfaction degree and the comprehensive path cost; and repeating the path construction and pheromone updating steps until a preset termination condition is met, and outputting an optimal pickup path sequence.

The invention relates to a three-coordinate measuring machine global detection trajectory planning method based on an improved ant colony algorithm. The method comprises the following steps: constructing a distance adjacent matrix; initializing related parameters of the improved ant colony algorithm and initial iteration positions of ants; next nodes are selected in sequence based on the state transition probability until the ant traverses all the nodes, and a complete detection path is obtained; updating pheromones among nodes according to the quality of the detection path; calculating node fitness according to the quality of the detection path, and calculating a node selection probability based on the node fitness; selecting an initial node of the next iteration of the ant colony by using a roulette strategy based on the node selection probability; on the basis of the updated pheromone and the initial node of the next iteration of the ant colony, the ant colony iteration search detection path process is carried out again; and selecting the path with the shortest detection path length in the iteration process as a global optimal detection track. According to the invention, the global detection trajectory planning effect of the three-coordinate measuring machine is improved.

The invention relates to a storage AGV path planning method based on a hierarchical collaborative ant colony algorithm, belongs to the field of storage robot path planning, and aims to solve the problem of realizing better path planning in a complex storage environment. The method comprises the following steps: constructing an environment model of intelligent storage based on a grid method; an ant colony algorithm is improved on the basis of a hierarchical cooperative system, an ant colony is divided into scout ants responsible for exploration and foraging ants responsible for optimization, a heuristic function comprehensively considering the distance, the corner and a new exploration area is constructed, an improved pheromone updating strategy based on ant layering and the pheromone concentration on a self-adaptive volatilization factor updating path are adopted, and a new exploration area is obtained. The transfer strategy fuses three modes of random selection, probability selection and determinacy selection, and finally an improved ant colony algorithm is obtained; an improved algorithm is utilized to plan a global path for each AGV, and secondary optimization is performed in a targeted manner, so that the path length is shorter, and the smoothness is higher. And an effective solution is provided for AGV path planning in a complex storage environment.

The application provides a ship path planning method and device for a near-shore environment and electronic equipment, and belongs to the technical field of artificial intelligence. The method comprises the following steps: rasterizing a near-shore environment image to obtain a sailing space model; setting initial parameters of an improved potential field-ant colony algorithm and initializing a path taboo table, then determining path point pheromone distribution and a combined potential field force, calculating a transfer probability of an ant transferring to other nodes to determine a next node, and updating the taboo table; after an ant traverses all nodes to reach a target point, updating the pheromone distribution and recording a traversal path; after all ants traverse all nodes, obtaining traversal paths of all ants and determining an optimal path. The application improves the potential field-ant colony algorithm, introduces potential field information, and dynamically adjusts a heuristic information function; adjusts a path selection probability optimization algorithm structure; and considers the sailing characteristics of a ship itself to ensure the demand for autonomous sailing of the ship.

The invention provides a runoff hydropower station annual maintenance and power generation plan joint optimization method based on an improved ant colony algorithm, and belongs to the technical field of hydropower station optimization scheduling. According to the method, for the multi-target conflict problem of abandoned water loss, peak regulation capacity insufficiency and base load vacancy in an annual maintenance plan of a runoff hydropower station, a comprehensive target function containing abandoned water penalty, peak regulation vacancy penalty and base load vacancy penalty is constructed, and combined optimization is achieved by adopting an improved multi-group coevolution ant colony algorithm. The core of the algorithm lies in that ant colonies are divided into common ant colonies and elite ant colonies, the search capability is improved through local and global pheromone updating and diffusion mechanisms, and the process is optimized to determine the optimal overhaul time period of a to-be-overhauled unit and the optimal operation mode of a non-overhaul unit. Example verification shows that the method can significantly reduce abandoned water loss and base load vacancy, ensures sufficient peak regulation capacity, saves annual operation cost of the hydropower station, and is suitable for intelligent decision-making of the large radial flow type hydropower station.

The invention relates to the technical field of multi-objective optimization control, in particular to an improved ant lion algorithm-based multi-objective optimization control method for an air source heat pump air conditioning system, which comprises the following steps of: constructing a triple constraint fitness function; the method has the beneficial effects that the limitation of single target optimization of a traditional control method is broken through by constructing a triple constraint fitness function comprising a system energy consumption item, a comfort item and a system comprehensive penalty item. By dynamically adjusting the correction coefficient, the relation of energy consumption, comfort level and stability can be flexibly balanced according to actual requirements, the extreme situation that comfort level is sacrificed for energy conservation or excessive energy consumption is caused for comfort is avoided, and the optimal comprehensive performance of the system is achieved.

An intelligent ant nest sound wave positioning weak signal denoising method combining SDM and LG-BPN comprises the following steps: step 1, obtaining termite sound wave data to obtain a real data sample set; mixing the real data sample set and the synthetic data sample set to obtain a training sample set; 2, generating a large number of training samples by using a shear wave embedding generator SDM according to the training sample set in the step 1, and obtaining an expanded training sample set; step 3, training the LG-BPN denoising network by using the training sample expanded by the shear wave embedding generator SDM as input; and step 4, inputting the collected ant nest sound wave positioning weak signal to be denoised into the trained LG-BPN denoising network, and outputting the denoised ant nest sound wave positioning weak signal. Aiming at the technical problems of low ant nest positioning speed and low positioning precision caused by small sample and low signal-to-noise ratio in ant nest sound wave positioning weak signals, the invention provides the method.

The invention relates to a multi-center load balancing method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring performance indexes of a plurality of data centers; setting a fitness function based on the performance indexes, and randomly generating a plurality of initial load schemes; based on the fitness function, optimizing the plurality of initial load schemes in an iteration mode to obtain an intermediate load scheme; the intermediate load scheme is converted into an initial pheromone, ant colonies are placed in a plurality of data centers in an iteration mode, action paths of the ant colonies are obtained, the initial pheromone is iteratively optimized according to the action paths, optimized target pheromone is obtained, and the optimized target pheromone is converted into a target load scheme; and allocating tasks to the plurality of data centers based on the target load scheme. By adopting the method, multiple data centers can efficiently process large-scale requests.

The invention relates to the technical field of wireless charging, in particular to a wireless energy transmission equipment combination selection method and device based on deep reinforcement learning and ant colony optimization. Comprising the following steps: inputting transmission equipment parameters and receiving equipment parameters, and generating heuristic information selected by equipment through a pre-trained graph neural network model; initializing a selection probability matrix of all ants and pheromone concentration between transmission devices; generating a candidate energy transmission combination scheme through an ant colony optimization algorithm by using heuristic information and pheromone concentration; selecting an optimal scheme with the maximum energy transmission power from the candidate combination schemes through iterative optimization; and triggering the transmission equipment in the optimal scheme to execute wireless energy transmission to the receiving equipment. A high-power wireless energy transmission combination scheme can be efficiently calculated to meet the energy supply demand of equipment.

The present invention discloses a multi-mode project scheduling method based on a hybrid heuristic ant colony system. First, a benchmark sequence that satisfies the execution order of activities in the activity-on-arrow network diagram is randomly generated. Then, an actual scheduling plan that satisfies the activity-on-arrow network diagram is gradually constructed according to the benchmark sequence. Next, the paths of ants are updated through hybrid heuristic information. After that, asynchronous simulation processing is performed on the cost and execution time parameters, and the correlation coefficient between solutions is calculated. Finally, simulation is carried out in the next generation according to the correlation coefficient between solutions in the previous generation. The method of the present invention utilizes the characteristics of uncertain parameters in multi-mode medical project scheduling optimization and the high dependence of uncertainty on the context to design and improve the algorithm. On the premise of ensuring the evaluation accuracy, it reduces the consumption of computing resources and improves the optimization efficiency of the ant colony system.

The present invention discloses a distribution network measurement optimization configuration method and terminal. The method uses the scalar function of the Fisher information matrix of multiple time sections as a parameter affecting the pheromone update in the first ant colony algorithm to compress the initial measurement set of the distribution network into a candidate measurement set; uses the state estimation accuracy value of multiple time sections as a parameter affecting the pheromone update in the second ant colony algorithm to solve the measurement configuration scheme with the best state estimation accuracy from the candidate measurement set. Therefore, during the second stage of calculation, the existence of the first stage greatly reduces the number of optional measurements when the ants walk, and saves calculation time while ensuring that the measurement configuration can obtain the optimal solution. At the same time, since the two-stage measurement optimization configuration of multiple time sections is taken into account, it can effectively solve the problem of insufficient observability of the previous active distribution network and improve the accuracy of solving the optimal measurement configuration scheme.

This invention proposes an optimized method for drone data acquisition based on matrix completion and trust assessment, applicable to drone forensics in IoT network defense. First, in matrix completion, the sampling point locations and data packet acquisition times are constructed into a matrix, and matrix completion technology is used to recover all information and select sampling points. Second, in the drone flight trajectory, the selected sampling points and the Elite Ant Trail Optimization (EATO) algorithm are combined to optimize the drone's flight trajectory. Third, in the trust evolution mechanism, the comprehensive trust level of sensor nodes is obtained through dual evaluation by neighboring nodes and the drone. The proposed method can solve the security risks and resource consumption problems of data acquisition from IoT smart devices, effectively identify malicious nodes, optimize the accuracy of trust assessment, improve network security, and reduce drone energy consumption.

The invention discloses a multi-SoC cooperative task allocation method based on quantum ant colony optimization. The method comprises the following steps: acquiring a node resource portrait set and a task attribute set for each system-on-chip node in a multi-SoC system; constructing a task-resource mapping graph; obtaining a quantum coding task set; loading the quantum coding task set to a search state space of quantum ant individuals, setting a pheromone matrix and a quantum rotation angle matrix, and obtaining an initial population of a quantum ant colony; obtaining an updated quantum state population; calculating a delay index, a power consumption index and a reliability index of each deterministic task allocation result according to the node resource portrait set, and screening out a candidate feasible solution set meeting real-time constraint; and obtaining a global scheduling scheme. According to the method, the modeling granularity and the distribution precision of task distribution under multi-domain heterogeneous resources are remarkably improved, the problems of bandwidth contention and delay mutation in cross-node and cross-domain migration are effectively avoided, and the real-time performance and the high adaptability of task distribution are achieved.

The invention discloses a dynamic equipment layout method based on a quasi-object strategy and a multi-target ant colony optimization algorithm. The method comprises the steps of layout initialization, layout legalization operation, a movement strategy based on reference equipment, configuration optimization operation and Pareto optimal configuration selection based on a maximum and minimum target distance method. N configurations Xl (l = 1, 2,..., n) are randomly generated, each ant l represents one configuration Xl, and initial configurations of the n ants are obtained; performing legalization operation on the configuration X1 by adopting a gradient method based on dynamic step length, executing a movement strategy based on reference equipment to obtain a group of compact and legal configurations, and recording the configurations as a configuration library BL; selecting all non-dominated configurations in the set BL, and storing the non-dominated configurations in an external document CS; and for the current ant l, generating a random number o, and judging the size relationship between the random number o and the parameter p to select an optimization strategy, when o is greater than p, selecting to execute a local search strategy based on improved pseudo-random proportion, otherwise, using a global optimization strategy based on ecological niche.

The invention discloses a parking lot recommendation method based on an improved ant colony algorithm, and the method comprises the steps: S1, setting the number of ants and a pheromone matrix, and determining a weight parameter of a heuristic function; s2, in the current iteration, each ant starts from a starting point, a next node is selected by adopting a roulette strategy according to the pheromone concentration between the nodes and a heuristic function value until the next node reaches an end point, a complete path is formed, and in the path construction process, part of ants execute a random walking strategy to perform global exploration; s3, after all the ants complete path construction, a pheromone matrix is updated uniformly according to the path advantages and disadvantages of all the ants, and the updating process does not include a pheromone volatilization step; and S4, the steps S2 to S3 are executed repeatedly until the iteration termination condition is met, the global optimal path in all iterations is output as a recommendation result, and the recommendation result at least comprises the target parking lot, the distance and the passing time information. The method is suitable for parking lot recommendation.