21 results about "Memetic algorithm" patented technology

The invention relates to a multi-target logistics distribution path optimization method based on AI, and the method comprises the following steps: obtaining order data, road network data and vehicle data of logistics distribution of a customer point, extracting basic information from the three types of data, and constructing the multi-dimensional features of the customer point; inputting the multi-dimensional features of the customer points into a pre-trained graph attention network for feature embedding, generating a distribution network feature graph fusing customer demands and a road network relationship, inputting a pre-trained reinforcement learning agent, and generating an initial path population with quality and diversity balance; performing iterative optimization on the initial path population based on a Memetic algorithm framework; and calculating the fitness of each individual on a plurality of preset optimization objectives, maintaining a Pareto solution set by adopting a multi-objective evolutionary algorithm with reference points, and recommending a final path scheme from the Pareto solution set based on user preferences. The method has the effect of remarkably improving the quality, efficiency and dynamic adaptability of path optimization.

The application belongs to the field of workshop scheduling, and aims at solving the problem that the existing production scheduling method is only applicable to the homogeneous factory model.The method comprises the following steps: S1, establishing a distributed heterogeneous flow shop scheduling model; S2, initializing the basic parameters of the meme algorithm, and generating an initial population according to a hybrid initialization strategy; S3, evaluating the fitness value of the population individuals, and performing a Pareto non-dominated sorting; S4, performing a crossover operation on the individuals in the population with a certain probability; S5, performing a collaborative search on the population by using a search operator; S6, merging the offspring population and the parent population, and selecting half of the individuals as local search targets; S7, performing a local search on the selected target individuals; S8, updating the population; S9, if the algorithm meets the stopping condition, ending the algorithm process, and outputting a non-dominated solution set; otherwise, the algorithm goes to S4 and continues iteration.

The invention discloses a hybrid production flexible assembly job shop scheduling method considering multi-assembly sequence change and transportation tasks based on a Q-Learning model algorithm. The method comprises the following steps: S1, establishing a scheduling model taking total production completion time, total inventory time and total manpower cost as optimization targets; s2, providing a Q-Learning memetic algorithm to solve the scheduling model, designing a five-layer segmented mixed chromosome coding structure and a two-subgeneration chromosome updating method, and introducing a variable neighborhood search strategy and an elite retention strategy; s3, adaptively adjusting the cross range of the chromosomes through Q-Learning to improve the algorithm efficiency; and S4, through comparison with other three algorithms, validity and robustness of the proposed algorithm are verified. Based on the scheduling method provided by the invention, the processing and assembling sequences of product parts can be optimized simultaneously in the scheduling process of multi-product mixed production and consideration of transportation tasks, and meanwhile, machines, workers and AGV resources are reasonably allocated, so that the production cycle of products is effectively shortened.

The invention relates to a hydro meteorology change point detection method and system, and belongs to the technical field of hydro meteorology scientificity, and the method comprises the steps: obtaining the observation data of a hydro meteorology time sequence; the number of change points of the observation data is initially 1; judging whether the number of change points is greater than a given maximum value or not; when the number of the change points is smaller than or equal to the maximum value, calculating the change rate of the fitness function under the current number of the change points according to a change point set obtained by carrying out change point detection on the observation data by utilizing a memetic algorithm and a corresponding fitness function value, and adding 1 to the number of the change points until the number of the change points is larger than the maximum value; and when the number of change points is greater than a given maximum value, determining an optimal number of change points and an optimal change point set according to the change rate of the fitness function corresponding to different numbers of change points. According to the invention, the dynamic change of the time sequence data can be adaptively processed, and the performance of hydro-meteorological change point detection is improved.

The present application relates to the technical field of inspection, and discloses a campus open space room heterogeneous unmanned cluster three-dimensional security inspection method, comprising: a central control platform based on an event type-atomic task-robot capability mapping knowledge base, decomposing a complex event into an atomic task set, and defining a capability demand vector; collecting real-time states of robots, constructing a constrained multi-objective optimization problem, and using a two-stage collaborative solving method combining an improved meme algorithm and conflict-oriented search to output an optimal task allocation and collaborative space-time path plan; issuing instructions through wireless communication, and robots working collaboratively and uploading data in real time; the central control platform fuses and analyzes data, triggers a rapid generation of an adjustment scheme when re-planning, and collects data after the completion of a task; to solve the technical problem that heterogeneous robots lack intelligent task allocation capability in a three-dimensional complex environment, resulting in low inspection collaboration efficiency.

The invention discloses a hybrid production flexible assembly job shop scheduling method considering multi-assembly sequence change based on a hybrid memetic algorithm, and the method comprises the following steps: S1, building a hybrid production flexible assembly job shop scheduling model with total production completion time, total inventory time and total labor cost as optimization targets under dual-resource constraint; a flexible assembly job shop scheduling mathematical model for multi-assembly sequence change mixed production is considered; s2, on the established mathematical model, providing a hybrid memetic algorithm to solve the provided problem, designing a four-layer segmented hybrid chromosome coding structure adapted to the problem and an updating method of two sub-generation chromosomes, and introducing a variable neighborhood search strategy and an elite retention strategy; s3, proving the superiority of the proposed scheduling method through case research; and S4, through comparison with a flower pollination algorithm and a rapid non-dominated genetic algorithm, verifying the effectiveness and robustness of the proposed algorithm. According to the hybrid production flexible assembly job shop scheduling method considering the multi-assembly sequence change based on the hybrid memetic algorithm, the processing sequence and the assembly sequence of product parts can be optimized at the same time in the multi-product hybrid production scheduling process, and machine and worker resources are reasonably distributed at the same time; and the production cycle of products is effectively shortened.

The invention discloses an unmanned aerial vehicle-unmanned vehicle air-ground cooperative logistics scheduling method, system and device, a medium and a product, and relates to the field of logistics distribution, and the method comprises the steps: obtaining the state parameter information of a logistics entity; on the basis of the state parameter information, a collaborative logistics mixed integer programming model is constructed by taking minimization of logistics scheduling completion total time as a target; and constructing an improved memetic algorithm, and performing iterative solution on the collaborative logistics mixed integer programming model through the improved memetic algorithm based on the state parameter information to obtain a collaborative logistics scheduling scheme. According to the method, the calculation cost can be effectively reduced, the solving efficiency and the practicability of the scheduling scheme are improved, and the real-time performance and safety of air-ground collaborative distribution are guaranteed.

The invention discloses an automatic attack countermeasure method based on a multi-target memetic algorithm, which comprises the following steps: sampling after sorting loss values from a complete data set, and selecting a small data set for rough evaluation; initializing a population, generating an initial population containing a plurality of random attack sequences, and evaluating the robust precision and time consumption of each individual by using a small data set; generating a progeny population through crossover and mutation operations, and evaluating individuals in the progeny population; performing non-dominated sorting and crowding degree distance calculation on individuals of the parent population and the descendant population, and selecting a certain number of excellent individuals to form a current population; optimal individuals in the current population are randomly selected, local search is carried out by adjusting a loss function, an iteration step length or a restart point, and iteration is continuously carried out until a preset termination condition is not met; and carrying out local search on a better individual by utilizing the complete data set, adjusting a loss function, an iterative step length or a restart point, and outputting an optimal adversarial attack combination mode.

The invention discloses a whole vehicle logistics loading optimization method based on uniform counterweight constraints, which comprises the following steps of: taking a to-be-transported commercial vehicle attribute set and an available car transport vehicle attribute set as input data, setting model parameters and algorithm parameters, setting decision variables and objective functions, setting uniform counterweight constraints, and constructing a mixed integer programming model; and solving the model by adopting a memetic algorithm, and taking an optimal solution of a target function value as a final scheduling scheme to realize transportation income maximization.

The invention discloses a multi-unmanned aerial vehicle multi-task target air route guiding method, and belongs to the field of unmanned aerial vehicle intelligent optimization algorithms. Providing a feasible waypoint set generation method for a multi-target collaborative detection scene; meanwhile, a feasible waypoint set reduction method is designed based on requirements in the aspects of unmanned aerial vehicle performance, sensor performance and communication conditions; in addition, a set of complete air route guiding conflict detection and resolution mechanism is also constructed, so that the continuity of the air route guiding solution of the unmanned aerial vehicle is ensured. And finally, establishing an optimal model of the feasible waypoint for flight, and resolving by means of an improved memetic algorithm, thereby successfully implementing rapid and stable tracking detection on a plurality of targets on the premise of guaranteeing collaborative operation.

The invention discloses a full-precision model scheduling method for a super-sensitive earth observation satellite, which comprises the following steps of: 1, accurately calculating attitude switching time of adjacent tasks through an Euler angle and a rotation matrix, 2, searching task insertion time based on attitude switching satisfaction deviation values of the adjacent tasks on two sides, and simultaneously realizing a local search algorithm for scheduling the super-sensitive satellite. Step 3, based on discrete integer coding and minimum graph decoding, realizing optimal path exchange and reserved crossover and mutation operators, and further realizing a global search evolutionary algorithm of hypersensitive satellite scheduling; step 4, based on a memetic algorithm combining the step 2 and the step 3, realizing scheduling high-efficiency solution of the full-precision model of the hypersensitive satellite; according to the method, the problem of efficient scheduling of the full-precision model of the hypersensitive satellite is solved.

The invention discloses a transient stability assessment method and system for a power system. The method comprises the following steps: firstly, acquiring multi-dimensional real-time electric quantity data of a power system; thirdly, performing feature selection optimization by applying a knowledge-guided memetic algorithm, combining global evolutionary search and local fine search and fusing expert knowledge to obtain an optimal feature combination and a time window; then, constructing a reservoir calculation network formed by differential neuron ring oscillators based on the combination, and extracting deep dynamic features of the system; and finally, inputting the deep features into a CNN-LSTM hybrid model for evaluation, and obtaining a transient stability probability value. According to the method, through innovative feature optimization and dynamic feature extraction mechanisms and in combination with the deep fusion model, rapid and accurate evaluation of the transient stability of the power system is realized, and the safety protection level of a power grid is remarkably improved.

The application discloses a kind of high-efficiency dynamic roadside unit deployment methods and systems that can detect witch attack, including collection geographic information, assess the service coverage of roadside unit in deployment area, overlapping range and accident information initial diffusion speed, establish the RSU deployment constraint that can detect witch attack;Based on particle swarm optimization and improved genetic algorithm, RSU deployment scheme is constructed;Obtain transportation and reconstruction RSU overhead, obtain minimum RSU adjustment cost based on bipartite graph matching algorithm;Collect each RSU work load, and based on Pareto optimal principle, heuristic RSU minimum cost adjustment scheme is constructed.The application is under the constraint that can detect witch attack, by constructing the efficient deployment and low-cost adjustment scheme of heuristic RSU, improve the service effective coverage range in RSU deployment, accident information initial diffusion speed, minimum costly realize load balancing between RSU and have the ability of detecting witch attack, solve the problem that traditional RSU deployment scheme comprehensive performance is low, cannot detect witch attack.

The application belongs to the field of workshop scheduling, and aims at solving the problem of low efficiency of the existing scheduling method of heterogeneous factories. The application comprises the following steps: S1, constructing a distributed heterogeneous flow shop scheduling model; S2, determining the value of the basic parameters of the algorithm, and generating an initial population by using a random strategy according to the coding rules; S3, evaluating the initial population, and performing a Pareto non-dominated sorting according to the fitness value; S4, performing an EOX crossover operation on the population with two-layer coding; S5, searching the solution space by using multiple operators; S6, adopting an elite reservation strategy, merging the parent population and the child population, and selecting half of the individuals as the evolution target of the next stage; S7, performing an evolution operation of local search on the selected individuals; S8, updating the population; S9, if the algorithm reaches the stopping condition, the algorithm is ended and the result is output, otherwise, the algorithm jumps to step S4 and continues to execute.

This invention discloses a vehicle logistics loading optimization method based on uniform weight constraint. The method takes the set of attributes of the vehicles to be transported and the set of attributes of available car carriers as input data, sets model parameters and algorithm parameters, sets decision variables and objective function, sets uniform weight constraint to construct a mixed integer programming model, uses the meme algorithm to solve the model, and takes the optimal solution of the objective function value as the final scheduling scheme to maximize transportation revenue.

The invention discloses a multi-unmanned aerial vehicle multi-task target air route guiding method, and belongs to the field of unmanned aerial vehicle intelligent optimization algorithms. Providing a feasible waypoint set generation method for a multi-target collaborative detection scene; meanwhile, a feasible waypoint set reduction method is designed based on requirements in the aspects of unmanned aerial vehicle performance, sensor performance and communication conditions; in addition, a set of complete air route guiding conflict detection and resolution mechanism is also constructed, so that the continuity of the air route guiding solution of the unmanned aerial vehicle is ensured. And finally, establishing an optimal model of the feasible waypoint for flight, and resolving by means of an improved memetic algorithm, thereby successfully implementing rapid and stable tracking detection on a plurality of targets on the premise of guaranteeing collaborative operation.

The invention discloses a no-waiting flow shop bad scene set robust scheduling method based on a memetic algorithm, and the method comprises the steps: carrying out the processing time description of a to-be-processed workpiece set and a processing machine set through employing a discrete multi-scene method, defining a threshold value bad scene set according to a preset performance threshold value, and building a robust optimization model of the threshold value bad scene set. According to the method, a threshold value bad scene set robust scheduling model of a wait-free flow shop is established, and an optimization objective function is determined. An initial population is generated by using a scene-based NEH heuristic algorithm. And performing global search on the current population through a genetic operator and a selection mechanism, selecting a target scene by using a strategy related to the model, constructing a neighborhood structure by using knowledge related to problems in the target scene, and performing local search. The whole optimization process is circularly executed under iteration control until the preset maximum iteration number is reached, and finally the optimal scheduling solution in the optimized population is output. The method can effectively cope with the uncertainty of the processing time, improves the robustness and optimization efficiency of no-waiting flow shop scheduling, gives consideration to the global search capability and the local refinement capability, and achieves the quick solving of a high-quality scheduling scheme.

The present invention discloses a discrete orthogonal code sequence optimization method based on an iterative greedy code search meme algorithm, which belongs to the field of radar waveform design technology. The performance of the method proposed in the present invention is better than that of the currently available methods, and the autocorrelation and cross-correlation performance of the discrete orthogonal code sequence set optimized by the method of the present invention are further close to the existing lower bound, narrowing the difference with the existing lower bound. Compared with the greedy code search method, the iterative greedy code search strategy proposed in the present invention has stronger local search capabilities, better optimization effects and higher search efficiency. Moreover, this method can improve the final search optimization results of the algorithm without increasing the running time of the algorithm. By simultaneously using crossover and mutation operators in each iteration, the search diversity of the algorithm is increased, and the search space of the algorithm is expanded, thereby making it more likely to obtain a discrete orthogonal code sequence set with better orthogonality.

The application discloses a kind of post-disaster city full coverage detection methods based on heterogeneous unmanned system, belong to rescue technical field, including: unmanned aerial vehicle and unmanned vehicle are as heterogeneous unmanned system, construct heterogeneous unmanned system full coverage path planning model;Discretization is carried out to heterogeneous unmanned system full coverage path planning model, and the discrete optimization model of heterogeneous unmanned system full coverage path planning is obtained;The switch state of path point in discrete optimization model is optimized using improved gene algorithm, and the optimal open point set is obtained, the shortest full coverage detection path is obtained by traversing the optimal open point set using lawnmower algorithm;Construct charging point dynamic adjustment mechanism, based on shortest full coverage detection path and charging point dynamic adjustment mechanism carries out full coverage detection.The application effectively improves the global search capability and convergence speed of gene algorithm, designs the dynamic adjustment strategy of two-way meeting charging point, effectively reduces charging waiting time.

The invention discloses a cold rolling order selection optimization method considering multi-bottleneck unit productivity constraint, and belongs to the technical field of intelligent optimization and production scheduling optimization. According to the method, a mathematical model of an order selection problem considering multi-bottleneck unit capacity and order deadline is constructed, constraint conditions are set, and a memetic algorithm based on a Markov chain is used for solving; according to the method, a one-step transition probability model is constructed by fusing a Markov chain, and a high-quality new solution is generated by utilizing a state transition probability guide algorithm. And the quality and stability of the solution are improved by combining a feasibility repair operator and a local search operator considering the order delivery date and the capacity constraint, so that multi-scheme, high-quality and high-feasibility order selection decision support is provided for managers in an auxiliary manner. According to the method, on the premise of ensuring that the capacity constraint is satisfied, the order delivery rate on schedule and the resource utilization efficiency are improved, the method has good solving performance and industrial application value, and important guidance is provided for actual production scheduling of enterprises.

The invention discloses an unmanned aerial vehicle task path planning method based on a memetic algorithm, and the method comprises the steps: initializing a population, representing each gene in the population as an unmanned aerial vehicle task path point position sequence, enabling the first position in the unmanned aerial vehicle task path point position sequence to be a starting point position, enabling the position between the starting point position and an end point position to be an access point position, and enabling the access point position to be a target point position; a non-access point location is arranged behind the end point location; iteratively calculating the maximum total revenue of the genes in the population until the maximum number of iterations is reached or the number of iterations without improvement of the total revenue exceeds a threshold value, and obtaining an unmanned aerial vehicle task path according to the point location sequence of the genes with the maximum total revenue when the iterative calculation is finished; during each iterative calculation, two genes are subjected to cross processing, and a single gene is subjected to local optimization including displacement, exchange and damage-repair. According to the method, a high-quality solution is obtained while the convergence speed is ensured, and the balance of endurance safety, time cost and economic benefits is realized.