187 results about "Population diversity" patented technology

The invention discloses a path planning heuristic function generation platform and method based on collaborative optimization of a large-scale language model and evolutionary computation. According to the technology, the large-scale language model (LLM) and evolutionary computation (EC) work cooperatively. The platform generates or mutates a heuristic function expressed as an executable code through LLM based on a structured prompt containing an environment context and performance feedback; and an EC framework (such as genetic programming) is combined with performance evaluation feedback to perform selection and iterative optimization on a heuristic code population, and population diversity is maintained. The method aims at overcoming the limitation that a traditional heuristic design is difficult and poor in adaptability, a high-quality heuristic function adapting to a complex and dynamic environment is automatically generated, and therefore the efficiency of a path planning algorithm and path quality are remarkably improved.

The invention discloses an unmanned aerial vehicle path planning method and system for a complex urban environment, and relates to the technical field of unmanned aerial vehicle path planning. According to the method, a plurality of algorithms are deeply fused, and a combination mode and an information transmission and optimization result inheritance mechanism between stages are beneficial to path planning of the unmanned aerial vehicle; a self-adaptive learning factor with a time-varying period is designed, the global exploration and local development balance capability of PSO is remarkably improved, and the population diversity and the global search capability are enhanced; through rapid global guidance of PSO, population dynamic enhancement of SSA, solution space depth optimization of GA and final precision improvement of LS, the path of the unmanned aerial vehicle can be rapidly and efficiently planned.

The invention discloses an engineering optimization method based on an improved particle swarm algorithm, and the method comprises the steps: constructing a particle swarm optimization model according to an engineering problem mathematical model, and initializing the position and speed of particles; then, calculating a fitness value index and a diversity index of the particles according to the model, and constructing convergence and diversity data according to the fitness value index and the diversity index; the two kinds of data are used for updating the initial positions and speeds of the particles, and the middle adjustment positions and speeds are obtained. Then, on the basis of the particle swarm optimization model and the intermediate adjustment position, the particle individuals and the global position are updated, and individual convergence and global convergence positions are obtained; and calculating a particle swarm output result according to convergence and diversity data and individual and global convergence positions. And finally, obtaining an optimal solution of the engineering problem according to an output result. According to the embodiment of the invention, two kinds of data are constructed by taking population diversity and fitness value as evaluation criteria, the population position and speed are updated, the optimal solution can be found more quickly and accurately, and the engineering optimization problem is efficiently solved.

The invention relates to a fuzzy flexible job shop scheduling method, a fuzzy flexible job shop scheduling system, fuzzy flexible job shop scheduling equipment and a storage medium, which are applied to a scene in which scheduling is required when disturbance occurs in a job shop operation process. Parameters of the genetic algorithm are initialized according to the real-time resource state of the current production environment, wherein the parameters comprise the maximum number of iterations; an initial population is generated according to the parameters, a weight vector is initialized, an external file is created, the external file is used for storing an optimal solution or an optimal solution in the algorithm, and the optimal solution is a solution capable of increasing population diversity; selecting an operation individual in the population and the external archive; and performing iterative optimization on the population based on the operation individuals until the maximum number of iterations is reached, outputting a non-dominated solution set, and after each iterative optimization, updating the external archive and the weight vector based on the generated non-dominated solution so as to optimize the fuzzy flexible job shop scheduling problem under the urgent order insertion and improve the production efficiency and the response flexibility.

The invention discloses a method for dispatching automatic guided vehicles in a parallel arrangement container terminal. The method comprises the following steps: collecting container terminal AGV dispatching parameters; establishing an AGV scheduling optimization model; solving the AGV scheduling optimization model; and outputting an AGV scheduling scheme. The AGV scheduling optimization model with minimum AGV power consumption and minimum operation completion time as optimization objectives is constructed by considering the influence of AGV path conflicts and the charging process on actual operation, so that collaborative optimization of AGV charging, task assignment and path planning problems is realized, and the method has the characteristics of high universality and wide coverage. According to the method, the improved adaptive genetic algorithm is adopted, and adaptive adjustment of crossover and mutation operators gives consideration to population diversity and convergence rate at the same time. And meanwhile, conflict-free path planning is carried out by adopting a space-time A * algorithm, so that collaborative optimization of AGV task scheduling and path planning is realized, the prediction precision is high, the convergence is fast, and the workload is small.

The invention discloses a cable operation state fault rapid detection method based on an improved grey wolf algorithm model. The method comprises the following steps: collecting fault data through a detection unit; cleaning, denoising, normalization processing and feature extraction are carried out on the collected fault data; constructing an improved grey wolf algorithm model, wherein the improved grey wolf algorithm model comprises a coding module, an adaptive weight adjustment module, a multi-objective optimization module, a population diversity control module and an optimal path search module; fault detection is carried out on the cable operation state data through a global search module and a local search module; real-time monitoring and early warning are carried out, wherein the real-time monitoring and early warning comprise early warning threshold setting, early warning signal triggering, early warning notification and feedback optimization; according to the invention, the operation state fault of the cable is detected through the improved grey wolf algorithm model, and the detection efficiency and the accuracy of cable fault positioning are greatly improved.

The invention is suitable for the technical field of structure optimization design, and provides a tension-compression spring design method based on an improved particle swarm optimization algorithm. According to the improved particle swarm optimization (SuperMOP) algorithm provided by the invention, an elite archive strategy and an advantage combination learning strategy are fused, and a mutation operator based on an individual level is introduced. Different roles are allocated to particles according to the performance of the particles, so that a learning strategy is dynamically updated. The improved particle swarm optimization algorithm is compared with several most advanced algorithms, and the result shows that the SuperMOP algorithm effectively balances the exploration and utilization process, improves the population diversity and enhances the diversity of learning samples. According to the method, the improved particle swarm optimization algorithm is applied to the design problem of the tension-compression spring, a new mode with strong global search capability is provided for the design problem of the tension-compression spring, and the method is particularly suitable for solving the optimization problem of complex and changeable constraint engineering.

The invention relates to the technical field of panel processing, in particular to a panel furniture workpiece typesetting method based on an improved genetic algorithm, which comprises the following steps: acquiring parameter information of a to-be-typeset workpiece; and performing classification based on the parameter information of all the workpieces to be typeset to obtain a plurality of workpiece categories, endowing each workpiece category with a unique type code, constructing an initial population according to the category and the number of the workpieces to be typeset, and inputting the initial population into the improved genetic algorithm to obtain an optimal chromosome. According to the method, the workpieces with the same size and shape are classified into workpieces with the same specification, and a repeated gene coding mode is adopted, so that the defect that actual repeated chromosomes are generated when a plurality of workpieces with the same specification are processed in a traditional unique coding mode is effectively avoided, and the algorithm efficiency is improved. And secondly, in the gene crossover operation, an improved ordered crossover and order crossover strategy is introduced, and a random deletion mechanism is combined, so that the algorithm population diversity and the solution space coverage are effectively improved, and the search divergence and convergence efficiency are improved.

The invention discloses an unmanned aerial vehicle flight path planning method based on a hybrid WOA-CEM-DRL algorithm, and the method is based on a hybrid WOA (whale optimization algorithm), a cross entropy method (CEM) and deep reinforcement learning (DRL), and aims at solving the problems that an existing algorithm is slow in convergence speed and is liable to fall into a local optimal solution in a complex three-dimensional environment. The invention provides a three-stage dynamic coevolution framework. In the early stage of evolution, population diversity is enhanced through global disturbance of CEM; in the middle stage of evolution, exploration and development are balanced; and in the later stage of evolution, performing fine tuning on the elite solution by using gradient information of the DRL. According to the method, global search of WOA, efficient sampling of CEM and rapid convergence capability of DRL are fused, and the method is mainly used for accurately generating a safe and smooth optimal track for an unmanned aerial vehicle in a complex three-dimensional environment.

The invention discloses a neural architecture search method based on a diffusion evolution algorithm, and the method comprises the steps: firstly constructing a supernet which comprises subnets, and enabling each subnet to give a weight to each operation on each connection edge through continuous coding; then obtaining different types of pictures to construct a data set; and finally, performing alternative optimization on the super-network weight and the sub-network codes, stopping optimization until constraint conditions are met, obtaining optimal sub-network codes, and further obtaining the architecture of the neural network. According to the method, an iterative denoising mechanism of a diffusion model and a global search strategy of an evolutionary algorithm are creatively fused, a super network is constructed by combining the denoising mechanism of the diffusion model and the global search capability of the evolutionary algorithm, and network weight and subnetwork coding are alternately optimized; through adaptive noise scheduling and density estimation, population diversity is enhanced, local optimum is avoided, and an optimal neural network architecture suitable for a task can be better found.

The invention discloses a multi-mechanical-arm accurate assembly method for double-population evolution reinforcement learning, and the method comprises the steps: constructing a partially observable Markov game model, designing a mixed reward function, and building a double-population mechanism: an Exploit main population is responsible for optimizing the performance, and an Explore auxiliary population maintains the diversity; extracting unified feature representation through a shared observation embedded network, optimizing a main population by adopting conjugate gradient differential evolution, and optimizing an auxiliary population by adopting novelty driven evolution; promoting information interaction between the double populations through an elite exchange mechanism, and utilizing parameter migration fusion to reinforce learning and evolutionary optimization results; and finally, an optimal strategy set for balancing accumulated rewards and novelty is selected through Pareto optimization, and the optimal strategy set is deployed to a multi-mechanical-arm system to execute a cooperative task after convergence conditions are met. According to the method, the population diversity is effectively maintained and the cooperation robustness is improved while the high performance of the strategy is ensured; and a powerful technical support is provided for automatic assembly in a complex industrial scene.

The invention discloses an unmanned aerial vehicle cluster task allocation method based on a large language model optimization genetic algorithm, and belongs to the field of computers. The method comprises the following steps: setting a specific chromosome coding mode; generating a multi-constraint initial population; calculating fitness to quantify the advantages and disadvantages of individual genes of the population; when the optimal individual meets the requirement or the maximum iteration round is reached, ending; retaining the optimal individual as a filial generation; generating a batch of new filial generation individuals by the large language model, and fusing the new filial generation individuals with the current filial generation population; calling an optimized large language model to analyze individual chromosome semantics, and outputting an evolutionary potential score; obtaining an individual comprehensive selection probability by integrating the fitness and the evolution potential score, and executing a selection operation; and selecting individuals based on the individual comprehensive selection probability to carry out crossover and mutation operation to generate offspring. The large language model is embedded into the core link of the genetic algorithm, and the algorithm efficiency is improved by improving the population diversity of the genetic algorithm in the unmanned aerial vehicle cluster task allocation scene.

The invention discloses an unmanned aerial vehicle area coverage flight path planning method and system based on graph segmentation, and relates to the technical field of unmanned aerial vehicle path planning. According to the method, a coverage point pool with high quality, comprehensive coverage and robustness is constructed for subsequent greedy selection through a strategy of adaptively generating candidate points; then, a greedy selection mechanism based on effective scores is adopted, the current optimal coverage point is accurately selected in each round of iteration, the new coverage area can be increased to the maximum extent, the overlapping area can be effectively controlled, and finally the whole target area is completely covered with the minimum number of circles. In the aspect of a path planning algorithm, a hybrid initialization mode of a greedy algorithm and a random generation strategy is innovatively combined, and the quality of an initial solution and population diversity are ingeniously balanced; meanwhile, in each generation of evolution of the genetic algorithm, a 2-opt local search strategy is embedded into a new non-elite individual, so that the convergence speed of the algorithm is increased, and the path optimization efficiency is remarkably improved. In conclusion, more efficient and more reliable planning of the unmanned aerial vehicle area coverage flight path can be realized.

The invention belongs to the technical field of semi-active suspension control methods, and relates to a semi-active suspension control method based on an improved multi-objective particle swarm optimization (MOPSO) algorithm, which comprises the following steps of: performing adaptive grid division on an external file of the MOPSO algorithm to determine global optimum with the most diversity and global optimum with the most convergence; judging the evolution state of the population according to the distribution entropy variation of all optimal solutions in an external file, and selecting a global optimal gBest for guiding evolution; according to the variable quantity of the local crowding distance of the current population evolution, adjusting flight parameters of population evolution to balance the evolution trend of the population; and fusing the two core mechanisms, providing a multi-information fusion multi-target particle swarm optimization algorithm, and applying the multi-information fusion multi-target particle swarm optimization algorithm to determination of a semi-active suspension LQR control strategy weight coefficient. According to the novel multi-objective optimization algorithm, external archive information and current population dynamic information are cooperatively integrated, so that balance between convergence precision and population diversity is effectively realized.

The invention provides a building component prefabrication task scheduling method based on quantum inspiration, and relates to the technical field of task collaborative scheduling, and the method comprises the steps: abstracting a building prefabrication task into a quantum optimization model, and coding a task allocation scheme through a quantum bit probability amplitude; and the quantum superposition state is collapsed into a classical feasible solution through Monte Carlo observation, and multi-target quantitative evaluation is carried out by using a weighting function. A controlled phase gate is introduced to realize directional crossing among chromosomes, a simulated annealing temperature attenuation strategy is fused to dynamically adjust variation intensity, probability amplitude distribution is adjusted in real time according to fitness difference by using an adaptive quantum revolving door, and population quality is continuously improved. Monitoring resource load and process dependence, and performing intelligent repair and elastic scheduling when a conflict or overrun occurs; and iteration termination adopts triple criteria of an algebraic threshold value, a fitness improvement rate and population diversity, after convergence, the final generation individuals are comprehensively weighed, and a global optimal scheduling scheme is output.

The invention discloses a multi-strategy improved fox-monkey optimization algorithm for global optimization and application thereof, and belongs to the technical field of optimization algorithms, the method comprises the following steps: firstly, using Chebyshev chaotic mapping to initialize a population so as to improve the diversity and coverage range of the initial population; secondly, a difference population evolution strategy is introduced in the iteration process, and the algorithm is prevented from falling into local optimum in the later period; and finally, introducing a crisscross strategy to enhance global search capability and maintain population diversity. 20 benchmark test functions and a part of complex functions in a cec2017 function set are adopted for a simulation experiment, and the experiment result shows that the optimization precision, the convergence speed and the stability of the improved algorithm are obviously improved. The improved algorithm is applied to the engineering optimization problem, compared with an original algorithm, the convergence precision of the algorithm in three actual engineering problems is improved by 19.56%, 19.18% and 6.7% respectively, the standard deviation is reduced by 91.44%, 27.56% and 92.19% respectively, and the feasibility of the improved algorithm is further verified.

The invention belongs to the field of water-wind-light multi-energy complementary optimal scheduling, and particularly discloses a water-wind-light optimal scheduling method and system based on a two-stage dual-population evolutionary algorithm, and the method comprises the steps: taking the water level of a reservoir as a decision variable, taking the maximum power generation amount and the minimum residual load mean square deviation as objective functions, and constructing a multi-objective scheduling model; the two populations are initialized, and the water-wind-light multi-target scheduling model is solved; the two populations are initially in a fixed division stage, and operators A and B are respectively adopted for iterative updating; when a switching condition is met, switching to a self-adaptive cooperation stage for iterative updating, determining the selection probability of the operators A and B according to population performance at the moment, and selecting the operators based on the selection probability for iterative updating; the switching condition is that after the population is iteratively updated each time, if the population diversity is smaller than a threshold value or the evaluation frequency reaches the threshold value, stage switching is carried out. According to the method, the contradiction between convergence and diversity in water-wind-light multi-objective optimization can be solved, and accurate optimization is realized.

The invention discloses an electromagnetic pulse coupling signal separation method based on optimized variational mode decomposition, and belongs to the field of signal processing. The method comprises the following steps: carrying out injection test on an engine rotating speed control system shielding wire harness, and constructing an electromagnetic pulse coupling signal data set; a population initialization process of a parrot algorithm is improved by adopting spatial pyramid matching chaotic mapping, population diversity is improved by utilizing uniform ergodicity and initial value sensitivity of a chaotic sequence, and premature convergence is avoided; a mirror reflection learning strategy is introduced to improve the optimization precision and convergence speed of the parrot algorithm; optimization is carried out by using input parameters of improved parrot optimization variational mode decomposition, and mode decomposition is carried out on electromagnetic pulse coupling signals of a rotating speed control system; and calculating a kurtosis factor of each intrinsic mode function, and selecting a reconstruction target signal of the intrinsic mode function with the minimum kurtosis factor. According to the method, efficient and accurate electromagnetic pulse coupling signal separation can be realized, and the stability and reliability of the system in a strong electromagnetic interference environment are improved.

The invention provides a hybrid MPPT (Maximum Power Point Tracking) control method based on a chaos initialization Levy flight Swane algorithm and an improved conductance increment method, belongs to the field of photovoltaic power generation systems, and aims at enhancing population diversity through chaos initialization, improving global search capability in combination with a Levy flight strategy and switching to the improved conductance increment method in an optimal solution neighborhood to realize high-precision tracking. The Logistic chaotic mapping initialization is combined to solve the problem that local optimum is caused by periodic values or uneven distribution easily occurring in the random initialization of the traditional South goose algorithm, the Brownian motion of the original South goose algorithm is changed into the Levy flight, the dynamic beta is increased, the global exploration and local exploration capabilities are balanced, local optimum is avoided, the improved conductance increment method is combined, and the local optimization is improved. And finally, the optimal duty ratio is screened by using a sliding window to lock the output, so that the problem of insufficient MPPT precision of a traditional optimization algorithm is solved, and the problem of power jitter near the maximum power point of a traditional conductance increment method is solved.

The invention relates to the technical field of intelligent network connection vehicle cluster decision making, in particular to an intelligent network connection vehicle cluster macro-micro integrated alliance decision making method, which constructs a cluster alliance decision making cost function by constructing an initial path of an intelligent network connection vehicle cluster and considering safety, comfort, efficiency and energy consumption targets. Under the condition that kinematics constraint and comfort conditions of the intelligent network connection automobile are met, an adaptive coevolution algorithm is used for solving, a diversity-based search distribution mechanism is adopted in the adaptive coevolution algorithm for population diversity distribution and traffic flow guide variation, a global exploration module and a local optimization module are optimized respectively, and a global optimization module and a local optimization module are optimized respectively. And operations such as periodic global optimization are carried out to generate an optimal intelligent network connection vehicle cluster decision, so that the vehicle can reduce energy consumption and improve comfort on the premise of ensuring the safety to the maximum extent and improving the efficiency.

The invention belongs to the technical field of ship block two-dimensional irregular layout, and discloses a ship block layout method based on a critical polygon and a dynamic niche. The method comprises the following steps: constructing an NFP solving method based on an edge-vertex contact point convex hull to obtain a critical polygon, constructing a BL positioning stacking strategy on the basis to determine a feasible region, realizing accurate positioning of the irregular polygon, and calculating the fitness; designing a multi-stage adaptive genetic algorithm parameter collaborative optimization method, and dynamically adjusting genetic algorithm parameters based on population diversity and fitness variance; a dynamic niche management and crowding punishment mechanism is introduced, premature convergence of populations is avoided, and diversity of solutions is maintained; and carrying out refined optimization on the polygon rotation angle through genetic post-processing, and preferentially selecting a layout scheme. According to the method, the space utilization rate of the two-dimensional irregular layout can be effectively increased, the complex shape characteristic of the segments is fully reserved in the stacking process, and the method is suitable for the segmented stacking scene of ship construction.

The invention relates to a dynamic multi-objective optimization method and device, equipment and a medium. The method comprises the following steps: establishing an event model of a multi-objective optimization event; if it is detected that the environmental information changes, a first group and a second group are obtained based on a multi-objective evolutionary algorithm and a historical optimal set, an original optimal set of an event model is determined according to the first group and the second group, the first group is obtained based on a representative individual prediction algorithm, and the second group is obtained based on an inflection point prediction algorithm; otherwise, taking the Pareto optimal solution of the event model at the current moment as an original optimal set; and inputting the original optimal set into a social optimization learning model, and outputting a target optimal set of the event model through the social optimization learning model. According to the method, when the change of the environment information of the multi-target optimization event is detected, the target optimal set is output through the social optimization learning model by improving the population diversity and improving the precision of the Pareto optimal solution from the perspective of the population diversity.

The invention relates to the field of data processing, in particular to a high-throughput reaction screening system and method based on computer control and data processing. The method comprises the following steps: executing a parallel chemical reaction experiment, obtaining experiment parameters and experiment reaction results, and carrying out standardization processing to obtain standardized parameter values and standardized experiment reaction results; calculating the relative weight of the experimental parameters by analyzing the correlation between the experimental parameters and the experimental reaction results; and based on the relative weights of the standardized parameter values and the experimental parameters, selecting an optimal parameter vector through a high-throughput reaction parameter screening algorithm for guiding a subsequent experiment. The problems that in a chemical reaction screening method, experimental parameter importance is difficult to quantify, fitness is evaluated only based on a reaction result, and a suboptimal solution may be mistakenly selected are solved; in the optimization process, the population diversity is possibly reduced due to insufficient variation, or the optimal solution cannot be converged due to excessive variation, so that the optimization effect of the chemical experiment condition is influenced.

The invention discloses a task allocation method and system based on an improved whale optimization algorithm framework, relates to the technical field, and is used for optimizing order type adaptive cross-domain traffic control network task allocation and improving the key task response capability of a time-sensitive traffic system. ICWOA initializes a population through Chebyshev mapping, and introduces Levy flight disturbance to enhance the optimization ability; dynamically balancing local and global search by means of adaptive parameters; relieving population diversity attenuation through randomness retention, diversity maintenance and boundary constraint; dimension type pinhole imaging reverse learning is fused to reduce high-dimensional optimization dimension interference. According to the algorithm, the convergence speed and the solving precision are better, sub-second calculation time is kept under different task scales, the task distribution efficiency is improved, and the time-sensitive scene task success rate is remarkably improved. The method solves the problems that an existing algorithm is insufficient in adaptive order type'order application-order sending 'structure and prone to falling into local optimum, population diversity attenuation and calculation speed.

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 relates to the technical field of hydraulic engineering safety monitoring and data analysis, in particular to a dam deformation prediction method based on multi-source data dynamic fusion and a related product, and the method comprises the steps: obtaining multi-source monitoring data to construct an extended feature set; generating a weighted fusion input sequence; carrying out primary modal decomposition and secondary modal decomposition, and recombining into three types of components; obtaining a prediction result of each component; and outputting a dam deformation prediction result. According to the method, a target-oriented feature weight matrix dynamic updating mechanism is constructed, so that the problem that the actual loading state of the dam cannot be reflected by traditional static weighting is solved; by adopting a selective secondary decomposition strategy based on a modal aliasing criterion, redundant calculation of non-aliasing components is avoided while modal aliasing is effectively eliminated and high-purity characteristic components are extracted; by performing hybrid optimization on the neural network parameters, the convergence speed and population diversity are considered, the global optimality of the model parameters is ensured, and the prediction error is reduced.

The invention discloses a distributed wind power site selection and optimization method, system and device and a medium, and the method comprises the steps: obtaining wind resource data of a candidate wind power plant region, and building a multi-factor physical model; constructing a multi-factor wind field site selection model, and calculating optimal solutions of total power generation power maximization and total power generation cost minimization; and determining an optimal site selection scheme by combining the model optimal solution, wind resource distribution and topographic conditions. By improving the reserved suboptimal solution of the NSGA2 algorithm and dynamically adjusting the crossover and mutation probability, the population diversity is effectively maintained, the global search ability is enhanced, and a decision maker is supported to select the optimal layout; wind resource characteristics are comprehensively quantified through a multi-factor physical model, and the physical accuracy of site selection analysis is improved; through model hypothesis, the model complexity is reduced on the premise of ensuring the reliability, and the calculation efficiency is improved; the balance between economical efficiency and power generation efficiency is realized by taking minimization of power generation cost and maximization of power generation power as targets through a multi-factor wind field site selection model.

The invention discloses a dynamic cooperative wireless charging system control method based on a genetic gradient optimization algorithm, and the method comprises the steps: carrying out the global search based on a genetic algorithm, taking the transmitting power and phase angle of a transmitting coil of a group of transmitting arrays as particles for coding, and evaluating the adaptation degree of individuals in a population through building a fitness function. Iteration of the population is realized through roulette selection and self-adaptive crossover and variation; when the population diversity is insufficient, triggering gradient descent to carry out local deep optimization on the optimal individual; selecting excellent individuals of current genetic iteration, and performing gradient calculation and updating by taking phase angle parameters of the excellent individuals as particles; and combining the phase angle parameter with the power parameter to form a new population, sorting individuals in the new population according to fitness, retaining the individuals with the fitness being first 10%, continuing to enter genetic algorithm iteration, and starting a new round of circulation. The energy efficiency of the system is improved, and the convergence speed of the algorithm and the electromagnetic safety are optimized.

The application aims at providing a multi-stage multi-level assembly job shop energy-saving scheduling multi-objective optimization method, relates to the technical field of assembly shop scheduling, and establishes a mathematical model of the problem in stages, and designs a problem-driven energy-saving strategy triggering mechanism according to the individual state, so that the quality and search efficiency of the solution are improved. Secondly, two heuristic rules and a random generation method are used to construct an initialization population that takes into account high quality and diversity, and a dynamic self-adaptive adjustment strategy for the assimilation operator parameters is realized through Q-learning, so that the convergence speed is improved while the population diversity is ensured, so that the exploration and mining ability of the algorithm is better balanced. A revolutionary operation guided by a hyper-heuristic variable neighborhood search is designed, and a high-quality colony found is searched in detail. The joint empire invasion operation is used to replace the competition, realizes the cooperative evolution and information interaction sharing of multiple empires, and thus finds non-dominated solutions with more uniform distribution.

The invention discloses an electric vehicle charging station planning and pricing method based on an existing charging station, and the method achieves the deep fusion of a charging station extension decision and an operation strategy through the construction of a unified framework of planning and pricing collaborative optimization, solves a problem that the overall optimization effect is not good because of the disjunction of the two in an existing method, and improves the efficiency of electric vehicle charging station planning and pricing. And the economical efficiency and the practicability of the scheme are improved. According to the improved chaos non-uniform mutation artificial hummingbird algorithm provided by the method, population diversity is ensured through chaos initialization, and a search behavior is dynamically adjusted by adopting an adaptive mutation strategy, so that the problems that a traditional optimization algorithm is easy to fall into local optimum and slow in convergence speed when processing a high-dimensional, nonlinear and multi-constraint mixed integer programming problem are effectively solved. The Pareto optimal solution set finally output by the method provides rich tradeoff selection schemes for decision makers, scientific decision making according to different preferences (such as cost minimization or user satisfaction maximization) is supported, and the flexibility and applicability of the method are enhanced.