52 results about "Biobjective optimization" patented technology

The invention relates to the technical field of power-carbon collaboration, and discloses a block chain-based power-carbon collaboration data management and intelligent optimization method, system, device and medium, and the method comprises the steps: collecting power-carbon multi-source data in real time, and carrying out the data preprocessing; establishing a correlation model between electricity and carbon data so as to quantify a correlation relationship between unit power generation and carbon emission, and obtaining carbon emission reduction data; a double-objective optimization function is adopted to construct an electricity-carbon collaborative decision-making large model, and an optimal unit output scheme is output through deep learning; and generating a block chain hash identifier for each green electricity transaction, performing contract automatic verification, and if a performance risk is monitored, triggering early warning and sending an optimization suggestion. According to the invention, by constructing a technical system based on power-carbon collaborative data management and intelligent optimization, accurate conduction of carbon emission technical constraints, safety interaction of cross-system data, credible tracing of environmental rights and interests and dynamic prevention and control of risks are realized, and fusion and efficient collaborative operation of a power system and carbon emission management are promoted.

The invention discloses a wind power prediction model double-target countermeasure attack method, which belongs to the technical field of crossing of power system security and artificial intelligence, and comprises the following steps: S1, collecting original data and determining parameters; s2, training a time sequence diagram auto-encoder detection model; s3, constructing and training a wind power prediction model; s4, constructing a dual-objective optimization function; s5, iteratively generating a confrontation sample; and S6, adversarial sample injection and detection. According to the wind power prediction model dual-target countermeasure attack method, through dual-target weighting function and reconstruction loss item coefficient adjustment, joint adjustment and optimization of attack destructiveness and concealment are realized, the detection probability is reduced, and bias selection is supported; through the improved GAE structure, the time sequence dependence is captured, artificial feature design is avoided, and the accuracy of detecting the time sequence adversarial sample is improved.

The invention discloses an efficient multi-target unloading and scheduling optimization method for a large-scale directed acyclic graph (DAG) task, and belongs to the technical field of mobile edge computing (MEC) and industrial internet of things (IIOT). The method comprises the following steps: constructing a system model containing DAG task topology, delay energy consumption dual-objective optimization and resource competition constraint; a comprehensive multi-objective optimization solution scheme is adopted for solving, a dynamic probability coding mechanism is introduced to replace traditional individual representation, and the high-dimensional decision space search efficiency is improved; designing a cooperative target domain-based decomposition strategy to enhance convergence and diversity; and in combination with a serial active scheduling mechanism based on random weight, generating a task sequence meeting priority constraints. According to the method, the collaborative optimization problem of large-scale dependent tasks under delay, energy consumption and resource competition in an industrial Internet of Things environment is effectively solved, and the task execution efficiency and energy efficiency are remarkably improved.

The invention provides a robot delivery planning method based on full-life-cycle carbon emission evaluation and optimization. The robot delivery planning method comprises the following steps: S1, constructing a full-life-cycle total carbon emission model; s2, constructing a multi-objective optimization model: defining decision variables, constraint conditions and a dual-objective optimization model; s3, dynamically adjusting the weight of the multi-model optimization model, then solving the multi-target optimization model by adopting an NSGA-II algorithm, and obtaining a Pareto optimal solution set for a user to select a Pareto optimal solution therein; and S4, based on the selected robot deployment scheme, correcting the Pareto optimal solution set. According to the invention, economic delivery optimization of the robot is realized, and intelligent decision support is provided for low-carbon economic transformation.

The application discloses a kind of composite cemented filling material design method, and the application relates to the technical field of mine filling, comprising the following steps: determining optimization component and its optimization domain, and randomly generating multiple groups of design combinations, preparing filling material and filling to be filled sample to form composite filling body test piece;Sample test body is cured under simulated environment, with the degree of hydration heat reaction as index, dynamically adjust sampling interval and determine multiple sampling time;Coupling monitoring test is carried out using the detection method of pulse velocity and low-frequency dielectric spectrum combination, interface coupling degree parameter is extracted, and cementation coupling degree is calculated, and the termination time is determined based on its change rate;The structural strength at this time is obtained, and the preset strength is used as constraint, the cementation coupling degree maximization and the experimental time minimization are used as optimization goal, the double objective optimization function is set, and the optimal design combination is determined by optimization algorithm, which can significantly reduce engineering risk and prolong the service life of structure.

The invention discloses a reverse logistics planning system and method based on transfer learning, and the system comprises a reverse logistics planning module which is used for minimizing a dual-objective optimization problem and a constraint condition set of the total cost and the total disassembly processing time of a reverse logistics system, and obtaining a reverse logistics planning model; the optimization solving module is used for solving the reverse logistics planning model by adopting a two-stage multi-objective optimization algorithm to obtain a Pareto optimal solution set; and the transfer learning module is used for performing transfer learning on the Pareto optimal solution of the previous period to obtain the initial population when the significant change of the part demand is detected in the new period. According to the method, a two-stage multi-objective optimization algorithm combined with a transfer learning mechanism is utilized, and the problems of split optimization of disassembly line balance and vehicle path planning in reverse logistics and demand dynamic uncertainty are solved. The efficient balance between the total cost and the disassembly time of the system is realized, and the planning efficiency, the adaptive capability and the resource utilization rate are remarkably improved.

The invention relates to the technical field of flywheel energy storage system structure optimization, and discloses a flywheel energy storage system variable thickness flywheel profile optimization method, system, device and medium, and the method comprises the steps: building a dual-target optimization model, and taking the maximum unit mass energy storage density and the minimum maximum centrifugal stress as targets; the geometric boundary of the flywheel, the orthotropic property of the composite material and the lamination process constraint are considered, the reasonable and feasible design is ensured, and the failure states such as interlayer stripping or fiber fracture are reduced. And further parameterizing flywheel thickness distribution by adopting a third-order B-spline function, forming a design parameter space in combination with a fiber laying angle, setting a non-uniform node vector and an adjacent coefficient difference upper limit constraint, and ensuring radial smooth change of the thickness. An optimized data set is generated through finite element simulation based on a parameterized model, and a foundation is laid for physical information neural network training. The method provides a scientific, efficient and reliable design optimization scheme for the flywheel energy storage system, and assists energy transformation and sustainable development.

The invention provides a scheduling method for dual-objective optimization of a multi-agent flow shop under time-of-use electricity price, and relates to the technical field of intelligent manufacturing and production scheduling. According to the method, unified modeling is carried out on the total power cost TEC and the total customer completion time TCTA, and a dual-target mixed integer programming model is constructed; a double-node decision tree structure is constructed, and synchronous processing of task sorting and processing time interval distribution is achieved; analyzing the structural characteristics of the multi-agent flow shop under the constraint of time-of-use electricity price, designing a series of inequality constraints and multiple pruning rules, and compressing a search space on the premise of ensuring the integrity of a feasible solution; the method comprises the following steps of: performing dual-objective cooperative solution, embedding a branch and bound algorithm into an improved epsilon-constraint framework to perform iterative solution, accurately obtaining a Pareto frontier, and providing multiple optimal scheduling schemes balanced between cost and service quality for decision makers. According to the method, dual decisions of task sorting and cycle allocation can be realized, and the problems of decision model splitting, low solving algorithm efficiency and the like are solved.

The invention discloses an active power distribution network flexibility improvement method and system based on intelligent soft switch optimization, and the method comprises the steps: firstly constructing a network flexibility evaluation framework, and then constructing a deterministic planning model of intelligent soft switch optimization configuration based on an obtained flexibility evaluation index. Based on the deterministic planning model, introducing a scene-based maximum and minimum regret value method, and constructing an intelligent soft switch optimal configuration robust planning model; and finally, solving and verifying the model. According to the method, the maximum permeation capacity range of renewable energy is defined as a flexibility index, and the uncertainty of a new energy high-permeation scene can be more efficiently adapted; the economy and the flexibility of the power distribution network are considered through dual-objective optimization; the robust method based on the scene is adopted for solving, all considered renewable energy output fluctuation scenes can be covered, it is ensured that the obtained scheme has feasibility under various fluctuation working conditions, and the operation stability and the anti-interference capacity of the power distribution network are remarkably improved.

The invention discloses a multi-branch pipe flow uniformity optimization method based on valve equivalent resistance model mapping, which comprises the following steps of: 1, replacing a real valve with an equivalent resistance area, performing CFD (Computational Fluid Dynamics) simulation on a single valve and a single pipe, and establishing a mapping table of the opening degree, the viscous resistance of the equivalent resistance area and the inertial resistance; 2, establishing a multi-branch-pipe CFD model, querying a mapping table for the opening degrees of all valves, randomly generating an inertia resistance coefficient and a viscous resistance coefficient, constructing a dual-objective optimization function by taking the virtual opening degree of each branch pipe as a design variable, then calling CFD to solve all the branch pipes, and returning the dual-objective optimization function, so as to obtain a multi-branch-pipe CFD model; the flow unevenness of each branch pipe and the most unfavorable loop resistance in the system are output, multi-objective optimization and global optimization are carried out by adopting an NSGA-II algorithm, and a Pareto leading solution set of an optimal valve opening combination is output for a user; and 3, the flow of the multiple branch pipes is leveled through the optimal valve opening degree combination. The method has the advantages of modeling simplification, high calculation efficiency, short optimization period, strong adaptability and good stability.

A flexible thin-wall crossed roller bearing multi-target parameter optimization method relates to the technical field of bearing design, and comprises the following steps: 1, establishing a parameterized system model containing a shaft system, a thin-wall crossed roller bearing and a box body assembly relation, the bearing model being determined by geometric and assembly parameters; 2, carrying out finite element modeling on the inner / outer ring of the bearing, and carrying out polycondensation on the degree of freedom of an interface to obtain an equivalent stiffness matrix; 3, inputting working conditions and material parameters, and setting a design variable feasible region and constraints; 4, coupling a mapping matrix to solve load distribution, calculating the maximum contact stress and the life damage rate, and constructing a dual-objective optimization model; 5, performing test design to obtain response data, and performing multi-target algorithm iterative optimization to obtain a Pareto optimal solution set; and 6, screening the solution set, and outputting a structure parameter combination meeting the requirement. The method can solve the problems that in the prior art, it is difficult to consider calculation efficiency and ferrule flexibility effect precision, and it is difficult to conduct rapid iterative optimization on roller load distribution and life / contact stress indexes.

The invention discloses an AI-driven thermal power plant combustion stability-economic benefit double-target intelligent coal blending method, and relates to the technical field of thermal power plant coal blending optimizing.The method comprises the steps that a coal-wrapped type combustion fingerprint feature database is constructed; inputting the combustion fingerprint feature data of the candidate coal blending scheme into a boiler combustion risk prediction model based on a weighted support vector machine to obtain a corresponding combustion risk probability value; the coal blending proportion is used as an optimization variable, a dual-objective optimization function is constructed, a penalty function is adopted to perform relaxation processing on a preset constraint condition, and the dual-objective optimization function comprises an economical efficiency function taking fuel purchase cost minimization as a target and a safety function taking combustion risk probability value minimization as a target; carrying out Pareto optimization on the economy function and the safety function based on a multi-objective evolutionary algorithm, and outputting a Pareto optimal coal blending scheme set; and executing the coal blending scheme. Therefore, collaborative optimization of boiler combustion safety and fuel purchase economy is realized.

The invention discloses a virtual power plant dual-target aggregation method and system, and relates to the technical field of electric power automation. The method comprises the steps that virtual power plant operation data are collected and preprocessed; and constructing an economic objective function and a low-carbon objective function based on the preprocessed data. And performing dual-objective optimization solution on the constructed economic objective function and the low-carbon objective function by adopting a red fox optimization algorithm, and generating a Pareto optimal solution set under the condition of meeting the operation constraint of each controllable device. And determining the constraint boundary of the motion space of the deep reinforcement learning model based on the Pareto optimal solution set. And constructing a state space based on the preprocessed data, inputting the deep reinforcement learning model, and outputting a normalized scheduling instruction. And converting the output normalized scheduling instruction into a physical power control instruction of each controllable device, and monitoring the total power of the power distribution network in real time. And if the total power of the power distribution network exceeds a preset total power threshold value, starting an overload optimization process dynamic adjustment scheduling strategy.

This invention relates to the field of industrial process management and optimization scheduling technology, and particularly to a method for tracking and optimizing carbon emission flows in a multi-product co-production process in the fluorochemical industry. First, a directed graph model of the product chain with process units as vertices is constructed. A multi-dimensional carbon emission flow calculation model is established, including direct carbon flows from fuel combustion, indirect carbon flows from purchased electricity and heat, direct carbon flows from the process itself, and carbon flows from the emission of fluorinated greenhouse gases. A dynamic evolution algorithm for node carbon potential and an iterative algorithm for reverse tracing of branch carbon flows are employed to achieve accurate tracking of spatiotemporally coupled carbon flows throughout the entire process. Using the time-varying carbon intensity coefficient as the core parameter, a dual-objective optimization scheduling model is established to minimize both total system carbon emissions and overall operating costs. An adaptive penalty mechanism with carbon constraints is introduced to solve for the optimal scheduling scheme, constructing a rolling optimization closed-loop control link for the production process. This invention effectively solves the problems of calculating carbon flows from the emission of fluorinated gases and allocating carbon flows among multiple products in the fluorochemical industry, achieving a synergistic improvement in carbon emission reduction and economic benefits.

The invention discloses an ecological hydrological benign relationship maintenance threshold defining method and system, and relates to the technical field of ecological hydrology, and the method comprises the steps: obtaining multi-source data of a target drainage basin, and selecting a core decision variable; constructing a dual-objective optimization model comprising a hydrological objective function and an ecological objective function based on the core decision variable; constructing a plurality of constraint conditions based on the multi-source data; based on all constraint conditions, a multi-objective evolutionary algorithm is adopted to solve the dual-objective optimization model, and a Pareto optimal solution set is obtained; and determining a weight based on an expert scoring method, and selecting an optimal threshold value from the Pareto optimal solution set based on an ideal point method and a comprehensive satisfaction index. And collaborative optimization of ecological protection and economic development is realized, so that the stability and practicability of the threshold value under the climate change background are ensured.

The invention discloses a boiler combustion intelligent optimization control system based on machine learning, and relates to the field of boiler combustion control, and the system comprises a collection module which is used for collecting fuel characteristics, hearth temperature, flue gas components, air supply amount and auxiliary machine operation parameters in a boiler combustion process, and recording the collected parameters; the extraction module is used for synchronously receiving the original acquisition parameters, preprocessing the parameters to extract core characteristic parameters representing the combustion state and standardizing the core characteristic parameters; according to the method, the future combustion state is predicted based on the time sequence coupling model, the combustion working condition can be comprehensively evaluated by combining the comprehensive evaluation index calculated by the multi-dimensional coefficient, the model continuously iteratively optimizes parameters through self-supervised learning, the combustion state is optimal and the adjustment action is milder through a dual-target optimization adjustment scheme, and the adjustment efficiency is improved. And the combustion heat efficiency and the operation stability of the boiler are greatly improved integrally.

The application discloses a crack-resistant reinforcement configuration optimization method for high concrete face slab dam panels, S1: acquiring first data; S2: generating second data based on a geometric model of dam panels; S3: generating an indication signal based on the first data and the second data through a Pareto frontier solver; S4: generating a reinforcement network optimization instruction based on a selected working point on a Pareto frontier curve; and S5: adjusting an initial reinforcement topology network in response to the reinforcement network optimization instruction, and outputting a final reinforcement configuration scheme; the application can realize significant synergistic gain of design safety and economic benefits by deep quantization of structural risk through probability inference, construction of a double-target optimization framework of efficiency and robustness, generation of a Pareto frontier performance boundary, and output of a reinforcement scheme with higher safety under the same cost or lower cost under the same safety standard through an interactive decision tool.

The invention discloses a curved surface grouping selection assembly method based on an improved genetic algorithm, and belongs to the field of precision assembly, and the method comprises the steps: determining a to-be-assembled reference part and a to-be-assembled matching part, and obtaining the three-dimensional point cloud data of an assembly interface through sampling; establishing a dual-objective optimization model of a weighted square difference index by taking minimization of an average gap and a maximum gap as objectives; an improved genetic algorithm is adopted to solve the dual-objective optimization model, and when the number of iterations reaches a set threshold value, an optimal group mapping relation pairing index matrix is output; matching the index matrix according to the optimal group mapping relation, generating an optimal matching combination of the reference part and the matching part, and completing curved surface grouping selection assembly. According to the method, the limitation that three-dimensional shape errors are difficult to consider in an existing two-dimensional size matching method is solved, curved surface grouping selection assembling is achieved by constructing a double-target optimization model fusing three-dimensional shape parameters and combining a multi-target optimization strategy of a genetic algorithm, and assembling precision and consistency are improved.

The invention provides a multi-unmanned aerial vehicle wireless energy transmission and wireless data collection collaborative optimization method based on adaptive hierarchical deep reinforcement learning, comprising: constructing a dynamic system model composed of a plurality of unmanned aerial vehicles, a plurality of information devices and a plurality of energy devices, the information devices uploading data to the unmanned aerial vehicles, and the energy devices uploading data to the unmanned aerial vehicles; the energy equipment receives wireless energy from the unmanned aerial vehicle; minimizing the total information age of all the information devices and minimizing the total energy hunger degree of all the energy devices are set as common optimization objectives, and the double-objective optimization problems are fused into a single-objective optimization function according to weights; and constructing and operating a three-layer multi-agent deep reinforcement learning framework, solving the single-target optimization function, and outputting an optimal cooperative control strategy. According to the method, real-time balance, autonomous decision making and efficient cooperation of the multiple unmanned aerial vehicles for heterogeneous Internet of Things equipment requirements are realized.

The invention provides a flow shop scheduling dual-objective optimization method based on a bacterial foraging optimization algorithm, and relates to the technical field of production and manufacturing management. The method specifically comprises the steps of obtaining original data of production scheduling in an industrial assembly line, and constructing a coupling coding scheme based on a task sequence and a delay period matrix under the background of time-of-use electricity price; an initial population is generated by adopting a hybrid heuristic initialization strategy, wherein each individual represents a complete scheduling scheme through a coupling coding strategy based on a task sequence and a delay matrix. According to the method, global optimization is carried out on a solution space based on an improved multi-target bacterial foraging optimization algorithm, and finally, an optimal individual is output and serves as an optimal scheduling scheme of a flow shop. According to the method, the problem of double-target optimization faced by original equipment manufacturers in flow shop scheduling under a time-of-use electricity price strategy is effectively solved, and meanwhile, the production efficiency is improved and the electric charge expenditure is reduced.

The invention discloses a peak regulation type energy storage equivalent capacity conversion method and system considering economic cost constraints. The method comprises the following steps: acquiring energy storage and actual operation data of a power grid, and extracting operation parameters of various indexes; determining a reference energy storage system, recording the reference energy storage system as I type, determining various technical parameters of the reference energy storage system, and calculating the whole life cycle total discharge capacity and the whole life cycle cost of the reference energy storage system; solving the to-be-assessed type of stored energy recorded as type II, the required initial configuration capacity and the full life cycle cost thereof by taking the equal total discharge capacity as a target, and recording the to-be-assessed type of stored energy as type II; and S3, judging whether a constraint condition is met or not, and if not, returning to the step S3. According to the method, a double-objective optimization model with technical performance and full-life-cycle economy collaboration is constructed, a multi-type energy storage equivalent peak regulation capacity configuration scheme meeting the technical performance requirement and the economy requirement at the same time is output, and a quantitative and reliable decision basis is provided for energy storage economic operation in a peak regulation scene.

This invention discloses a method, system, equipment, medium, and product for quantitatively assessing the demand-side adjustability of distribution networks, relating to the field of power system analysis and optimization technology. First, a dual-objective optimization model is constructed with the optimization objectives of maximizing renewable energy absorption and minimizing system operating costs. Then, multiple sets of training samples are generated based on this dual-objective optimization model and preprocessed to construct a labeled dataset. Subsequently, the labeled dataset is input into a pre-set deep neural network for training, resulting in a rapid prediction model for flexible resource adjustability. Finally, the distribution network data to be predicted on the demand side of the target distribution network is input into this rapid prediction model, outputting the quantitative assessment result of resource adjustability. The model-driven approach uses the dual-objective optimization model as its core, while the data-driven approach uses a deep neural network as its carrier, enabling rapid response in the assessment process. The combination of these two approaches effectively solves the technical problem of existing flexible resource adjustability assessments struggling to balance accuracy and real-time response efficiency.

The invention discloses a composite cemented filling material design method, and relates to the technical field of mine filling, and the method comprises the following steps: determining optimization components and optimization domains thereof, randomly generating a plurality of design combinations, preparing a filling material, and filling a to-be-filled sample to form a composite filling body test piece; maintaining the sample test body in a simulated environment, and dynamically adjusting a sampling interval and determining a plurality of sampling moments by taking a hydration heat reaction degree as an index; carrying out a coupling monitoring test by adopting a detection method combining a pulse speed and a low-frequency dielectric spectrum, extracting an interface coupling degree parameter, calculating a cementation coupling degree, and determining a termination moment based on a change rate of the cementation coupling degree; according to the method, the structural strength at the moment is obtained, the preset strength is taken as a constraint, the cementing coupling degree maximization and the experiment time minimization are taken as optimization objectives, a dual-objective optimization function is set, and the optimal design combination is determined through an optimization algorithm, so that the engineering risk can be remarkably reduced, and the service life of the structure is prolonged.

The invention discloses a distributed flow shop scheduling method based on a large model aided multi-objective optimization algorithm, and particularly aims to solve the problems of insufficient sequence correlation setting time, insufficient wait-free constraint processing and low multi-objective optimization efficiency in existing distributed heterogeneous factory scheduling. And proposing a non-dominated sorting genetic algorithm based on large language model assistance. The method comprises the following steps: constructing a dual-objective optimization model, adopting a one-dimensional integer array coding solution structure and separating a factory operation sequence through '-1'; in combination with a greedy algorithm idea, selecting a greedy initialization algorithm based on maximum completion time or a greedy initialization algorithm based on sequence-dependent setting time to initialize a population; designing a large model cue word including problem definition, solution example, evolution instruction and legality verification, and executing parent selection, crossover and mutation operation by a large language model; and updating the population through non-dominated sorting, and finally outputting a Pareto frontier solution.

The application discloses a beneficiation whole-process optimization control system and method based on a large model, relates to the technical field of beneficiation process optimization, and comprises the following steps: constructing a global prediction model based on unified feature embedding, and distilling the global prediction model into an edge lightweight model with a conditional branch selection mechanism; performing hot start on the edge lightweight model in combination with historical experience obtained through knowledge graph retrieval, constructing a double-target optimization problem, searching based on loss constraint through improved difference, and generating a candidate control strategy set; calibrating and confidence screening the candidate control strategy set under multiple scenario conditions, and generating a safe control instruction; and monitoring data drift in the safe control instruction execution process. The application guarantees prediction accuracy, and also considers real-time performance and availability under a low-computing-power environment.

The invention discloses a shared energy storage industrial park dual-objective optimization method and system based on SOC layering and preemptive scheduling, a terminal and a storage medium, and the method comprises the steps: obtaining shared energy storage parameters in different operation scenes, a key load guarantee supply demand of a park energy system and an energy use request of each user, and optimizing a state-of-charge dynamic boundary value; calculating the penalty cost of preempting the redistribution power based on the optimized charge state dynamic boundary value; establishing an economical efficiency optimization objective function and an economical efficiency optimization constraint condition of the park, and solving park scheduling schemes in different operation scenes; performing rolling optimization on the optimized dynamic boundary value of the state of charge based on park scheduling schemes in different operation scenes; and on the basis of the dynamic boundary value of the state of charge after rolling optimization, penalty cost for preempting the redistribution power and an economical efficiency optimization objective function are updated, and a park optimization scheduling scheme under different operation scenes is solved, so that the shared capacity is multiplexed in real time among multiple users and is guaranteed according to priorities, and the utilization rate and response timeliness of shared energy storage are improved.

The invention belongs to the technical field of energy storage, and provides an energy storage system capacity optimization configuration method and device, a medium, a product and equipment. The energy storage system capacity optimization configuration method comprises the following steps: constructing a double-target optimization model taking the life cycle cost and the annual average light abandoning rate of an energy storage system as targets; under a set constraint condition, solving the dual-objective optimization model by using a golden leopard optimization algorithm to obtain the optimal capacity configuration of the energy storage system with the minimum life cycle cost and the minimum annual average light abandoning rate of the energy storage system; introducing crossover operation into the golden leopard optimization algorithm, and in the crossover operation, performing crossover operation on individuals of different populations on the same dimension so as to generate new individuals; and meanwhile, the global search and the local search are balanced according to the absolute value of the escape energy. According to the method, high-proportion new energy can be consumed as a target, and the capacity of the new energy is optimally proportioned.

The invention provides an elastic bus route planning method and device based on a multi-objective intelligent optimization algorithm, and a medium, and relates to the field of intelligent transportation. The method comprises the following steps: acquiring passenger demands and regional station data; generating a bus route initial population based on a plurality of different oriented initial population generation rules; establishing a dual-objective optimization model by taking the passenger convenience as a first objective and the vehicle operation efficiency as a second objective according to the regional station data; in combination with constraint conditions, optimizing the initial population by using a decomposition-based multi-objective evolutionary algorithm, determining a Pareto optimal solution, and obtaining a plurality of pre-optimized paths; and according to an operation strategy, screening a plurality of satisfactory solutions in the pre-optimized path, further performing iterative optimization on the screened satisfactory solutions by using a variable neighborhood search algorithm, and executing local search operation in the vehicle path and / or between the paths to obtain an optimized elastic bus path. The method can scientifically plan the vehicle running path according to the travel demand of the passenger, and provides high-quality elastic bus service for the passenger.

The application discloses a building structure implicit carbon and cost double target collaborative optimization method and system, and the method comprises the following steps: establishing an engineering quantity quota database, an implicit carbon factor database and an information price database; reading structure calculation model information, constructing a component data dictionary and a material data dictionary; intelligently mapping component data dictionary information and engineering quantity quota database information, and calculating engineering quantity list data; based on the engineering quantity list data, the implicit carbon factor database and the information price database, calculating total implicit carbon and cost; based on the component data dictionary and the material data dictionary, establishing cross-section and material index optimization variables and constraint ranges; constructing a double target optimization function of minimizing total structure cost and implicit carbon emission and constraint conditions of strength constraints and construction constraints; based on the optimization variables and constraint ranges, the double target optimization function and the constraint conditions, using a genetic algorithm optimization iteration to output a Pareto optimal solution set and a visual chart.

This invention discloses a key node detection method based on Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). This method primarily aims to optimize and improve the accuracy of key node detection in complex networks, enabling it to find an optimal set of non-dominated solutions suitable for various application scenarios. First, for the key node detection problem in complex networks, this invention designs a non-uniform attack cost, using PWC in non-cascade scenarios and a cascade failure simulation framework based on node load and capacity constraints in cascade scenarios, designing attack effects and constructing a dual-objective optimization function. Second, a bisection method is used to obtain the minimum number of nodes required for total failure, compressing the search space and improving the evolution speed. Prior knowledge heuristics and random generation of the initial population are used to improve population quality. Next, based on the GA algorithm framework, the crossover, mutation, local search, and selection strategies are improved to adapt to the current discretized space, achieving comprehensive exploration of the candidate key node set within the search space. Finally, based on the PSO framework, the update strategies for particle velocity and position are improved to adapt to the discretized space, achieving directed guided local optimization and improving solution quality. Finally, based on the Knee Point technique and non-dominated sorting technique, the Pareto front is selected to obtain the optimal set of non-dominated solutions, thereby obtaining the key node set with the minimum attack cost and the maximum attack effect.