240 results about "Pareto solution" patented technology

The invention discloses an ant colony optimization processing method of large-scale multi-target intelligent moving route selection; after data of NTSP target logistics delivery addresses, distances between every two addresses, and M price of cost for passing through each route are obtained, a route planning unit is solved by ant colony optimization technology so as to obtain a specific walking route for intelligent mobile-agent delivery, and the route is outputted to an executive mechanism for realization. When the method is used to solve the problem of large-scale multi-target intelligent moving route selection, the invention has good optimization performance, and has the advantages of parallelism, self-organization, strong robustness, and the like, and the obtained solutions are large in quantity, high in quality, and have strong approximation capability to the real Pareto solution set; the obtained solution set has uniform distribution; the calculation speed is high. The inventioncan be used in intelligent processing units of route planning systems in fields of logistics distribution, intelligent traffic, internet, robots, etc.

The invention discloses an inner and outer layer nesting ECMS (equivalent fuel consumption minimization strategy) multi-objective double-layer optimization method. The inner and outer layer nesting ECMS multi-objective double-layer optimization method includes steps of building multi-objective optimization models of plug-in hybrid electric vehicles; solving the multi-objective optimization modelsby the aid of inner and outer layer nesting multi-objective particle swarm algorithms to obtain multi-objective optimized Pareto solution set front edges; weighting equivalent fuel consumption per hundred kilometers and variation ranges of deviation of SOC (state of charge) final values and target values, building total evaluation functions related to the equivalent fuel consumption per hundred kilometers and SOC deviation and selecting the optimal charge and discharge equivalent factors and engine and motor power distribution modes corresponding to the optimal charge and discharge equivalentfactors. The inner and outer layer nesting ECMS multi-objective double-layer optimization method has the advantages that output power of engines and motors of the plug-in hybrid electric vehicles canbe reasonably distributed at CS (charge sustaining) stages, so that fuel consumption can be reduced as much as possible, battery SOC balance still can be effectively kept, and the fuel economy of theintegral vehicles can be improved.

In the field of multi-objective optimization using evolutionary algorithms conventionally different objectives are aggregated and combined into one objective function using a fixed weight when more than one objective needs to be optimized. With such a weighted aggregation, only one solution can be obtained in one run. Therefore, according to the present invention two methods to change the weights systematically and dynamically during the evolutionary optimization are proposed. One method is to assign uniformly distributed weight to each individual in the population of the evolutionary algorithm. The other method is to change the weight periodically when the evolution proceeds. In this way a full set of Pareto solutions can be obtained in one single run.

The invention discloses a reconfigurable assembly line sequencing method based on an improved genetic algorithm. The method comprises the following steps of: determining a population size according to a minimum production cycle of a reconfigurable assembly production line, and executing genetic encoding according to a standard of taking a chromosome as a full array of all tasks; calculating the idleness of the minimum reconfigurable assembly line, the quantity of unfinished work, the uniform parts use rate and the minimum production adjustment cost of the individual; executing a grading operation, executing a Pareto solution set optimization filtering operation, calculating the fitness of each grade, executing genetic operations according to the fitness, executing an elite reservation strategy, and obtaining a Pareto optimal solution set and a corresponding objective function value by judging whether convergence is realized or the pre-set maximum number of iteration is achieved. In the method, three major factors influencing the optimized sequencing of the reconfigurable assembly line are comprehensively considered, a plurality of technologies are used in the genetic operation, population diversity is ensured, algorithm prematurity is avoided, and global optimal search ability of the algorithm is enhanced.

The invention relates to a multi-target random fuzzy dynamic optimal energy flow modeling and solving method for a multi-energy coupling transmission and distribution network and belongs to the field of day-ahead scheduling plan research of electric power systems in an energy interconnection environment. The method comprises the following steps: basic data in a system scheduling period are obtained,; random fuzzy space-time sequence models for large-scale wind power, distributed power source and multi-energy loads are obtained via historical data mining; power and voltages of a power transmission network and all active distribution networks at joint nodes are used as share variables; multi-target SoS dynamic optimal energy flow models characterized by high economic performance, low carbon emission, renewable energy absorption, loss reduction and the like are built within static state security constraints; multi-energy source charge forecast can be realized through random fuzzy simulation; a Pareto solution set, an optimal compromise solution and an energy flow result can be obtained via adoption of an improved SoS layered optimizetion algorithm based on approximate dynamic programming and NSGA-11. The method can adapt to a development trend of energy interconnection, and comprehensive coordination optimization of day-ahead scheduling of transmission and distribution parties can be realized on the premise that requirements for static state safety and stabilization of systems can be satisfied.

The invention belongs to the crossing field of a computer application technology and production manufacturing. A natural computing technology is used to optimize a multi-target flexible job shop scheduling problem. A problem that a cooperative hybrid artificial fish swarm algorithm is used to solve multi-target flexible job shop scheduling is provided. The method is characterized in that a foraging behavior with a distribution estimation attribute and an artificial fish attraction behavior are designed to improve an artificial fish swarm model; a cooperation idea is introduced into the model; through multiple population cooperation of the fish swarm, global searching is performed and is cooperated with a simulation annealing algorithm so as to enhance an algorithm local searching capability; aiming at a multi-target problem, an improved epsilon-Pareto dominant strategy is designed to evaluate an individual applicable degree value. The method in the invention has the following advantages that problems of slow later-period convergence, a poor local optimizing ability and the like, which exist in the artificial fish swarm algorithm during a searching process, can be overcome; through cooperative optimization, a pareto solution set with good quality and dispersibility is obtained.

The invention discloses a power system multi-target differentiation planning method based on an improved harmony search algorithm. The method is based on the differentiation planning basic principle and requirement, economical efficiency and reliability are quantized according to additional cost and lost benefits in the whole life cycle, and an optimized multi-target differentiation planning model is built; an optimizing criterion is formulated according to the Pareto dominance relation, and an optimal differentiation planning scheme is selected from a Pareto solution set with the maximum benefit-cost ratio as the goal. The newly-developed harmony search algorithm is applied to solution of the model, chaotic mapping, dynamic parameter setting, improved pitch adjustment strategies and a harmony optimizing information sharing mechanism are introduced to solve the 0-1 planning problem, and search performance is improved. According to the method, economical efficiency and reliability can be comprehensively given into consideration, multi-target differentiation planning of a power grid is achieved, and the method is of guidance significance in building a core backbone net rack and building a strong power grid.

The invention discloses an image dividing method based on immune multi-object clustering, relating to the technical field of image processing, and mainly solving the problems that the conventional method has single evaluation index, and easily has bad region consistency and disorder boundary. The method comprises the following realization steps of: (1) extracting the characteristic of an image to be divided, and primarily dividing the image by controlling the watershed of a mark; (2) setting a running parameter and initializing the population of an antibody; (3) combining the locally-searched immune multi-objective optimizing method with the population of the antibody to obtain an approximate Pareto solution set; (4) selecting an optimal solution in the approximate Pareto solution set obtained in the step (3) according to a PBM index; and (5) marking an image pixel point according to a primary dividing result obtained in the step (1) and a clustering result obtained in the step (4) to obtain a final classifying result. The image dividing method has the advantages of good dividing result region consistency, being capable of keeping complete information, and having fast computation speed, and can be used for identifying an image object.

The invention relates to a parallel multi-objective optimized scheduling method for a cascaded hydropower station group. A multi-population evolution strategy is used to ensure the relative independence of small-scale subpopulations, elite individuals of a Pareto solution set are coupled to an inter-population annular migration mechanism in the evolution process, information is transmitted and back fed mutually among the subpopulations, and the individual diversity and guidance quality of the solution set are ensured; and a multi-core parallel calculation technology is used to realize synchronized evolution of the subpopulations, waste of calculation resources under in the serial calculation mode is avoided, and calculation is accelerated. According to the invention, the calculable scale of optimized scheduling of the cascaded hydropower station group is further enlarged, a reasonable and feasible scheduling scheme set is provided for a decision maker, the calculation efficiency is ensured, and the method of the invention is a feasible method to realize multi-objective optimized scheduling for the cascaded hydropower station group.

The invention discloses a multi-parent genetic algorithm air source heat pump multi-objective optimization control method based on a radial basis function neural network. The method comprises the following steps that 1, input and output variables are input into a system according to user requirements; 2, creating, training and testing a radial basis function neural network; 3, performing multi-objective optimization on an air source heat pump by using a multi-parent genetic algorithm based on the trained radial basis function neural network; and 4, obtaining the parameter value of the input variable of the optimal solution according to the Pareto solution through the above steps, and transmitting the obtained input variable value to the system to adjust the control quantity of the heat pump. The multi-objective optimization of the COP heating capacity Qh or the carbon dioxide release amount m and the heating capacity Qh of the system can be rapidly realized while the precision is high.

The invention provides a multipurpose optimization method for distribution of facilities in a plant, which comprises the steps of: 1. dividing distribution areas of facilities in the plant into operation units and obtaining related original parameters; 2. obtaining logistic relation parameters and non-logistic relation parameters of the operation units according to the original parameters obtained from the step 1; 3. building a multipurpose facility distribution optimization model; and 4. solving the multipurpose optimization model by using an NSGA (non-dominated sorting genetic algorithm) II, thus obtaining a Pareto solution set as a facility distribution optimization scheme set. With the method, the technical problem that multipurpose optimization distribution is carried out on the facilities in a building of the plant is solved, and the use of the method has the advantages of high distribution efficiency, high operation vision degree, good optimization effect, low use cost and strong university.

The invention discloses an air source heat pump multi-objective optimization design method of a non-dominated sorting genetic algorithm assisted by a SVR neural network. The method comprises the following steps: step 1, carrying out parameter selection and data processing according to design requirements; step 2, creating, training and testing a neural network; step 3, performing multi-objective optimization on the air source heat pump by using a non-dominated sorting genetic algorithm based on the trained SVR neural network; and step 4, obtaining the parameter value of the input variable of the optimal solution according to the Pareto solution through the above steps, thereby obtaining the design parameter value of each component, and feeding back the design parameter value to a designer.The SVR neural network assisted non-dominated sorting genetic algorithm-based air source heat pump multi-objective optimization design method provided by the invention is relatively high in precisionand can quickly realize COP and cost multi-objective optimization of a system.

The invention discloses a decomposition-based train operation multi-objective differential evolution algorithm. The algorithm comprises the steps of 1, establishing a train elementary substance pointdynamical model; 2, establishing a train multi-objective optimization model according to the train multi-objective operation requirement; Step 3, decomposing the train operation multi-objective optimization problem into N single-objective optimization sub-problems by adopting a Chebyshev method; 4, in order to ensure the uniformity of the obtained Pareto solution, generating a weight vector by adopting a formula uniform design method; And 5, selecting an evolution strategy to form a differential evolution strategy pool, and improving the diversity and convergence of an evolution process by adopting a self-adaptive differential evolution strategy based on reputation. The train operation multi-objective optimization problem is converted into the single-objective problem, on the basis that the uniformly distributed weight vectors are obtained, multiple control strategies are provided for the train on the premise that safety is guaranteed through the self-adaptive differential evolution strategy, and safe, quasi-point, accurate parking and low-energy-consumption operation of the train are achieved.

The invention relates to a method for solving Pareto solution sets of a wind-storage-thermal joint operation system in multiple time periods after access of large-scale wind power. The method adopts a means combining a traditional genetic algorithm and an NSGA-II algorithm in solving, and respectively solves the problem that the optimization result obtained by adoption of the traditional genetic algorithm is single and the problem that only the Pareto solution set of a single time period can be obtained through optimization by adoption of the NSGA-II algorithm. An optimization result of a wind-storage-thermal joint operation system mathematical model solved by the genetic algorithm is used as the initial value of solution by the NSGA-II algorithm so as to obtain the Pareto solution set of each moment of time through optimization. Compared with the traditional genetic algorithm and the NSGA-II algorithm, the method of the invention can effectively overcome the defects of the two algorithms in the solution process, more comprehensively search out optimization solution sets as many as possible, and provide comprehensive, clear and effective support for decision makers.

The invention provides a ship route navigational speed multi-task comprehensive optimization method. The ship route navigational speed multi-task comprehensive optimization method includes the steps:obtaining basic information including meteorological and sea conditions, geographic conditions, a recommended initial route and ship basic information during navigation; preprocessing the data; dividing each route segment of the recommended route at equal longitude; obtaining a plurality of equal division points as to-be-optimized steering points; establishing a multi-objective optimization modelwith a wave height penalty function by taking oil consumption and navigation time as objectives; and inputting related data into the model, solving the model by using a multi-objective evolutionary algorithm, obtaining a Pareto solution set of the optimal air route by adjusting the latitude position of each steering point to be optimized and optimizing the water navigational speed of each segment,and finally obtaining the optimal air route according to the requirements of a client. Compared with a traditional route optimization method, the route optimized through the ship route navigational speed multi-task comprehensive optimization method can effectively avoid meteorological severe areas, navigation risks are reduced while navigation oil consumption and navigation time are optimized, and navigation cost is reduced.

According to the multi-skill personnel scheduling method in the research and development project combination, which can solve the technical problems that a multi-skill personnel scheduling decision inthe research and development project combination is high in complexity and high in difficulty. The method mainly comprises the steps of generating an initial scheme of multi-skill personnel scheduling in a project combination according to a traditional serial progress production mechanism; Establishing a project combination multi-skill personnel multi-target scheduling model by considering the learning effect of personnel; When a learning effect and a plurality of scheduling targets are considered, applying a multi-target improved ant colony algorithm to iteratively solve the multi-skill multi-target scheduling model to obtain a Pareto solution set; And for the Pareto solution set, solving an optimal solution of the model by adopting an approximate ideal solution sorting method, and taking the optimal solution as a personnel configuration scheme for the project combination multi-skill personnel multi-target scheduling problem. On the basis of considering the cost and the constructionperiod of a traditional project personnel scheduling model, the personnel learning effect is further considered, the personnel skill training decision-making target is increased, rapid solving of themodel is achieved, and the decision-making requirements of enterprise managers are met.

The invention provides a self-adaptive multi-object evolution method adopting constraint cloud workflow scheduling. The overall detection and local mining capability of the multi-object evolution method can be improved. The multi-object evolution method comprises the steps that S1, the evolution states of populations in the evolution process are detected according to the number of Pareto solutions and Pareto entropies, and corresponding individual evaluation strategy processing constraint conditions are self-adaptively utilized to sort individuals in the populations according to the detected evolution states of the populations in the evolution process, wherein a constraint violation processing method is adopted to process the constraint conditions in individual evaluation strategies; S2, according to individual sorting results, individuals are selected from the populations to perform genetic manipulation, and sub-populations are obtained, wherein genetic manipulation parameters are self-adaptively adjusted according to the evolution states of the populations in the evolution process during genetic manipulation. The self-adaptive multi-object evolution method is suitable for solving the multi-object evolution problem having constraints and can be applied to the technical field of workflow scheduling in a cloud computing environment.

The invention relates to a power distribution network multi-target reactive-power optimization method based on a non-dominated neighbor-domain immune algorithm. Active power loss and reactive compensation input are regarded as targets to be optimized, and a power distribution network multi-target reactive-power optimization model considering constraints such as active balance, reactive balance, power distribution network power limits, node voltage limits, reactive compensation capacity limits, transformer tap limits, compensation node limits and line transmission power limits is established. The power distribution network multi-target reactive-power optimization model is solved by utilizing the non-dominated neighbor-domain immune algorithm. According to the algorithm, the non-inferiority and distributivity of a finally obtained Pareto solution are ensured by adopting proportional cloning, combination, variation and other operations and selection based on crowding distances. The specific configurations of a reactive compensation device having minimum active power loss and lowest compensation input cost can be rapidly and reliably obtained, and the optimization method has a better engineering application prospect.

A method for multi-objective reactive power optimization device, computer device and a storage medium, by determining an initial individual optimal particle and an initial global optimal particle foreach of the particles, A normal cloud generator is used to generate adaptive inertial weighting factors to balance the particle swarm exploration and development capability, Selecting Global Optimal Particles, so as to make full use of the directivity guidance information carried by the excellent infeasible solution and feasible solution so as to make the algorithm converge to the Pareto optimal front end quickly, In order to guarantee the distribution uniformity and diversity of Pareto solution set, a set of non-inferior solutions with better Pareto front and uniform distribution are obtainedby using the cyclic abandonment strategy, which provides decision makers with the opportunity to diversify their choices.

The invention discloses a multi-target multi-main-body distributed game optimization method for distributed energy sources, and belongs to the technical field of power system automation. Aiming at the multi-target, multi-constraint, nonlinear and multi-main-body game characteristics of a plurality of types of distributed energy sources, the invention provides the multi-target multi-main-body distributed game optimization method. According to the economical, environment-friendly and high-efficiency target demands of the joint optimization of the plurality of types of distributed energy sources, the method builds a multi-energy-system multi-target joint optimization model through combining the output and climbing rate constraints of the plurality of types of distributed energy sources. Based on the distributed coordination optimization theory, the method enables the whole model to be divided into a plurality of subsystem multi-target joint optimization models, employs an improved multi-target optimization algorithm for solving, and obtains a Pareto solution set of each subsystem, thereby finally forming an optimal Pareto solution set for the whole system and providing a reliable decision support for a decision maker.

The invention discloses an air source heat pump multi-objective optimization design method integrating a BP neural network and a multi-parent genetic algorithm. The air source heat pump multi-objective optimization design method comprises the following steps that 1, performing parameter selection and data processing according to design requirements; Step 2, creating, training and testing a neuralnetwork; Step 3, performing multi-objective optimization on the air source heat pump by using a multi-parent genetic algorithm based on the trained neural network; And step 4, obtaining the parametervalue of the input variable of the optimal solution according to the Pareto solution through the above steps, thereby obtaining the design parameter value of each component, and feeding back the design parameter value to a designer. The BP neural network assisted multi-parent genetic algorithm air source heat pump multi-target optimization design method is high in precision and capable of rapidlyachieving multi-target optimization of COP and cost of a system.

The invention discloses a PSO improved algorithm-based microgrid multi-target operation optimization method. A multi-target operation optimization model based on the minimum microgrid operation cost,the minimum pollution emission treatment expenditure and the optimal renewable energy output power fluctuation suppression effect is established; an improved hybrid POS algorithm is adopted for solving the model; according to the algorithm, a particle swarm is divided into multiple sub groups averagely corresponding to multiple optimization targets; next, parent particles with the appointed quantity are selected from each sub group for hybridization; finally, a Pareto solution set is obtained, and the solution set is subjected to standardization sorting to solve a group of accurate optimal solutions; in addition; a judgment threshold value on the environment change is added to the algorithm, so that timelines of the algorithm is further improved, and quick and accurate optimization is realized; and therefore, the microgrid system can make corresponding adjustment according to the environment changes in real time, so as to realize stable, economic and environment friendly operation of the microgrid finally.