96 results about "Multi population" patented technology

The invention discloses a heterogeneous multi-UAV (Unmanned Aerial Vehicle) cooperative scouting and striking task planning method, and belongs to the technical field of UAV task planning. The method comprises the steps of firstly establishing a task planning model which takes shortest UAV task performing flight and maximum target value as an objective function; and then solving the task planning model by using a heterogeneous multi-population ant colony algorithm so as to acquire an optimal task allocation plan which conforms a task completion time constraint, a UAV task type and capacity constraint and a condition that each task is only performed for one time. The method disclosed by the invention effectively solves a multi-UAV multi-task allocation problem under complex constraint conditions, can satisfy a task sequence proposed in order to improving the combat efficiency while meeting a sequential relation of different types of tasks, and has good flexibility in use.

Embodiments relate to systems and methods for the generation of multi-population statistical measures on a set of data sources. A set of applications such as online analytical processing (OLAP) applications can access the combined data of a set of data sources via an aggregation engine. The set of data sources can be configured with diverse dimensions and associated data. In general the data sources may not be expected to reflect a strictly consistent structure. In embodiments, the aggregation engine can combine or aggregate the set of data sources using common dimensions or data points, and build an index into a transform table reflecting a combined mapping. An OLAP or other application can then perform statistical computations, searches, sorts, and/or other operations on the combined mapping, even when the original data sources do not contain identical dimensions or other formats.

The present invention provides a multi-population genetic particle swarm optimization method containing the micro-grid capacity configuration of electric automobiles. The method realizes the energy storage function of an electric automobile on the premise that the electricity demand of the electric automobile can be met. According to the technical scheme of the invention, a multi-target model, with the annual cost, the annual loss of load probability and the peak-valley difference of a load curve as targets, is proposed. Based on the multi-population genetic particle swarm algorithm, a target function is solved out. In this way, the optimal capacity of each unit in a micro-grid system can be obtained precisely. On the premise that the system reliability is ensured and the load fluctuation is stabilized and inhibited, a higher economic benefit is obtained. Through optimizing the micro-grid system containing the electric automobile, the mobile energy-storage device of the electric automobile is utilized to realize the peak-load shifting purpose on the basis that the reliability and the economy of the system are guaranteed. Meanwhile, the peak-valley difference of the system curve is reduced. Not only is the stability of the power system improved, but also the economic benefit is higher. Therefore, the popularization and the utilization of a cleaning device of the electric automobile are facilitated.

The invention discloses a job-shop scheduling method based on multi-population evolution mechanism, which pertains to the computer field and mainly solves the problem that the present job-shop scheduling method of multi-population genetic algorithm is easy to be trapped into local optimization and poor local search ability. The method comprises the steps of (1) setting parameters and initializing the population; (2) evaluating the chromosomes and the initial memory bank of the population, optimizing the initial memory bank by means of the simulated annealing algorithm; (3) judging whether the termination condition is satisfied, if so, outputting the currently obtained optimal scheduling plan; otherwise, continuing step 4; (4) carrying out the crossover and mutation operation on the chromosomes in each sub-population; (5) communicating the sub-population with the memory bank; and (6) updating the memory bank and optimizing the memory bank by means of the simulated annealing algorithm, and then returning to step (3). The job-shop scheduling method of the invention can obtain the job-shop scheduling plan with high quality, shorten the production time and can be used for selecting the job-shop scheduling plan.

The invention discloses a prediction system and a method of circulating fluidized bed household garbage incineration boiler NOx discharge. An integrated modeling method of a BP neural network algorithm and a multi-swarm particle swarm optimization algorithm introducing a simplex operator is adopted to construct a rapid economic self-adaptive updating system and a method for carrying out real time prediction on the boiler flue gas NOx discharge, so that the tedious complex mechanism modeling work is avoided. A dynamic change characteristic of NOx discharge is represented by utilizing the non-linear dynamic characteristic, the generalization ability and the real time prediction ability of the BP neural network algorithm; an initial weight value and a threshold value of the BP neural network are optimized by utilizing the particle swarm optimization algorithm, and the possibility of reaching a local optimum of the BP neural network in a training process is reduced; the simplex operator and the multi-population migration mechanism are introduced, so that the diversity of the particle swarm optimization algorithm and the local searching ability are improved, and the possibility of reaching the local optimum of the particle swarm optimization algorithm is reduced.

The invention discloses a many-objective optimized scheduling method for combined operation of cascade hydropower stations, and aims at solving main problems in engineering application of standard quantum-behaved particle swarm optimization and problems in solving single-objective optimized scheduling. A multi-population evolution strategy is realized by external file set, advantageous individual selection and a chaotic mutation operator strategies, diversity of individuals is ensured, calculation of the method is accelerated, and an approximate Pareto optimal leading edge with sound distribution is obtained. An external file set is introduced to store elite individuals, dynamic update and maintenance of the file set are realized via non-inferior layered ordering and crowd distance, and distribution of the individual is kept uniform; and a chaotic mutation operator is used to carry out local disturbance on a non-control solution, and the neighborhood exploration capability of the individuals is enhanced. According to the invention, the particle swarm optimization is improved, and effectively applied to making the many-objective optimized scheduling scheme of combined operation of the cascade hydropower stations, and a feasible and high efficiency calculating method is provided for many-objective optimized scheduling of the cascade hydropower stations.

The invention discloses a multi-population chaos grey wolf algorithm-based multi-unmanned aerial vehicle cooperative path planning method. The method comprises the steps of firstly, building a multi-vehicle cooperative path planning model on the basis of a three-dimensional planning space; secondly, building a multi-unmanned aerial vehicle initial track set by combining a multi-population idea; and finally, searching optimal path of each unmanned aerial vehicle by a chaos grey wolf optimization algorithm. According to the method, cooperative path numbering of multiple unmanned aerial vehiclesis achieved by introducing the multi-population idea, the path searching range is expanded by chaos local searching, the problem that an original algorithm is easy to get in local optimization is effectively solved, the algorithm stability is improved, and a better path planning effect can be achieved. The employed multi-population grey wolf algorithm involves a few adjustment parameters and is rapid in convergence speed, multi-dimensional space searching and path planning under different conditions can be achieved, different constraint conditions and planning requirement are met, and optimalpath searching of an unmanned aerial vehicle group is achieved.

The invention relates to a multi-population simulated annealing hybrid genetic algorithm based on similarity expelling. The multi-population simulated annealing hybrid genetic algorithm includes the following steps: coding is carried out; initialization parameters are set; initial populations are created; fitness values are calculated; selecting operation is carried out; interlace operation is carried out; mutation operation is carried out; gene overturning operation is carried out; simulated annealing Metropolis rules are judged; migration operation based on similarity expelling is carried out; optimal storage is carried out; judgment is ended. The migration operation based on similarity expelling particularly includes the following steps: calculating the fitness values of individuals in a source population and a target population; selecting the individual with the largest fitness value from the source population to serve as the individual to be immigrated; conducting similarity calculation; conducting expelling replacement. The multi-population genetic algorithm with simulated annealing operation can improve the local search capability of the multi-population genetic algorithm, and the algorithm can search for approximate solutions even though optimal solutions to a larger extent. The individual similarity judgment is additionally carried out, attention is paid to differences between the individuals, the diversity of populations is maintained, premature convergence of the genetic algorithm is avoided, the solving quality of the algorithm is improved, and the algorithm is closer to the optimal solutions.

The invention discloses a multi-objective optimized overall workshop layout method based on a multi-population genetic algorithm. The method comprises the following steps: firstly, a multi-row linearworkshop layout mathematical model is established, and a functional area layout problem is converted into a combined optimization mathematical model problem; secondly, based on the optimization objective of minimum total material handling cost and maximum area utilization ratio of workshop layout, a precise workshop layout model is established by taking account of constraints including horizontaland vertical placement of main streets and functional areas, adaptive row spacing and the like of the manufacturing workshop, and multiple optimization objectives are converted into a single evaluation function with a weighting method; finally, solving is performed with the multi-population genetic algorithm, immigration operators are linked with populations in the solving process, information exchange and co-evolution of multi-population are achieved, different crossover and mutation probability parameters are set for different populations by crossover and mutation probability control formulae, and different search purposes are guaranteed. The total logistics handling cost of the workshop can be effectively reduced, and the utilization rate of the workshop area is increased.

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 provides an opportunity network evolution algorithm for promoting node cooperation. The algorithm comprises the following steps: firstly, establishing a socialized distributed credit model, including creditability quantification, a direct credit computation method, and an indirect credit evaluation method; secondly, establishing a corresponding incentive model based on creditability, including a creditability-based payoff function design, a creditability optimization based self-adaptive learning mechanism, and the like; thirdly, establishing a multi-population asymmetric evolution model, including multi-population classification, a strategy updating mechanism, and the like; and finally, designing and achieving the evolution algorithm based on the models, including a security cooperation evolution algorithm, and a firefly algorithm for solving the problem of searching the optimum among multiple objects. The evolution model, the credit incentive model and the credit evaluation method provided by the invention are applicable and reliable, and a dynamic security mechanism is operable.

The invention provides a multi-core adaptive & parallel simulated annealing genetic algorithm based on a cloud controller (APSAGABC), which mainly overcomes the defect that the traditional genetic algorithms are easily premature and get into local optimum. A multi-population mechanism is introduced, the advantage that threading building blocks support parallel computing of multi-core processors and support expanded nested threading paralleling is adopted and efficient operation of the method on the multi-core computers is realized. The method is characterized by firstly initializing parameters and individuals in each population; then each population independently selecting genetic individuals; obtaining the current optimum individual; later, each population independently crossing and varying, wherein a Metropolis mechanism undergoing adaptive control and simulated annealing based on the cloud controller is involved in the process; and finally judging whether the termination conditions are met, if not, continuously selecting the genetic individuals to carry out cross and variation. The algorithm is simple and flexible in design process, is easy to expand, conforms to the development trend of the computer towards multiprocessors and multi-core architectures and is convenient, fast, intelligent and strong in practicability.

The present invention discloses a sliding mode robust control method of a discrete time delay uncertain system. A multi-population chaotic simulated annealing particle swarm optimization algorithm is used to optimize a support vector regression machine (SVR) structure parameter, a combined kernel function support vector regression machine is used to give a system prediction model, a control law is solved through a novel sliding mode reaching law, and the reachability of a sliding mode can be guaranteed. The method is used for the robust control of uncertain discrete system with state time delay.

The invention provides an intelligent community demand response scheduling method and system, and the method comprises the steps: employing the minimum total operation cost of a system and the minimumexchange electric quantity of a power grid as a target function, building an optimal scheduling model comprising photovoltaic, energy storage and electric vehicles, and carrying out the solving through employing a multi-population collaborative purification genetic algorithm, and obtaining an optimization result. The method comprises the following steps: firstly, analyzing main components of theintelligent cell, and researching the operation characteristics of the intelligent cell; For the coordinated optimization scheduling problem of the daily load demand response of intelligent power cells containing multiple energy resources, the residential intelligent power mode including distributed energy such as electric vehicles and energy storage is taken as the research object. The minimum total operation cost of a system and the minimum exchange electric quantity of a power grid are taken as objective functions, the constraint conditions of schedulable loads, electric vehicles, distributed energy storage and the like are considered, a multi-population collaborative purification genetic algorithm is used for solving, an optimization result is obtained, peak clipping and valley fillingare achieved, and finally the effectiveness of an optimization model and a solving strategy is analyzed by means of examples.

The invention discloses a real-time prediction system of a circulating fluidized bed household garbage burning boiler furnace outlet flue gas oxygen content and a method thereof. An integration modeling method of a support vector machine algorithm and a multi-population genetic particle swarm optimization algorithm is used. A rapid, economic and adaptive updating system and a method are constructed so as to predict a boiler furnace outlet flue gas oxygen content in real time, and tedious and complex mechanism modeling work is avoided. A nonlinear dynamical characteristic of a SVM algorithm, a generalization capability and a real-time prediction capability are used to represent a dynamical change characteristic of a flue gas oxygen content. A particle swarm optimization algorithm is used to optimize a SVM algorithm punishment parameter C and a nuclear parameter g so as to increase a generalization capability of a model. A genetic operator and a multi-population migration mechanism are introduced so as to accelerate a convergence speed of a particle swarm algorithm, increase a diversity of a particle swarm optimization algorithm solution, reduce a possibility for particle swarm algorithm optimization calculation to get into local optimum and increase a global search capability and a local search capability of the algorithm.

An AGV scheduling optimization model is built with goods cabinets, sorting tables and AGVs as scheduling research objects and minimum time for AGVs to complete a specified task as an optimization objective. The AGV scheduling optimization model is solved using a two-stage multi-population parallel genetic algorithm and a simulated annealing algorithm, and the algorithms are solved by means of parallel computation to get an optimal scheduling scheme of AGVs for a specified task. Populations can be prevented from converging too early. The computational efficiency is improved. The algorithms can deal with a large-scale scheduling optimization problem.

The invention provides an oscillating float wave power generation method based on a multi-population genetic algorithm, which comprises the following steps: according to an oscillating float wave power generation system, a mathematical model of the power generation system is built; The output power of the system is analyzed by the mathematical model of the power generation system, and the wave energy capture rate is obtained. According to the wave energy capture rate, a multi-population genetic algorithm is obtained. The multi-population genetic algorithm is simulated to capture the optimal solution of wave energy of multi-population genetic algorithm. The invention provides an oscillating float wave power generation method based on a multi-population genetic algorithm, which combines a multi-population genetic optimization algorithm with an MPPT of wave power generation to capture wave energy, thereby completing an optimal wave energy capture rate.

The invention discloses a dynamic task perception-oriented cloud manufacturing collaborative task scheduling method, which comprises the following steps of firstly, constructing a collaborative task scheduling model according to the proportion of tasks completed by a supply chain; generating an initial population as a current population when a new task arrives according to an original manufacturing task and an optional manufacturing service, dividing the current population into a plurality of sub-populations for subsequent iteration, and representing each collaborative task scheduling scheme in the current population by using matrix coding; and finally, solving is performed through an improved multi-population biogeography optimization algorithm, and fusing a multi-population strategy, a local search strategy and a cooperation mechanism, so that the task scheduling problem solving performance of the algorithm is improved. According to the method, a better scheduling scheme can be obtained in the scheduling stage and the re-scheduling stage.

The invention discloses a multi-population genetic algorithm based comprehensive optimization design method for electrical and mechanical performance of a basin-type insulator. The method comprises the following steps of building an electric field and stress finite element analysis program of the basin-type insulator; partitioning parts to be optimized of the basin-type insulator; determining an optimization decision variable and a constraint condition of each part of the basin-type insulator, and building a structure optimization objective function; constructing a structural optimization design evaluation function of the basin-type insulator; and calling the finite element analysis program to figure out by applying a multi-population genetic algorithm. According to the method, the electrical performance and the mechanical performance of the basin-type insulator are comprehensively considered, a comprehensive optimization design scheme can be determined for the whole structure of the basin-type insulator with a complicated shape, the design period and the design cost of the basin-type insulator are effectively reduced, and the method has wide practicability and economy.

The invention discloses a multi-population genetic algorithm-based power distribution network fault section location algorithm. The algorithm includes the following steps that: 1) the fault current ofa power distribution network containing distributed power sources is coded with binary codes; 2) on the basis of satisfying the requirements of the multi-power source network, a distributed power source switching coefficient is introduced to represent power source switching, the application of the algorithm to a complex power distribution network is considered, a corresponding switching functionis established; 3) the construction of a fitness function is completed for the fault section location problem of the power distribution network according to a fault current code and the switching function of the power distribution network containing the distributed power sources; and 4) a multi-population genetic algorithm (MPGA) is implemented, so that population initialization, control parametersetting, immigration operator, manual selection operator and convergence condition determination are completed. With the algorithm of the invention adopted, the fault section of the power distribution network can be located accurately. The algorithm is suitable for a complex power distribution network containing distributed power sources, and has certain effectiveness and fault tolerance.

The invention belongs to the technical field of data analysis and discloses a kernel incremental transfinite learning machine and a differential multi-population gray wolf mixed optimization method. Aiming at the problem that the kernel incremental transfinite learning machine (KI-ELM) has the redundant nodes with low learning efficiency and poor accuracy; At first, that invention utilize the differential evolution algorithm and the multi-population grey wolf optimization algorithm to propose a hybrid intelligent optimization algorithm--the differential multi-population grey wolf optimizationalgorithm, optimizes the node parameter of the hidden layer, and determine the effective node quantity, so as to reduce the network complexity and improve the learning efficiency of the network; Secondly, the depth structure is introduced into the kernel incremental transfinite learning machine, and the input data is extracted layer by layer to realize the high-dimensional mapping classification of the data and improve the classification accuracy and generalization performance of the algorithm. The simulation experiment results show that the hybrid intelligent depth kernel incremental transfinite learning machine provided by the invention has good prediction accuracy and generalization ability, and the network structure is more compact.

The invention discloses an image segmenting method based on a dynamic multi-population integrated differential evolution algorithm. According to the method, sub-populations are divided and redistributed by adopting an original population in the aspect of population structure planning so as to guarantee the diversity of a population evolution process and avoid a local optimum phenomenon; local search variation is combined with global search variation in the variation strategy so as to achieve a balance between an optimal threshold of population survey and accelerated convergence; and the shortcomings caused by fixed parameters of a standard differential evolution algorithm are effectively overcome through the parabolic dynamic incremental change of a cross probability factor. Compared withother evolutionary algorithms on a benchmark test set, the improved algorithm has the advantage that the optimization and convergence speed can be obviously and significantly improved; and the improved differential evolution algorithm has a significant effect in accuracy and speed when being applied to image segmentation.

The invention discloses a conveying path combination optimization method based on a stage structured predator-prey model. Supposing that multiple animal populations grow in an ecological system, and each population is divided into two stage types of adult populations and young populations; the adult populations produce their young populations at a certain birth rate, and the young populations grow up into the adult populations after a period of time; the influence of mutual competition for living resources between the populations is shown in the influence on the characteristics; the population having larger proportion in all the populations has higher influence, and the population spreads its influence to other populations; the advantageous and strong populations spread their high quality characteristics to other populations; and if one population is continuously influenced by other populations, its growth state continuously changes, and the global optimal solution of the problem of conveying path combination optimization can be rapidly determined by using the change and the multi-population predator-prey system model having the stage structure.

The invention discloses a multi-population symbiotic evolution based radio frequency identification network layout optimization method, and mainly solves the problems of proneness to precocity, slow convergence and poor stability due to the fact that the genetic algorithm is used for network layout in the prior art. The method includes the implementation steps: firstly, randomly grouping populations, independently calculating fitness of each sub-population objective function, designing and sequentially comparing strategies of the objective functions and deleting operators tentatively; secondly, in the process of iteration, sharing the whole population till now, and finding out the global best fitness; thirdly, updating the own position of each sub-population according to the global optimal position for each sub-population so as to find out the optimal network layout scheme. The method has the advantages of small initial population size, fast convergence and high stability, and the problem about network layout can be solved effectively.