253 results about "Population diversity" patented technology

The invention discloses a cascade hydropower station group optimized dispatching method based on an improved quantum-behaved particle swarm algorithm. The problems that local optimum happens to the quantum-behaved particle swarm algorithm at the later iteration period due to premature convergence for the reason that population diversity is decreased, and an obtained hydropower station group dispatching scheme is not the optimal scheme are mainly solved. The hydropower station group optimized dispatching method based on the improved quantum-behaved particle swarm algorithm is characterized by comprising the steps that first, power stations participating in calculation are selected, and the corresponding constraint condition of each power station is set; then, a two-dimensional real number matrix is used for encoding individuals; afterwards, a chaotic initialization population is used for improving the quality of an initial population, the fitness of each particle is calculated through a penalty function method, the individual extreme value and the global extreme value are updated, an update strategy is weighed, the optimum center location of the population is calculated, neighborhood mutation search is conducted on the global optimum individual, the positions of all the individuals in the population are updated according to a formula, and whether a stopping criterion is met or not is judged. The hydropower station group optimized dispatching method based on the improved quantum-behaved particle swarm algorithm is easy to operate, small in number of control parameters, high in convergence rate, high in computation speed, high in robustness, reasonable and effective in result, and applicable to optimized dispatching of cascade hydropower station groups and optimal allocation of water resources.

The invention discloses a CFD and improved PSO based microscopic wind-farm site selection method of a complex terrain, belonging to the technical field of microscopic wind-farm site selection. The method comprises the steps of simulating values of wind farms of the complex terrain via CFD to obtain wind resource distribution, extracting a CFD value simulating result corresponding to a position at the hub height of a wind generating set, establishing a wake model, a wind farm model and a target function, solving a particle swarm speed and position updating equation by utilizing the improved PSO which is combined with niche technology, chaotic mutation and punishment, and solving the target function in an iterative manner by combining the wake model to generate an optimal distribution result. According to the method, the CFD value simulating result is applied to optimizing microscopic site selection for wind farms in the complex terrain, influence of wind energy distribution and wake is fully considered, and layout of the wind farms is optimized; and the whole convergence process is highly efficient due to improvement of population diversity, chaotic mutation of particles and change of moving dimensions of populations.

The invention provides a cloud computing task scheduling method based on the improved NSGA-II and relates to the field of cloud computing. The method includes the steps that firstly, the number of meta tasks is input, and a task scheduling model is generated through a DAG chart; secondly, the number of virtual machines is input, the virtual machines of different specifications are generated randomly, and a cluster model is generated; thirdly, a cloud computing task scheduling problem is expressed as a multi-target solving problem relevant to time and cost, and the problem is solved with the combination of the improved NSGA-II. A new population is generated by the adoption of a similarity task sequence crossover operator and a displacement mutation operator in the population evolution process according to the features of task scheduling, meanwhile, a congestion distance self-adaptation operator is introduced in, it is ensured that the optimal border of the obtained time and cost is obtained, and cloud computing task scheduling is achieved. The searching capability for the optimal solution in the application of cloud computing task scheduling becomes stronger, the population diversity can be better kept, and the optimal solution set with the better distributivity is obtained.

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 belongs to the technical field of network security and particularly relates to an APDE-RBF (Affinity Propagation Differential Evolution-Radial Basic Function) neural network based network security situation prediction method. The APDE-RBF neural network based network security situation prediction method comprises the steps of dividing and clustering sample data by utilizing an AP clustering algorithm to obtain the number of nodes of hidden layers of the center and network of the RBF; obtaining population diversity by using AP clustering, changing a zoom factor and a crossover probability of a DE algorithm adaptively and optimizing the width and connection weight of the RBF; meanwhile, performing chaotic search on elite individuals and population diversity center of each generation of population in order to avoid falling into local optimization and jumping out of a local extreme point; and determining a final RBF network model, inputting a test dataset and outputting a situation prediction value. The APDE-RBF neural network based network security situation prediction method aims at improving the prediction precision for the network security situation while enhancing the generalization ability.

The invention relates to the technical field of micro grids, and in particular relates to a micro-grid multi-objective optimal scheduling method based on model predictive control and a micro-grid multi-objective optimal scheduling energy management system. According to the invention, a Gaussian process (GP) prediction algorithm is used to solve the maximum generated power of each renewable energysource, the load power demand of each stage and the forecast curve of the power purchase price of a large power grid in a certain time window in the future; the change of the energy storage efficiencyof an energy storage unit is considered in a model; constraints consider steady state constraints and dynamic constraints of the power grid; the optimal scheduling problem for solving the minimum power generation cost and reliability cost uses the Patrice Concavity Elimination Transform (PaCcET) algorithm; and the algorithm has superior performances in terms of convergence and population diversity. The energy management system ensures safe and economic operation in the grid-connected and islanded mode of a micro-grid. According to the invention, the economic utilization rate of the energy storage unit and renewable energy is improved under the premise of ensuring safe scheduling.

The invention relates to an improved particle swarm algorithm and the application of the improved particle swarm algorithm. The improved particle swarm algorithm includes the following steps that firstly, the algorithm is initialized; secondly, the positions x and speeds v of particles are randomly initialized; thirdly, the number of iterations is initialized, wherein the number t of iterations is equal to 1; fourthly, the adaptive value of each particle in a current population is calculated, if is smaller than or equal to , then is equal to and is equal to , and if is smaller than or equal to , then is equal to and is equal to ; fifthly, if the adaptive value is smaller than the set minimum error epsilon or reaches the maximum number Maxiter of iterations, the algorithm is ended, and otherwise, the sixth step is executed; sixthly, the speeds and positions of the particles are calculated and updated; seventhly, the number t of iterations is made to be t+1, and the fourth step is executed. By means of the improved particle swarm algorithm, at the initial iteration stage, the population has strong self-learning ability and weak social learning ability, and therefore population diversity is kept; at the later iteration stage, the population has weak self-learning ability and strong social learning ability, and therefore the convergence speed of the population is improved.

The invention discloses an optimal configuration method for an electric automobile charging pile. The method comprises the following steps: predicting the charging power demand of a planning area by a Monte Carlo simulation method on the basis of analysis of various electric automobile behavior characteristics; building a bi-level planning model of charging station investment profit and user perception effect under the consideration of constraint conditions such as a power grid, a charging station and an investor budget; and introducing a KKT (Karush-Kuhn-Tucker) condition to realize equivalent conversion of a double-layer model and a single-layer model, and solving by adopting a variable neighborhood search-particle swarm mixed algorithm with a convergence polymerization degree. Through adoption of the method, the problem of premature convergence of particles is avoided effectively; population diversity is increased; the optimization capacity of the particles and the convergence speed of the algorithm are improved and increased remarkably; the calculation speed and the calculation accuracy of optimal configuration of the charging station are increased; and important references are provided for investors to plan and build the charging station under an enterprise-dominant pattern.

The invention discloses a robot path planning method based on an adaptive sparrow search algorithm. The method comprises the following steps: S1, introducing an adaptive weight and a differential variation strategy to propose the adaptive sparrow search algorithm; and S2, planning the path of the robot by adopting an adaptive sparrow algorithm. According to the method, the capacity of the standardSSA algorithm for large-scale optimization and local precise optimization in the early stage is improved through the self-adaptive strategy, the population diversity of the SSA algorithm is improvedthrough the differential mutation strategy, the problem that the SSA algorithm is prone to falling into local optimum in the later stage of search is solved, and therefore the search performance and development performance of the algorithm are improved; and meanwhile, the algorithm has relatively high convergence rate and relatively strong optimization capability.

The invention discloses a novel group searching method for optimal scheduling of cascade reservoir groups, and relates to the field of power generation scheduling of a hydropower system. An elite set dynamic updating strategy is combined with a neighborhood variation search mechanism, global search and local exploration are considered in a balanced manner in the method, and both the diversity of population and the convergence speed of the method are taken into consideration. The water level of a hydropower station serves as a state variable, an optimization target is to maximize the comprehensive generating capacity of the cascaded reservoir groups in a scheduling period, a certain amount of spider individuals are initialized, internal corporation of the sub-spider groups, marriage of heterosexual individuals, dynamic update of elite individuals and a neighborhood variation search strategy are implemented generation by generation, and an optimal scheduling strategy of the cascade reservoir groups is approached gradually. Dynamic update of the elite individuals can ensure that introduced elite spider population evolutes effectively, and the searching capability and the exploration capability of the method are balanced; the excellent individual neighborhood variation strategy can maintain the population diversity, and the calculation speed and the convergence speed of the method are improved; and the method has high population value and good application prospects.

The invention discloses a method for improving the cuckoo optimization algorithm, which effectively solves the defects that the traditional cuckoo optimization algorithm has low convergence precision and easy to fall into local optimum in the later stage of iteration. Firstly, through dynamic adaptive adjustment of the step size a and the discovery probability Pa, the refined search process of the algorithm is realized; secondly, the reverse learning strategy is introduced to increase the diversity of the population and improve the iterative operation efficiency of the algorithm; finally, according to the preset stagnation times, the Based on the multi-start strategy, jump out of the local optimum, and then get the optimal solution. According to the embodiment of the improved cuckoo optimization algorithm established in the present invention, the simulation results show that the method is improved to a certain extent in terms of convergence speed, convergence precision and convergence reliability.

The invention provides a complex network community discovery method based on spectral clustering improved intersection. Individuals in a population are divided by adopting spectral clustering having the advantages that clustering can be performed on any shapes of sample space, and genetic operation is performed by selecting the individuals of different divisions in intersection operation so that population diversity is increased and falling into local optimum can be avoided; and the similar individuals can effectively maintain the excellent characteristics of the individuals and maintain evolutionary direction of the population even the similar individuals cannot effectively increase population diversity so that genetic operation is also performed by the individuals of the same division when genetic operation of the individuals of different divisions is performed, and the two optimal individuals in the individuals generated by the two modes are selected to act as filial generation individuals. Intersection operation of the two modes is performed simultaneously so that falling of the algorithm into local optimum and low convergence speed can be avoided, convergence speed can be adjusted and balance between the optimal solutions can be searched.

The invention discloses an embedded software test data generating method based on a fuzzy-genetic algorithm and relates to a test data generating method. The problem that a test dataset generated with an existing test data generating method is large in scale, so that generating time is long is solved. A genetic algorithm is improved, a fuzzy control method is used, through population entropy and the disperse degree, selecting of a genetic operator in a genetic process is controlled in a self-adaptation mode, when population diversity becomes poor, crossover probability and mutation probability are enlarged, so that population is evolved in a global-optimum direction, and the scale of test data is decreased. Then, an ant colony algorithm is used for sorting the generated combination test data according to the large disperse degree, so that the distance between adjacent test data values is enlarged, and test data sorting with the large disperse degree are selected from the optimum path sorting of all combination test data and is used as final embedded software test data for outputting. The embedded software test data generating method is suitable for embedded software test data generating.

A minimal spanning tree-based clustering genetic algorithm complex web community mining method belongs to the field of complex web community mining technology and is characterized in that the method comprises the following steps: carrying out computer initialization, carrying out population initialization, clustering population by a minimum spanning tree method, carrying out single-point crossover operation, mutation operation and selecting operation on individuals after population clustering, and carrying out iteration for T times so as to obtain the optimum community division of complex networks. Through minimal spanning tree-based clustering of populations and by crossover among populations, population diversity is maintained, and immaturity convergence is inhibited. By crossover operation of excellent individuals in species, probability of searching spaces with better solutions is increased. By selecting a neighbor-node for maximizing local modularity M1 as a variance value, search efficiency of the algorithm is improved.

The invention discloses a heavy haul train operation curve multi-objective optimization method based on a hook buffer device model. Based on running line constraint conditions of the heavy haul train,a dynamic longitudinal dynamics model and a hook buffer device model in the train operation process are established; A multi-objective genetic algorithm is used to establish a train optimization control model. meanwhile, the premature phenomenon of the genetic algorithm is considered, the genetic algorithm parameters are dynamically adjusted through the self-adaptive algorithm, the self-adaptivegenetic algorithm combining the self-adaptive algorithm and the genetic algorithm can keep the population diversity, meanwhile, the convergence of the genetic algorithm is guaranteed, and a train operation optimization curve is obtained. For a complex nonlinear heavy haul train operation process, a dynamic longitudinal dynamics model and a hook buffer device model of the train operation process are established, a train optimization operation model is established by applying a multi-objective genetic algorithm, a train operation curve is optimized, and safe, punctual and energy-saving operationof a train is realized.

The embodiment of the invention provides a resource scheduling optimization method based on a binary space partitioning tree. The binary space partitioning tree drives search, when overlapping individuals occur under the action of a selection operator, self-variation drive of a genetic algorithm that local search, neighborhood search and cross-domain search are carried out in a search space with directivity is achieved, and therefore the overlapping individuals are effectively prevented from occurring, population diversity is kept, and random search of the genetic algorithm is changed.

The invention discloses a method for improving a salp swarm algorithm, and aims to improve the salp swarm algorithm to overcome the defects that the salp swarm algorithm cannot perform accurate searchin the later stage of iteration, is poor in population diversity and the like. By adding an attenuation factor, the search range is flexibly controlled and the algorithm convergence speed is increased, and by introducing a dynamic learning strategy, the assistance effect of a follower on optimization is enhanced, higher convergence precision of the algorithm is achieved, and the optimization performance of the salp swarm algorithm is improved. The convergence precision and the convergence speed of the improved salp swarm algorithm are greatly improved.

The invention discloses a reactive power optimization method of an electric power system based on an improved CSO algorithm. The algorithm is a swarm intelligent search algorithm based on an improved CSO (ICSO) algorithm. The reactive power optimization method mainly comprises a horizontal cross operator, a longitudinal cross operator and a differential mutation operator. In horizontal cross, every two of all particles in a population are non-repeatedly paired in the horizontal cross, and the paired particles and the edges thereof are searched and updated in real time; in longitudinal cross, all dimensions are paired and then subjected to arithmetic cross; in differential mutation, all particles are subjected to mutation disturbance and cross and finally subjected to preferential selection; the three operators update the population through the selection operation, so that the convergence rate is accelerated and the population diversity is kept. The reactive power optimization method has the beneficial effects that the convergence speed of the ICSO algorithm is high, information exchange among individuals in the population is complete, the global convergence capability is strong, the particle diversity is good, and the reaction power optimization method has good applicability aiming at the high-dimensionality, multi-constraint and nonlinear complicated practical problem of reactive optimization of the electric power system.

The invention discloses a method and a system for optimally dispatching cascade reservoirs on the basis of quantum-behaved particle swarm algorithms. The method includes acquiring initialized population according to established objective functions for solving problems for optimally dispatching the cascade reservoirs and utilizing the initialized population as parent-generation particles; constructing small-world networks to obtain adjacent matrixes; updating the parent-generation particles according to the adjacent matrixes and generating child-generation particles; computing the fitness of the child-generation particles according to fitness functions; comparing the fitness of the parent-generation particles to the fitness of the child-generation particles by the aid of competition operators, selecting the child-generation particles with the good fitness and utilizing the selected child-generation particles as parent-generation particles for next iteration; judging whether current iteration numbers are larger than the maximum thresholds or not; carrying out computation if the current iteration numbers are larger than the maximum thresholds and outputting cascade reservoir optimal dispatching computation results. The method and the system have the advantages that the quantum-behaved particle swarm algorithms are improved by small-world network models, so that the population diversity can be kept by improved algorithms, the shortcoming of easiness in trapping in local optimization of basic quantum-behaved particle swarm algorithms can be overcome, and effects of optimally dispatching the cascade reservoirs can be improved.

The invention provides a genetic algorithm by employing guided local search for a multi-objective optimization problem. The algorithm is used for the field of flexible job-shop scheduling. A flexible job-shop scheduling problem belongs to an NP-Hard problem, optimization of multiple objectives often needs to be faced in real production, and the objectives are in interaction and conflict. The genetic algorithm aims at solving the problems of too fast convergence, insufficient population diversity and over-high calculation cost of enumerating all neighborhood solutions caused by continuous cross breeding of close relatives in a genetic operation in the genetic algorithm in the prior art. According to the algorithm, a procedure of calculating a crossover rate and a mutation rate before genetic crossover and mutation and a procedure of searching a movable process and a feasible position by using the guided local search are designed for these problems; and through introduction of the two procedures, the calculation cost is reduced while algorithm premature is avoided. The algorithm is high in practicability and can be well used for actual job-shop scheduling.

The invention belongs to the technical field of electrics, and particularly relates to a method for designing a motor through numerical calculation and analytical analysis combined parameter collaborative optimization. The changes of electromagnetism, temperature, fluid, thermal stress, vibration, noise and other physical parameters in the motor are studied through the numerical calculation, an electromagnetic performance analytical expression function cluster with the structural member size as the variant, a maximum working temperature analytical function of different assemblies, a maximum temperature difference analytical function of the different assemblies, a maximum thermal stress analytical expression function, a motor electromagnetic noise change function, a maximum vibration mode value of different directions of the assemblies and a constant frequency analytical expression function are concluded, then the refined designing of the structural member size is carried out by comprehensively taking performance of all aspects of the motor into consideration, and the calculating accuracy of all performance indexes is greatly improved. An objective function is improved through a non-equilibrium relative both-way weighting method, and the effect on a calculating result of the values of different performance indexes is removed. The quantum calculation is introduced into an intelligent optimization algorithm, and the algorithm has better population diversities, global optimization capabilities and higher convergence speed.

The invention relates to an intelligent optimization method for ship dynamic positioning thrust distribution. The method comprises steps that S1, the layout of a ship propulsion unit is determined, parameters of each thruster are loaded, and the parameters of the thruster include the variable range of the thruster thrust direction, the variable range of the thrust magnitude, the change rate rangeof the thrust direction and the change rate range of the thrust magnitude; S2, change rate restriction of a thruster state vector is acquired according to the parameters of each thruster and a presentstate, after a target control instruction is received, a multi-step optimization problem model is established; S3, a global optimal solution of a state of the thruster in the long-term change range is acquired through the genetic algorithm; and S4, an optimal multi-step decision sequence for driving the thruster is acquired, and distribution decisions are outputted. Compared with the prior art, problems of slow convergence and rapid degradation of population diversity of the genetic algorithm are solved.

The invention discloses a multi-threshold image segmentation method based on a comprehensive learning differential evolution algorithm. The method comprises the steps that in the mutation operation process of the differential evolution algorithm, a Binary tournament selection method is utilized to select an individual from species at random, a comprehensive individual is generated by the individual and an optimal individual, then the comprehensive individual serves as a basic individual, a mutation operation is carried out on the basic individual to generate a mutation individual, the searching speed is accelerated as fast as possible while the population diversity is kept, and then a crossover operation operator and a selection operation operator of a traditional differential evolution algorithm are carried out. Meanwhile, a zoom factor value and a crossover probability value are adjusted adaptively according to current search feedback information, so that the robustness of the algorithm is reinforced. The steps are repeatedly executed until a terminal condition is met, and the optimal individual obtained in the computation process is a final segmentation threshold of an image. By means of the multi-threshold image segmentation method based on the comprehensive learning differential evolution algorithm, the probability of local optimum can be reduced, the image segmentation accuracy is improved, the segmentation speed is accelerated, and the real time performance of the segmentation is improved.

The invention relates to a TSP problem path planning method. The method comprises the following steps: initializing; reading the position and calculating the distance; initializing a population through a greedy algorithm; replacing the worst individuals with randomly generated individuals; calculating the fitness; selecting; crossing; performing variation; randomly performing simulated annealing on the plurality of individuals; calculating the fitness; giving the contemporary optimal solution and the variant solution thereof to the first individual and the second individual respectively; and iterating until the termination condition is met. The population generated by the greedy algorithm has randomness and high quality, and optimization can be accelerated. A plurality of worst individualsare replaced by randomly generated individuals, so that the influence of differential solutions is reduced, and precocity is avoided. Some better solutions can be found through simulated annealing, and precocity and local optimization are avoided. The storage of the optimal solution and the variant solution of the optimal solution retains excellent information and increases population diversity.According to the invention, a shortest access path can be effectively and quickly planned, so that the method is an effective method capable of providing path planning for the TSP problem.

The invention provides a flexible job shop scheduling method and system. The method comprises steps that S1, the initial population S is generated based on basic parameters of a flexible job shop scheduling problem FJSP, and the initial population S is taken as a parent population P; S2, the parent population P is selected, crossed and mutated to obtain a temporary progeny population T; S3, basedon the temporary progeny population T, the parent population P is subjected to niche pre-selection operation to obtain a progeny population C; S4, a fitness value of each individual in the progeny population C is calculated, and the individual with the highest fitness value in the progeny population C is taken as the optimal solution of the FJSP; and S5, based on the optimal solution, a job shop corresponding to the FJSP is scheduled. The method is advantaged in that the solution search space can be made to maintain population diversity, the obtained solution can be guaranteed to converge to the global optimum, and thereby the job shop scheduling effect can be improved.

The invention provides a high-throughput low-cost SNP (single nucleotide polymorphism) genotyping method based on a liquid molecular hybridization principle. The method comprises the following steps: extracting biological genome DNA (deoxyribonucleic acid) and carrying out biotin labeling on the biological genome DNA; designing site-specific hybridization primers LSP1 and LSP2 and carrying out 5' phosphorylation on LSP2; hybridizing an LSP1 mixture and an LSP2 mixture with the genome DNA to obtain a hybridization adsorption product; carrying out extended linkage reaction to obtain a target DNA fragment; carrying out a round of PCR (polymerase chain reaction) amplification on a universal primer; carrying out PCR amplification on the Barcode specific primer of the recovered target fragment; carrying out high-throughput sequencing; obtaining SNP site genotyping information through analysis. The method combines the site selection flexibility of the liquid hybridization technology with the advantages of high throughput and low cost of the high-throughput sequencing technology and has great application value and wide popularization prospect in the research fields such as large-scale screening of SNP, genome-wide association study, population diversity evaluation, gene function analysis and the like.

The invention discloses a plate-fin heat exchanger core structure optimization method based on dynamic pixel granularity. The method comprises the following steps: establishing a plate-fin heat exchanger core structure optimization design model according to a plate-fin heat exchanger core runner structure, providing dynamically-updated pixel granularity, enlarging the population search range and keeping the population diversity; and providing a pixel distance calculation model of non-head and tail particles and head and tail particles, calculating the cross and mutation operation probabilities in a self-adaption manner according to the pixel distances of the particles, and respectively adopting random cross and Gaussian mutation, so as to enhance the global population search capability, improve the local population search efficiency, prevent the algorithm from getting into local optimum and realize the purposes of wide coverage and uniform distribution of Pareto optimal solutions. According to the method, the heat exchanger core structure design efficiency can be improved, and relatively reasonable design parameters are provided. The plate-fin heat exchanger optimally designed by the method has the obvious characteristics of uniform passage load, small secondary heat transfer temperature difference, small flow resistance and high heat exchange efficiency.