59 results about "Coevolution" patented technology

The invention provides a flexible job shop scheduling method based on multi-species coevolution, which belongs to the field of shop scheduling, and mainly overcomes the disadvantage that a flexible job shop scheduling method based on a genetic algorithm can not be exerted fully. The method comprises the main steps of: 1. setting parameters; 2. initializing species according to a set coding method; 3. calculating the fitness value of each chromosome in each species according to a set method, and recording the optimum fitness value and the constitute chromosomes thereof; 4. carrying out multi-species coevolution: carrying out evolution operations, i.e. chiasmata and variation, to chromosomes in each subspecies, and evaluating new chromosomes; and 5. judging whether a method termination criteria is achieved or not: if so, terminating the method and outputting the optimum fitness value and the constitute chromosomes thereof; and otherwise, jumping to the step 4. The invention can be used to obtain a high-quality scheduling scheme suitable for practical production of shops, can shorten production time, and can be used for scheduling management and optimization of the production process of shops.

The invention discloses an optimization design method of a radial-flow-type hydraulic turbine. In the design method, a unitary thermal optimization design, a three-dimensional modeling method of a through-flow part and a complete machine optimization platform are utilized, wherein the optimization platform comprises four modules, namely nozzle blade and impeller blade parameterization, a coevolution genetic algorithm, a self-adaption approximation model, and autocall of CFD (computational fluid dynamics). Through the parameterization, characteristic variables describing impeller blades, nozzle blade patterns and installation angle variation are extracted. The optimization target is to enhance the overall efficiency and expansion ratio of the hydraulic turbine simultaneously under a complete machine environment. The optimization platform can be used for reducing the calculated amount and accelerating the convergence by virtue of the following measures: the approximation model is built and updated by a dynamic sampling strategy, and enough prediction accuracy is obtained by virtue of less CFD calculation; and a complicated multivariable optimization problem is decomposed into a plurality of relatively independent and interactive subproblems by virtue of the coevolution genetic algorithm, so that not only can the characteristics of the original problem be maintained, but also the calculated amount is reduced effectively.

The invention discloses an automobile part welding optimal path planning method of multi-robot coordination. The method comprises a welding spot information preprocessing step, a welding spot path planning step of a hybrid genetic-particle swarm optimization algorithm based on coevolution and a motion path step in which welding spot path planning information is converted into a welding robot. In the method, a genetic algorithm and a particle swarm optimization algorithm are combined; based on a mutation characteristic of the genetic algorithm, a diversity of a particle swarm during a particle swarm algorithm iteration process and a global search capability can be increased; and a welding path optimal planning problem during an automobile part generation process can be solved.

The invention provides a method for generating test data covering parallel program paths based on coevolution, and aims to provide a method for automatically and efficiently generating test data covering parallel program objective paths. The method includes the following specific steps that firstly, a mathematical model of a test data generation problem is built, and a problem for generating the test data covering the parallel program paths is modeled into a single-object optimization problem; secondly, a coevolution genetic algorithm is designed to solve the model. According to the method, groups are divided into a plurality of sub groups and a cooperative team group according to the correlation of course paths and program input components. Each sub group is used for independently optimizing a part of input components relevant to one certain course path. After the sub groups are evolved into a certain period, excellent individuals of the sub groups are combined to form an initial individual of the cooperative team group so as to be used for optimizing complete program input. After the cooperative group is evolved into a certain period, the excellent individuals are returned to the sub groups. Through alternate coevolution of the cooperative team group and the sub groups, the expected test data are generated.

The present invention provides a multi-target distributed power source site selection constant volume method considering power supply reliability. The method comprises the steps of: performing calculation of a plurality of target functions of a distributed power source site selection constant volume, integrating the target functions to establish a target function of the distributed power source site selection constant volume considering the power supply reliability, combining a distribution network power balance constraint condition, a distributed power source output constraint condition and adistribution network node voltage constraint condition to establish an optimization model of the multi-target distributed power source site selection constant volume considering the power supply reliability, employing the particle swarm optimization and the non-dominated sorting coevolution algorithm to perform model solution, and obtaining a distributed power source site selection constant volume optimization scheme of multiple targets. The defects are overcome that a current site selection constant volume algorithm is not practical to perform multi-target and simple weighing, the multi-target distributed power source site selection constant volume method is simple in calculation and fast in rate of convergence, improves the scientificity of the distributed power source site selection constant volume scheme, relieves the influence of the distributed power source for the distribution network operation and improves the economical efficiency of the distributed power source.

The invention provides a method for domestication of salt-resistant activated sludge. The method comprises that substances (such as soil or water) in a high-salt environment are directly added into activated sludge having a normal salt concentration according to a certain ratio and then salt concentration loading is gradually increased, and in the above process, the microflora in the high-salt environment and the microflora in the activated sludge carry out bidirectional coevolution so that novel microflora is formed. The method is free of a complex bacterium screening process, has simple and easy processes, utilizes a novel salinity gradient-increasing scheme, prevents a biomass loss caused by overquick increasing of salinity pressure and realizes fast and efficient domestication of high-salt sludge.

The invention relates to an in-vitro directed coevolution method for modifying L-phenylalanine gene engineering strains, which is realized in a way that: using genes aroG and pheA on an L-phenylalanine gene engineering strain constructed in the room as a whole; carrying out the in-vitro directed coevolution modification by using an error-prone PCR technology and a recombinant DNA technology; and screening to obtain a mutant strain, of which the yield of L-phenylalanine is increased by 114%. In the invention, by modifying the gene formed by coupling and connecting aroG and pheA in series, the key genes aroG and pheA in the metaboly process of the L-phenylalanine are used as a whole to carry out the directed modification, thereby obtaining a new metabolic balance, so that the expressed enzyme has high-efficiency catalytic activity and can resist the feedback inhibition of the L-phenylalanine, thereby obtaining the new modified high-yield L-phenylalanine gene engineering strain by screening. The method can provide an example for modifying the acid production rate of any amino acid gene engineering strain.

The invention discloses a multi-path coverage test data coevolution generation method for a message-passing parallel program, and aims to efficiently generate test data covering multiple target pathsfor the message-passing parallel program. The method comprises the following specific steps of (1) constructing corresponding populations for the target paths in each scheduling sequence respectively,wherein individuals in the populations are coded program inputs; (2) designing population performance and individual performance evaluation indexes; (3) solving the populations by using a genetic algorithm, and migrating the individuals to the populations with good performance through individual migration in the process; and (4) stopping evolution of the populations corresponding to the covered target paths according to evolution results of all generations, and stopping an algorithm until the test data covering all the target paths is generated or maximum evolution generations are reached.

The invention relates to the technical field of bioinformatics, and provides a genome unit point variation pathogenicity prediction method and system and a storage medium. The method comprises the following steps: obtaining genome unit point variation data and coevolution conservative data according to a genome unit point variation position and a variation condition; preprocessing the genome unit point variation data and the coevolution conservative data to generate a matrix; loading the model, inputting the matrix, performing feature extraction through the densely connected convolutional neural network, splicing the feature data by adopting the multilayer perceptron, performing calculation, and outputting a prediction result. By adopting the method, the problems of low prediction accuracy, low reliability and high cost of genomic unit point variation pathogenicity in the prior art can be solved.

The invention discloses a specific primer pair for assisting identification of Klebsiella pneumoniae. The primer pair provided in the invention consists of DNA shown in sequence 1 of the sequence table and DNA shown in sequence 2 of the sequence table. The specific primer pair designed in the invention can be used for assisting identification about whether Bactrocera dorsalis (Hendel) carries Klebsiella pneumoniae only by means of PCR (polymerase chain reaction) and electrophoresis, and the identification is characterized by high sensitivity, simple process and no need for cloning. In order to increase accuracy, a PCR amplified product can be subjected to sequencing. The specific primer pair provided in the invention improves the efficiency of Bactrocera dorsalis (Hendel) symbiotic bacteria (Klebsiella pneumoniae), and creates conditions for further study of the coevolution relation between Bactrocera dorsalis (Hendel) and Klebsiella pneumoniae.

The invention discloses an adaptive coevolution algorithm-based information kernel extraction method, and mainly solves the problem of incapability of ensuring an optimal recommendation effect in theprior art. The method is implemented by the steps of (1) constructing a user score matrix; (2) initializing parent populations; (3) adaptively adjusting a cross operator selection probability; (4) adaptively adjusting a mutation operator selection probability; (5) classifying the parent populations; (6) building a team; (7) judging whether each team member comes from a parent elite population or not; (8) updating progeny elite populations; (9) updating progeny ordinary populations; (10) judging whether all information kernels of the parent elite population are central information kernels or not; (11) updating the parent populations; (12) judging whether an iterative frequency is equal to 200 or not; and (13) outputting an optimal information kernel. An experimental simulation result showsthat compared with an existing information kernel extraction method, the method provided by the invention has a better recommendation effect.

The invention discloses a deep neural network optimizing method based on coevolution and back propagation, belongs to the technical field of combination of deep learning and evolutionary computation, and mainly aims to solve the problem of tendency to fall into a local optimal solution during training of the deep neural network. The deep neural network optimizing method comprises the following steps: (1) firstly, optimizing a network by using a back propagation algorithm; (2) when a stopping condition is satisfied, optimizing by using a co-evolution algorithm; (3) continually repeating the previous steps until an iterative stopping condition is satisfied; and (4) performing final optimization to obtain a weight and a deviation as optical parameters. In the deep neural network optimizing method, the advantages of an evolution algorithm are applied to the training of the deep neural network, and large-scale parameters are optimized by coevolution. Meanwhile, a selection strategy is designed in conjunction with the back propagation algorithm in order to increase the optimizing speed of the coevolution, so that the whole network can be trained more efficiently, high optimizing performance is achieved, and the classification accuracy of the network is increased.

The invention discloses a multi-species coevolution method for solving the storage operation optimization problem with aisles, and belongs to the field of intelligent logistics and storage equipment.According to the method, the transverse aisles are added on the basis of traditional warehousing, the warehousing operation optimization model with the aisles is established, and the model is solved.In view of the defects that the existing solving technology is easy to premature and low in convergence speed, the invention provides a multi-species co-evolution optimization method based on the joint participation of a genetic algorithm, a particle swarm algorithm and an artificial fish swarm algorithm, i.e., through a multi-species competition symbiotic predation strategy based on a learning mechanism, the environment adaptability of each species can be enhanced; by introducing a variation mechanism, the population diversity of all species is synergistically improved, so that the evolutioncapability of a single species is improved, and meanwhile, the global optimization capability and the solving efficiency of the algorithm are also improved. According to the method, the operation efficiency of overall storage is improved, and the logistics storage can be promoted to be transformed and upgraded to be intelligent and green.

The invention relates to a feature optimization selection method for a pedestrian detection based on coevolution, which includes that: (1) a training sample is read in; (2) an original characteristic set is generated and a sample set is formed; (3) four populations are initialized and a type of characteristic is corresponded to each population; (4)an individual is decoded to a feature combination and then a new sample subset is obtained, and fitness of the individual is calculated; (5) a terminal condition is judged for whether the requirement is met, if the terminal condition is met, a characteristic subset denoted by a best individual in each population is used as the optimum relation of an algorithm; (6) a competition within the population, an inter-population competition and self-increase rules are used for choosing the individual according to the fitness of each individual, a method for single interior extrapolation and its variation are used for generating the next generation individual; (7) the (4) step is returned and the population is evolved until an feature selection terminal condition of the (5) step is satisfied. The invention decreases the complexity of computation, and can obtain an optimizing feature subset, and promotes the veracity for pedestrian classification.