74 results about "Adaptive evolution" patented technology

The invention discloses a strain in the field of genetic engineering and a method for producing dihydroxyacetone (DHA) by using the strain. The method is characterized in that: modified Gluconobacter oxydans is used to bioconvert glycerin into dihydroxyacetone; sldAB gene expression is added in the modified Gluconobacter oxydans through modification, and mgdh gene and madh gene are removed from the modified Gluconobacter oxydans; and the modified Gluconobacter oxydans is further subject to the adaptive evolution on a medium which uses glucose as the sole carbon source. The genetically engineered strain disclosed herein can grow well on the medium which uses glucose as the sole carbon source, overcomes the defects that wild type Gluconobacter oxydans can only use relatively expensive sorbitol and mannitol as the effective carbon source, and saves the production cost. In addition, compared with a genetically engineered strain GAN cultured on sorbitol, the genetically engineered strain GAN cultured on glucose has higher capability of producing DHA.

The invention provides a complex network cell discovering method under an adaptive evolution bat algorithm for self-media network data. The method includes the following steps that S1, mass data is acquired, a network structure model is established, according to the bat algorithm, a modularity function serves as a fitness function, a coding mode based on characters is adopted, and an initialized population is improved through a label propagation method; S2, the individual speed of the bat algorithm is converted into a mutation probability value, position update is calculated through a cross operator and a mutation operator, therefore, adaptive evolution of the common bat algorithm is achieved, the adaptive evolution bat algorithm is used for dividing a network, and a more accurate network cell division result is obtained. Compared with other intelligent algorithms for cell discovery, the algorithm has the advantages of being high in convergence rate and solution precision, and is more suitable for cell discovery under the large-scale network.

The invention provides a spiral antenna design method based on an adaptive evolution optimization algorithm. The spiral antenna design method comprises the following steps: firstly, establishing a structural model of a spiral antenna and an optimization mathematical model corresponding to the structural model; then, optimizing the antenna structure parameters in the optimization mathematical modelby adopting a reinforcement learning-based adaptive evolution optimization algorithm to obtain final optimized antenna structure parameters; and finally, according to the antenna structure parameters, adjusting the structure model to obtain the designed spiral antenna. The beneficial effects of the method are as follows: the parameters and the operation operator of the evolution algorithm are controlled by utilizing reinforcement learning; therefore, the self-adaptive differential evolution algorithm is realized, and a large amount of intermediate result data generated in the operation process of the self-adaptive differential evolution algorithm is used for guiding subsequent parameter setting of the evolution algorithm, so that the optimal antenna structure parameter of the spiral antenna is obtained, the efficiency is high, and the optimization performance is good.

The invention provides a method for improving tolerance and increasing the degradation rate of chlorella on phenol. The method comprises the steps that firstly, the chlorella in a logarithmic phase is selected and subjected to shaking culture in a culture medium containing the phenol at certain initial density under the conditions of a certain temperature and illumination, wherein set culture time serves as an adaptive evolution cycle; secondly, the chlorella evolved through one cycle is diluted, the chlorella with the same initial density as that in the last step is taken and subjected to shaking culture in another culture medium under the same conditions for the same time; the culture process is repeated until the growing speed of the chlorella and the degradation rate of the chlorella on the phenol tend to be stable. According to the method, the chlorella is subjected to adaptive evolution experiments, the growing speed of the chlorella and the degradation rate on phenol are increased, and the tolerance on phenol is improved; meanwhile, the initial inoculum density is reduced, the time needed for completely degrading the high-concentration phenol is shortened, wastewater treatment cost is reduced, and superior chlorella is provided for treating industrial wastewater.

The invention discloses a dynamic recommendation method based on user interest adaptive evolution, which comprises the following steps: firstly establishing a user interest model of each user, then segmenting user interest vectors according to a time sequence, calculating the similarity among the users, screening out a plurality of to-be-recommended users, and fully considering the characteristicthat the user interest is changed along with time; Based on historical comment records of the target user and the to-be-recommended user, obtaining a recommendation result; constructing a historical interest sequence matrix His; and performing interest evolution iteration for preset times through a PSO (Particle Swarm Optimization) algorithm to obtain a recommendation priority sequence of the target user and each user in the to-be-recommended user set, and finally recommending the users which are screened out from the to-be-recommended user set and ranked in front to the target user. By the adoption of the technical scheme, the stability of the recommendation result is controlled, meanwhile, the interest change of the user is reasonably simulated, and the recommendation accuracy, predictability and comprehensiveness are improved.

The invention discloses a saccharomyces cerevisiae strain enduring ferulic acid. The saccharomyces cerevisiae strain is a strain which is obtained after adaptive evolution of saccharomyces cerevisiaewith a ferulic acid inhibitor culture medium and has remarkably improved endurance to ferulic acid, the strain is classified and named as Saccharomyces cerevisiae PAT02 and is preserved in the China General Microbiological Culture Collection Center, and the preservation number is CGMCC No.18022. The saccharomyces cerevisiae strain is capable of enduring ferulic acid, and when the strain is appliedto a fermentation system with the ferulic acid, the growth performance and the ferulic acid degradation velocity of the strain are both higher than those of an original strain.

The invention discloses an adaptive evolution method for gluconobacter oxydans efficiently utilizing non-glucose carbon sources and application of the adaptive evolution method, and belongs to the biotechnology and microbial technology field. The method comprises the concrete steps that firstly, the gluconobacter oxydans is cultured in a hydrolysate containing inhibitor lignocellulose, then the cultured gluconobacter oxydans is transferred into a hydrolysate without the inhibitor for further culture, and the gluconobacter oxydans is alternately transferred in such a way until the fermentationperformance of a strain reaches stable. The new strain obtained by acclimatization has obviously improved utilization efficiency of the non-glucose carbon sources, and the strain is named as gluconobacter oxydans RM7, and the collection number of the strain is CGMCC No.14801; and the method is simple and efficient, and has important referential significance in breeding of strains containing an inhibitor lignocellulose system.

The invention belongs to the technical field of microalgae organisms, and in particular relates to an algae species breeding method of microalgae capable of tolerating high-concentration flue gas. The algae species breeding method refers to the highest emission flue gas concentration allowed by the national standard as an environmental stress condition, adopts a strategy of gradually increasing the flue gas concentration, and enhances the tolerance of the microalgae to the flue gas by activating adaptive evolution, so as to carry out biological fixation on the flue gas. Before the adaptive evolution, the best growth flue gas concentration of the microalgae is about CO2 2%+NOx 0ppm+SO2 0ppm; when the flue gas concentration is CO2 10%+NOx 200ppm+SO2 100ppm, the microalgae cannot survive obviously. By means of the adaptive evolution of the breeding method, the obtained microalgae can tolerate CO2 10%+NOx 200ppm+SO2 100ppm and maintain a more biomass accumulation, and is high in sugar content. Compared with large-scale screening, the breeding method provided by the invention has the advantages of less amount of work, definite purpose and high efficiency.

The invention discloses a breeding method of a high-concentration CO2-tolerant microalgae species. The breeding method comprises the following steps: 1) cultivating inoculated microalgae with the presence of less than 40% of CO2 and 20-500 [micro]moles of photons m-2s-1 at 15-40 DEG C under the conditions of a light-dark cycle of (1-24h)/(0-23h) and an apparent ventilation rate of 0.01-1.0cm/s for 2-10 days as an adaptive evolution culture cycle; 2) inoculating the microalgae which is cultivated in the step 1) once again, and cultivating by a new cycle under conditions same with that of the step 1) for 2-10 days; and 3) turning to a next culture cycle as methods prescribed within the step 2) by taking operations prescribed within the step 1) as a culture cycle, and repeating the process by 10-200 culture cycles, so that the CO2-tolerant microalgae species is obtained. The breeding method disclosed by the invention is applicable to the breeding of microalgae bio-fuel species; and meanwhile, the breeding method can also provide a basis for microalgae biological studies.

The invention discloses a scheduling method based on a multi-strategy water wave optimization algorithm. According to an operation mechanism of a water wave optimization algorithm and problem characteristics of three operations in the algorithm, the method mainly comprises the steps that for a continuous optimization problem, the water wave algorithm based on reverse learning and a covariance matrix adaptive evolution method is proposed, and refraction operation of the original water wave algorithm is replaced by updating a population with a covariance matrix to improve population diversity. In the zero idle flow shop scheduling problem, a skewness and variable coefficient combined method is provided for an initialized population to generate an initial sequence population to improve population stability, and a neighborhood search method based on different three operation operation attributes is provided. According to a distributed zero-idle flow shop scheduling problem with an assembly process, machine learning and variable neighborhood search are introduced, so that a proposed algorithm obtains a high-quality solution within limited time through training and reward in an iterative updating process. The method has the advantages of being simple in frame, easy to implement and clear in logic.

Xylitol is an important functional food additive and chemical raw material, and has extensive applications. According to the invention, a high-temperature adaptive evolution method is utilized to screen TIB-Y1020 of high-temperature resistant Candida maltose which is CGMCC (china general microbiological culture collection center) No. 5521 and can grow well at the temperature ranging from 38 DEG C to 42 DEG C without influencing the transformation production of the xylitol. The high-temperature resistance strain can reduce the manufacturing cost increase brought by temperature reduction, thereby having an extensive industrial application prospect.

The invention discloses an adaptive dynamic evolution calculation method for a rough machining process. The method is used for processing complicated mould cavity type parts. According to the method,local inscribed circle movement representation models are firstly established according to the corresponding relationship between the central axis of a feature and the inscribed circle arc centers ofa machining area to guide calculation of a primary rough machining area and a residual area of the feature; then, the type is identified based on the central axis topological structure of the residualarea; then, according to the primary rough machining area and the residual area of the feature, a dynamic evolution model of the rough machining area and the residual area of a part based on the initial process plan is established; finally, an adaptive evolution mechanism of the part rough machining process driven by a process design idea is proposed to eliminate the interference and undercut problems in the rough machining process and ensure the continuity of the rough machining process. According to the method, the adaptive evolution method of the rough machining process is decided according to the type of residuals and the moment of forming the residuals to make up for the deficiencies of an existing method of analysis from the geometrical point of view, the burden of process designersis reduced, and the efficiency of numerical control programming is improved.

The invention discloses an adaptive evolution method of a cloud service system based on an improved wolf pack algorithm. The method can be used for quickly and efficiently completing the adaptive evolution of the cloud service system, and includes the following steps: S1, constructing a candidate subset of the cloud service system; S2, performing artificial wolf coding on the cloud service system;S3, defining a fitness function of the adaptive evolution of the cloud service system; S4, initializing a wolf pack by using information entropy; S5, carrying out migration on the cloud service system; S6, calling the cloud service system; S7, conducting a siege of the cloud service system; S8, updating the position of the leader wolf; S9, updating the wolf pack; S10, judging termination conditions; and S11, outputting a target cloud service system corresponding to the position of the leader wolf.

The invention discloses a self-adaptive evolutionary algorithm for supply and demand matching of reservation type shared parking under an AVP condition, which utilizes the empty driving characteristic of an AVP vehicle, parks the vehicle at different parking spaces at different time periods of shared parking demands, maximally meets the shared parking demands with the least AVP shifting times, and improves the utilization rate of the parking spaces. The method specifically comprises the following steps: on the basis of dividing shared parking supply and demand time, defining pairing, time period pairing and pairing scheme concepts; designing an adaptive optimization strategy of the AVP vehicle for berth adjustment in all time periods by analyzing the condition of berth adjustment of the vehicle in a given time period and the influence of berth change on the number of times of vehicle moving in the pairing scheme; a brand-new algorithm is designed, the calculation time is shorter than 1 second when the algorithm is used for solving a medium-scale matching problem, and the algorithm can be used for actual online parking space distribution of the shared parking platform.

The invention discloses a method for producing L-leucine by efficiently utilizing starch, and belongs to the technical field of biocatalysis. According to the invention, temperature-induced adaptive evolution treatment is carried out on corynebacterium stain producing L-leucine, so that the tolerance of the strain against high temperature is improved. Then, on the basis of the mutant strain, heterologous expression is carried out on an alpha-amylase encoding gene derived from bacillus microorganisms (such as B. amyloliquefaciens), and a metabolic pathway capable of efficiently utilizing starchis constructed in the corynebacterium stain producing L-leucine, so that the defect that the optimal thallus growth temperature is inconsistent with the optimal action temperature of alpha-amylase isovercome, and the recombinant corynebacterium glutamicum capable of producing L-leucine by efficiently utilizing starch is obtained.