1090 results about "Evolutionary algorithm" patented technology

Described invention and its embodiments, in part, facilitate discovery of ‘Most Recent Common Ancestors’ in the family trees between a massive plurality of individuals who have been predicted to be related according to amount of deoxyribonucleic acids (DNA) shared as determined from a plurality of 3rd party genome sequencing and matching systems. This facilitation is enabled through a holistic set of distributed software Agents running, in part, a plurality of cooperating Machine Learning systems, such as smart evolutionary algorithms, custom classification algorithms, cluster analysis and geo-temporal proximity analysis, which in part, enable and rely on a system of Knowledge Management applied to manually input and data-mined evidences and hierarchical clusters, quality metrics, fuzzy logic constraints and Bayesian network inspired inference sharing spanning across and between all data available on personal family trees or system created virtual trees, and employing all available data regarding the genome-matching results of Users associated to those trees, and all available historical data influencing the subjects in the trees, which are represented in a form of Competitive Learning network. Derivative results of this system include, in part, automated clustering and association of phenotypes to genotypes, automated recreation of ancestor partial genomes from accumulated DNA from triangulations and the traits correlated to that DNA, and a system of cognitive computing based on distributed neural networks with mobile Agents mediating activation according to connection weights.

A method and apparatus is provided for constructing a predictive model for a system based on a priori qualitative modeling of the system and on historical database collected from past activity of the system or past events in the system. An expert provides grouping of parameters and qualitative dependencies between parameters and attributes, wherein some of the attributes may be conceptual or virtual attributes. The present invention extends existing methods of `evolutionary algorithms` in order to build successive sets of quantitative predictive models for the system, wherein parts of each model are evolved by the evolutionary algorithm and parts of each model are derived using the historical database. According to the present invention a model constructed by this method can be incorporated as a predictive model into a diagnosis or control apparatus without the need for human inspection, as the model complies with the expert's knowledge about the system. The present invention also provides a method to update the constructed model when new data is delivered, thus adjusting the model to changes in the environment.

The present invention provides a fault prediction and condition-based repair method of an urban rail train bogie. An optimum service life distribution model of a framework, a spring device, a connecting device, a wheel set and axle box, a driving mechanism, and a basic brake device of a bogie is determined by adopting a method based on survival analysis; a reliability characteristic function of each subsystem is obtained; then, a failure rate of each subsystem of the bogie is calculated by adopting a neural network model optimized by an evolutionary algorithm; and finally, proportional risk modelling is conducted by taking the failure rate and safe operation days of each subsystem of the bogie as concomitant variables; and on the basis of cost optimization, thresholds and control limits for condition-based repair of a bogie system are obtained.

The invention discloses a method and system for site selection of a base station. The method comprises the following steps: obtaining parameter information of a preset type according to a networking type and a zone type of a target zone and introducing the obtained parameter information into a preset mathematical model to form a mathematical model of the target zone; selecting a preset number of sub sets from a candidate base station set of the target zone; and according to the mathematical model of the target zone, using an evolutionary algorithm to carry out solving on the selected of sub sets with the preset number, thereby obtaining a station site and configuration of a newly-established base station. According to the method and system for site selection of a base station, according to different geographical environments and networking types of different target zones, a TD-LTE network base station site and configuration are selected, wherein the TD-LTE network base station site and configuration correspond to the geographical environment of the target zone; and according to the selected base station site and configuration, the new base station is established. Therefore, the data traffic requirement with high bandwidth and high quality can be met; and a high-speed wireless broadband service can be provided for a large number of users.

A method, computer program storage medium and system that implement evolutionary algorithms on heterogeneous computers; in which a central process resident in a central computer delegates subpopulations of individuals of similar fitness from a central pool to separate processes resident on peripheral computers where they evolve for a certain number of generations after which they return to the central pool before the delegation is repeated.

The invention discloses a method for establishing virtual reality excavation dynamic smart load prediction models. The method includes the steps that the knowledge excavation technology is adopted so that a virtual reality analysis environment can be formed, the influence relation between fixed quantities is explored, and an input variable candidate set is determined; smart load prediction models of a support vector machine of a self-adaptive structure and an Elman neural network and the like are established, wherein input variables are determined by the support vector machine through the attribute screening technology and parameters are optimized by the support vector machine through a flora tendency differential evolutionary algorithm; a region load smart load prediction model based on data slice excavation is established; a load curve prediction model combined with dynamic electrovalence factors, user characteristics and the user response electric quantity is established, so that linked correcting prediction of loads, electrovalence and the response electric quantity is achieved. According to the method, the prediction models suitable for the actual condition of a smart power grid of China are established, the scale of construction of renewable energy sources is reasonably planned, more efficient power utilization of users is facilitated, and reasonable arrangement of power supply resources of power enterprises is facilitated.

A multiobjective evolutionary algorithm unit feeds a set of parameters of an individual to a search history storage device in a fitness estimating unit and to an optimization target. The optimization target outputs a set of sampled values of fitnesses on the basis of the set of parameters of the individual. The search history storage device stores the set of parameters of the individual and a set of sampled values as a search history. The fitness estimating module computes a set of estimated values of true fitnesses on the basis of the search history stored in the search history storage device for output to the multiobjective evolutionary algorithm unit. The multiobjective evolutionary algorithm unit determines a Pareto-optimal population in accordance with a genetic algorithm on the basis of a plurality of sets of estimated values.

A hybrid evolutionary algorithm (“HEA”) technique is described for automatically calculating well and drainage locations in a field. The technique includes planning a set of wells on a static reservoir model using an automated well planner tool that designs realistic wells that satisfy drilling and construction constraints. A subset of these locations is then selected based on dynamic flow simulation using a cost function that maximizes recovery or economic benefit. In particular, a large population of candidate targets, drain holes and trajectories is initially created using fast calculation analysis tools of cost and value, and as the workflow proceeds, the population size is reduced in each successive operation, thereby facilitating use of increasingly sophisticated calculation analysis tools for economic valuation of the reservoir while reducing overall time required to obtain the result. In the final operation, only a small number of full reservoir simulations are required for the most promising FDPs.

The invention provides a multi-satellite earth-observation task scheduling and planning method and device.The method comprises the steps that an observation task and satellite resource data are read; the observation task is partitioned according to the satellite resource data, and an observation meta-task set is obtained; task planning is conducted on the observation meta-task set through an evolutionary algorithm, and a planning result is obtained; the planning result is decoded, and an observation sequence is obtained; in this way, when multiple satellites observe the earth, the characteristics such as intelligence and concurrency of the evolutionary algorithm are fully utilized, and the task scheduling problem of multiple observation satellites which need to work cooperatively is solved; a dynamic task planning method based on the evolutionary algorithm is utilized, a reasonable load scheduling scheme is made for an in-orbit operation satellite group, resource allocation of a satellite system is optimized, and satellite system resources are utilized fully and reasonably.

The invention discloses a system and a method for optimizing overall planning of distributed energy. The system comprises a matrix laboratory (MATLAB) optimization analysis part, and a Microsoft EXCEL system input part and a Microsoft EXCEL system output part which are combined with the MATLAB optimization analysis part. The optimization method comprises the following steps of: (1) predicting andanalyzing; (2) modeling and outputting an optimization variable, a performance parameter and a system power flow network chart by using a combined cooling heating and power (CCHP) energy system; (3) starting a scheme optimization process by using a CCHP energy overall planning optimization module; (4) verifying and evaluating an optimization scheme by using a system constraint subsystem; and (5) outputting an optimization scheme. Modeling description is effectively performed on thermal equipment in combination with a mathematical modeling measure, an overall evolutionary algorithm with high performance is optimized and microscopic configuration optimization, and annual operating strategy optimization are performed on the entire system; the system and the method support optimization of a simple cyclic energy system and a more complicated combined cyclic energy system; and the efficiency and economic benefit of the entire energy system are improved.

The invention discloses a multi-unmanned aerial vehicle auxiliary edge computing resource allocation method based on task prediction. The method comprises the following steps of: firstly, modeling a communication model, a computing model and an energy loss model in an unmanned aerial vehicle auxiliary edge computing unloading scene; modeling a system total energy consumption minimization problem of the unmanned aerial vehicle auxiliary edge computing unloading network into task predictable process of terminal devices; obtaining prediction model parameters of different terminal devices by adopting centralized training through accessing historical data of the terminal devices; obtaining a prediction task set of the next time slot by utilizing the prediction model based on the task information of the current access terminal devices; and based on the prediction task set, decomposing an original problem into an unmanned aerial vehicle deployment problem and a task scheduling problem for joint optimization. The response time delay and completion time delay of the task can be effectively reduced through the deep learning algorithm, so that the calculation energy consumption is reduced; anevolutionary algorithm is introduced to solve the problem of joint unmanned aerial vehicle deployment and task scheduling optimization, the hovering energy consumption of the unmanned aerial vehicleis greatly reduced, and the utilization rate of computing resources is increased.

The invention discloses a quick feedback analyzing system in a tunnel constructing process. The system adopts a scheme: understanding currently adopted designing construction parameters; establishing a tunnel excavation three-dimensional finite element numerical grid calculation model; acquiring surrounding rock layering and convergent displacement monitoring information after a tunnel is excavated; establishing a non-linear support vector machine model; fixing an anchoring parameter according to the actual construction parameter, and optimally identifying rock mechanic parameters by adoptinga differential optimization algorithm; optimizing the construction parameter of an anchoring scheme by adopting a differential evolution algorithm; and optimizing the rock mechanic parameters by calling the differential evolution and optimization algorithms to further solve the construction parameter of the anchoring scheme, and outputting the construction parameter of the optimized anchoring scheme as a construction scheme through a computer display screen to guide the constructors to construct. The quick feedback analyzing system ensures that the monitoring information is used for optimizing the anchoring parameter while being used for identifying the surrounding rock parameters, so that the dynamic information construction is improved to a level of quantitative analysis.

The present invention discloses systems and methods of conducting multi-objective evolutionary algorithm (MOEA) based engineering design optimization of a product (e.g., automobile, cellular phone, etc.). Particularly, the present invention discloses an archive configured for monitoring the progress and characterizing the performance of the MOEA based optimization. Further, an optimization performance indicator is created using the archive's update history. The optimization performance indicator is used as a metric of the current state of the optimization. Finally, a stopping or termination criterion for the MOEA based optimization is determined using a measurement derived from the optimization performance indicators. For example, a confirmation of a “knee” formation has developed in the optimization performance indicators. The optimization performance indicators include, but are not limited to, consolidation ratio, improvement ratio, hypervolume.

The invention relates to a clustered wireless sensor network cluster head election model based on an evolutionary game trust mechanism. The cluster head election model comprises three system models, namely, a wireless sensor network model, a JDL (Joint Director of Laboratories) model, and an evolutionary game model. According to the clustered wireless sensor network cluster head election model based on an evolutionary game trust mechanism provided by the invention, an improved JDL model and an evolutionary game algorithm are adopted, the dynamic characteristic of network communication and the time-effectiveness of trust are fully considered, an optimal cluster head is elected on the premise of considering trust factors such as communication, energy and data, and moreover, the computation overhead is optimized, and the energy of nodes is saved.

Roughly described, a data mining arrangement for developing high quality classifiers using an evolutionary algorithm, includes a plurality of “mid-chain” evolutionary coordinators, down-chain of a main (top-chain) evolutionary coordinator and up-chain of evolutionary engines. Multiple levels of mid-chain evolutionary coordinators can be used in a hierarchy, and the various branches of the hierarchy need not have equal length. Each evolutionary coordinator (other than the top-chain evolutionary coordinator) appears to its up-chain neighbor as if it were an evolutionary engine, though it does not actually perform any evolution itself. Similarly, each evolutionary coordinator (including the top-chain evolutionary coordinator) also appears to its down-chain neighbors as a top-chain evolutionary coordinator. Each mid-chain evolutionary coordinator maintains its own local candidate pool, reducing the load on the top-chain evolutionary coordinator pool, as well as reducing bandwidth requirements. Only the evolutionary engines perform actual testing of candidate individuals on training data.

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

The invention discloses a combined optimization method algorithm to an irregularly-arranged sub-array four-dimensional antenna array. The definition of information entropy is introduced into a four-dimensional array, the originally-complicated optimization problem is divided into two sub-problems, optimization is performed according to two steps, in the first step, an information entropy-based genetic algorithm is employed, the array topology structure with maximum information entropy value is optimized according to a sub-array arrangement algorithm, in the second step, information such as a static excitation phase of each sub-array of the sub-arrays, the closing continuous time of a switch and the initial closing time of the switch is optimized by a differential evolutionary algorithm according to the requirement of low sideband and low side lobe, so that the whole optimization problem can be more efficiently solved. The maximum innovation lies in that the intrinsic characteristic ofthe original optimization problem is dug, optimization is performed by combining the information entropy-based generic algorithm and the differential evolutionary algorithm, the complexity of the original optimization problem is reduced, T/R modules are saved by half, and meanwhile, the characteristics of low sideband and low side lobe under large-angle scanning are ensured.

Methods are described for near optimal Antenna and/or sensor selection via population-based probabilistic evolutionary algorithms such as estimation of distribution algorithm (EDA) and bio-inspired Optimization (BIO). The aspects of the invention includes a method for joint transmit and receive antenna selection using EDA; and an enhanced EDA, which uses cyclic shift register and biased estimation of distribution; and methods for joint transmit and receive antenna selection using improved population-based optimization. The proposed EDA-based and bio-inspired selection methods results in performances that are close to the ESA (exhaustive search algorithm) and yet impose mush less computational burden than ESA. Another advantage of our methods is that they can be easily implemented on parallel processors.