genetic algorithm

摘要： In optimization theory,the adaptive control of the optimization process is an important goal that people pursue.To solve this problem,this study introduces the idea of neutrosophic decision-making into classical heuristic algorithm,and proposes a novel neutrosophic adaptive clustering optimization thought,which is applied in a novel neutrosophic genetic algorithm(NGA),for example.The main feature of NGA is that the NGA treats the crossover effect as a neutrosophic fuzzy set,the variation ratio as a structural parameter,the crossover effect as a benefit parameter and the variation effect as a cost parameter,and then a neutrosophic fitness function value is created.Finally,a high order assignment problem in warehousemanagement is taken to illustrate the effectiveness of NGA.

摘要： In multi-component systems,the components are dependent,rather than degenerating independently,leading to changes inmaintenance schedules.In this situation,this study proposes a grouping dynamicmaintenance strategy.Considering the structure of multi-component systems,the maintenance strategy is determined according to the importance of the components.The strategy can minimize the expected depreciation cost of the system and divide the system into optimal groups that meet economic requirements.First,multi-component models are grouped.Then,a failure probability model of multi-component systems is established.The maintenance parameters in each maintenance cycle are updated according to the failure probability of the components.Second,the component importance indicator is introduced into the grouping model,and the optimization model,which aimed at a maximum economic profit,is established.A genetic algorithm is used to solve the non-deterministic polynomial(NP)-complete problem in the optimization model,and the optimal grouping is obtained through the initial grouping determined by random allocation.An 11-component series and parallel system is used to illustrate the effectiveness of the proposed strategy,and the influence of the system structure and the parameters on the maintenance strategy is discussed.

摘要： In this study,three computational approaches for the optimization of a thermal conduction problem are critically compared.These include a Direct Method(DM),a Genetic Algorithm(GA),and a Pattern Search(PS)technique.The optimization aims to minimize the maximum temperature of a hot medium(a medium with uniform heat generation)using a constant amount of high conductivity materials(playing the role of fixed factor constraining the considered problem).The principal goal of this paper is to determine the most efficient and fastest option among the considered ones.It is shown that the examined three methods approximately lead to the same result in terms of maximum tem-perature.However,when the number of optimization variables is low,the DM is the fastest one.An increment in the complexity of the design and the number of degrees of freedom(DOF)can make the DM impractical.Results also show that the PS algorithm becomes faster than the GA as the number of variables for the optimization rises.

摘要： Target detection is an important research content in the radar field.At present,efforts are being made to optimize the precision of detection information.In this paper,we use the high pulse repetition frequency(HPRF)transmission method and orthogonal biphase coded signals in each pulse to avoid velocity ambiguity and range ambiguity of radar detection.In addition,We also apply Walsh matrix and genetic algorithm(GA)to generate satisfying orthogonal biphase coded signals with low auto-correlation sidelobe peak and cross-correlation peak,which make the results more accurate.In a radar receiver,data rearrangement of echo signals is performed,and then pulse compression and moving target detection(MTD)are utilized to get the final velocity and range information of a target without velocity ambiguity and range ambiguity.Besides,a small transmitting pulse time width is adopted to reduce the working blind area,and two different high pulse repetition frequencies(HPRFs)are adopted to solve the problem of range eclipse.Simulation results finally prove the effectiveness and feasibility of the proposed method.

摘要： The joint location planning of charging/battery-swap facilities for electric vehicles is a complex problem.Considering the differences between these two modes of power replenishment,we constructed a joint location-planning model to minimize construction and operation costs,user costs,and user satisfaction-related penalty costs.We designed an improved genetic algorithm that changes the crossover rate using the fitness value,memorizes,and transfers excellent genes.In addition,the present model addresses the problem of“premature convergence”in conventional genetic algorithms.A simulated example revealed that our proposed model could provide a basis for optimized location planning of charging/battery-swapping facilities at different levels under different charging modes with an improved computing efficiency.The example also proved that meeting more demand for power supply of electric vehicles does not necessarily mean increasing the sites of charging/battery-swap stations.Instead,optimizing the level and location planning of charging/battery-swap stations can maximize the investment profit.The proposed model can provide a reference for the government and enterprises to better plan the location of charging/battery-swap facilities.Hence,it is of both theoretical and practical value.

摘要： Accurate prediction of shipmotion is very important for ensuringmarine safety,weapon control,and aircraft carrier landing,etc.Ship motion is a complex time-varying nonlinear process which is affected by many factors.Time series analysis method and many machine learning methods such as neural networks,support vector machines regression(SVR)have been widely used in ship motion predictions.However,these single models have certain limitations,so this paper adopts amulti-model prediction method.First,ensemble empirical mode decomposition(EEMD)is used to remove noise in ship motion data.Then the randomforest(RF)prediction model optimized by genetic algorithm(GA),back propagation neural network(BPNN)prediction model and SVR prediction model are respectively established,and the final prediction results are obtained by results of three models.And the weights coefficients are determined by the correlation coefficients,reducing the risk of prediction and improving the reliability.The experimental results show that the proposed combined model EEMD-GARF-BPNN-SVR is superior to the single predictive model and more reliable.The mean absolute percentage error(MAPE)of the proposed model is 0.84%,but the results of the single models are greater than 1%.

摘要： In this work, austenitic stainless steel screws employed in a locking compression plate for veterinarian use were investigated. These types of implants are widely utilized in bone fractures healing. Two surgical screws were extracted due to the observation of slight superficial red rust colorizing on one of the screw implants, visual evidence of probable screw rusting. From the same implant, another screw was extracted simultaneously without visual evidence of rusting. In order to characterize and analyze the different behavior of both screws, the chemical composition was characterized by atomic absorption and energy dispersive X-ray spectroscopy (EDS) coupled to a scanning electron microscope (SEM). Also, the screws were studied by metallography, optical microscopy (OM), Vickers microhardness tests, and SEM analysis. On the other hand, a prospection for alloy chemical composition limits of these types of implants was performed based on the Schaeffler-Delong diagram and the ASTM F-138 standard. To analyze the effect of the chemical composition, heat treatment, microstructure, pitting resistance equivalent number (PRE) and stacking fault energy (SFE), a genetic algorithm (GA) and an artificial neural network (ANN) were used. In accordance with the elemental analysis, the surgical screws do not fulfill the ranges of the chemical composition established by the ASTM F-138 standard. Furthermore, there were found differences between the microstructures of the screws. In regard to the prospection, the results of GA and ANN support the proposed chemical composition region on the Schaeffler-Delong diagram. The corrosion failure was associated with severe plastic deformation and the presence of precipitates. The proposal can minimize the cause of failures in these types of austenitic stainless steel implants.

摘要： In this paper Genetic Algorithm has been integrated with Fouquet modal analysis to optimize radiation pattern of coupled periodic antenna. Floquet analysis is used with MoM-GEC (Moment-Generalized Equivalent Circuit) method to study a finite periodic array with uniform amplitude and linear phase distribution. This method is very advantageous for studying large antenna array since it considerably reduces the computation time and the number of operations. In this way, Genetic algorithm is introduced and combined with Floquet analysis to optimize the radiation pattern distribution of this coupled periodic antenna. The goal of the optimization is to provide a better radiation characteristic for the coupled periodic antenna with maximum side lobe level reduction.

摘要： Both high-dense wireless connectivity and ultra-huge network capacity are main challenges of next generation broadband networks.As one of its key promising technologies,non-orthogonal multi-ple access(NOMA)scheme can solve those challenges and meet those needs to some extent,in the way that different user equipments(UEs)multiplex on the same resource.Researchers around the world have presented numerous NOMA solutions.Among those,sparse code multiple access(SC-MA)technology is a typical NOMA solution.It supports scheduled access and random access which can be called granted access and grant-free access respectively.But resources allocated to granted UEs and grant-free UEs are strictly separated.In order to improve resource utilization,a hybrid non-orthogonal multiple access scheme is proposed.It allows granted UEs and grant-free UEs sharing the same resource unit in terms of fine-grained integration.On the basis,a resource allocation method is further brought forward based on genetic algorithm.It optimizes resource allocation of all UEs by mapping resource distribution issue to an optimization problem.Comparing throughputs of four meth-ods,simulation results demonstrate the proposed genetic algorithm has better throughput gain.

摘要： A scanning and uniform array architecture with large spacing,low complexity and high scalability is presented for high integration massive array applications.It is constructed by offset phase center elements arranged in a uniform and regular way,but its spacing can be larger than that of traditional arrays.An ideal model of the offset phase center element is established and its far-field distribution is derived.To suppress grating lobes,the phase center of any element is designed to be movable without changing its physical position.Using genetic algorithm(GA),a new constraint condition limiting the number of phase center changes is proposed to solve the objective function of the minimum values of grating lobes(GLs)and side lobes(SLs).It is shown that the optimal results can be achieved by two changes of phase centers.A multimode circular patch is developed and designed,and characteristics of the offset phase center are analyzed and verified.A prototype array of 12×12 offset phase center elements is implemented based on multi-mode circular patches.Full wave simulation results of radiation patterns show that the level of grating lobes is suppressed at least 7dB with 1.12λ spacing,while the scanning angle is 20°.

摘要： Evolution algorithm is a kind of simple and effective global optimization algorithm, which hasbeen attracted much increasing interest from academia applications to various fields successfully. This paperdescribes the basic idea of the evolutionary algorithm；and then analyses the thinking and framework of solving problems basing on the evolutionary algorithm, and introduces some key technologies of evolution algorithm, at last, the paper indicates the some actual applications of evolution and points out the direction of future research.

摘要： Evolution algorithm is a kind of simple and effective global optimization algorithm, which hasbeen attracted much increasing interest from academia applications to various fields successfully. This paperdescribes the basic idea of the evolutionary algorithm；and then analyses the thinking and framework of solving problems basing on the evolutionary algorithm, and introduces some key technologies of evolution algorithm, at last, the paper indicates the some actual applications of evolution and points out the direction of future research.

摘要： Evolution algorithm is a kind of simple and effective global optimization algorithm, which hasbeen attracted much increasing interest from academia applications to various fields successfully. This paperdescribes the basic idea of the evolutionary algorithm；and then analyses the thinking and framework of solving problems basing on the evolutionary algorithm, and introduces some key technologies of evolution algorithm, at last, the paper indicates the some actual applications of evolution and points out the direction of future research.

摘要： Evolution algorithm is a kind of simple and effective global optimization algorithm, which hasbeen attracted much increasing interest from academia applications to various fields successfully. This paperdescribes the basic idea of the evolutionary algorithm；and then analyses the thinking and framework of solving problems basing on the evolutionary algorithm, and introduces some key technologies of evolution algorithm, at last, the paper indicates the some actual applications of evolution and points out the direction of future research.

摘要： With the increasingly prominent issue of network security, Intrusion Detection System as a logicaladdition to traditional security technology, have paid more and more attention. In this paper, machine learningin Intrusion Detection System with a brief introduction and overview, described the general structure Intrusion Detection System based on machine learning, and introduced the five specific machine learning methodsin Intrusion Detection System, the last with the development trend of network attacks, described based on thedevelopment of Intrusion Detection System based on machine learning.

摘要： With the increasingly prominent issue of network security, Intrusion Detection System as a logicaladdition to traditional security technology, have paid more and more attention. In this paper, machine learningin Intrusion Detection System with a brief introduction and overview, described the general structure Intrusion Detection System based on machine learning, and introduced the five specific machine learning methodsin Intrusion Detection System, the last with the development trend of network attacks, described based on thedevelopment of Intrusion Detection System based on machine learning.

摘要： With the increasingly prominent issue of network security, Intrusion Detection System as a logicaladdition to traditional security technology, have paid more and more attention. In this paper, machine learningin Intrusion Detection System with a brief introduction and overview, described the general structure Intrusion Detection System based on machine learning, and introduced the five specific machine learning methodsin Intrusion Detection System, the last with the development trend of network attacks, described based on thedevelopment of Intrusion Detection System based on machine learning.

摘要： With the increasingly prominent issue of network security, Intrusion Detection System as a logicaladdition to traditional security technology, have paid more and more attention. In this paper, machine learningin Intrusion Detection System with a brief introduction and overview, described the general structure Intrusion Detection System based on machine learning, and introduced the five specific machine learning methodsin Intrusion Detection System, the last with the development trend of network attacks, described based on thedevelopment of Intrusion Detection System based on machine learning.

摘要： With the increasingly prominent issue of network security, Intrusion Detection System as a logicaladdition to traditional security technology, have paid more and more attention. In this paper, machine learningin Intrusion Detection System with a brief introduction and overview, described the general structure Intrusion Detection System based on machine learning, and introduced the five specific machine learning methodsin Intrusion Detection System, the last with the development trend of network attacks, described based on thedevelopment of Intrusion Detection System based on machine learning.

摘要： Online examination system is built on Internet applications, aimed at achieving information management of examinations. Online examination system in the generation of test papers, the submission and approval can be done automatically on the network. This paper proposes a new heuristic genetic test paper algorithm, and the proposed algorithm is applied to the online examination system. From the experimental results,the proposed algorithm has achieved satisfactory results.