In medical science for envisaging human body’s phenomenal structure a major part has been driven by image processing techniques.Major objective of this work is to detect of cerebral atherosclerosis for image segmenta...In medical science for envisaging human body’s phenomenal structure a major part has been driven by image processing techniques.Major objective of this work is to detect of cerebral atherosclerosis for image segmentation applica-tion.Detection of some abnormal structures in human body has become a difficult task to complete with some simple images.For expounding and distinguishing neural architecture of human brain in an effective manner,MRI(Magnetic Reso-nance Imaging)is one of the most suitable and significant technique.Here we work on detection of Cerebral Atherosclerosis from MRI images of patients.Cer-ebral Atherosclerosis is a cerebral vascular disease causes narrowing of the arteries due to buildup of fatty plaque inside the blood vessels of the brain.It leads to Ischemic stroke if not diagnosed early.Stroke affects majorly old age people and percentage of affected women is more compared to men.Results:Preproces-sing is done by using alpha trimmed meanfilter which is used to remove noise and also it enhances the image.Segmentation of cerebral atherosclerosis is done by using K-means clustering,Contextual clustering,and proposed Hybrid algo-rithm.Various parameters like Correlation,Pixel density,energy is determined and from the analysis of parameters it is determined that proposed Hybrid algo-rithm is efficient.展开更多
With the high proportion of new energy access,the traditional fault self-healing mechanism of the distribution network is challenged.Aiming at the demand for fast recovery of new distribution network faults,this paper...With the high proportion of new energy access,the traditional fault self-healing mechanism of the distribution network is challenged.Aiming at the demand for fast recovery of new distribution network faults,this paper proposes a fault self-healing cooperative strategy for the new energy distribution network based on an improved ant colony-genetic hybrid algorithm.Firstly,the graph theory adjacency matrix is used to characterize the topology of the distribution network,and the dynamic positioning of new energy nodes is realized.Secondly,based on the output model and load characteristic model of wind,photovoltaic,and energy storage,a two-layer cooperative self-healing model of the distribution network is constructed.The upper layer is based on the improved depth-breadth hybrid search(DFS-BFS)to divide the island,with the maximum weight load recovery and the minimum number of switching actions as the goal,combined with the load priority to dynamically restore the key load.The lower layer uses the improved ant colony-genetic hybrid algorithm to solve the fault recovery path with the minimum total power loss load and the minimum network loss as the goal,generate the optimal switching sequence,and verify the power flow constraints.Finally,the simulation results based on the IEEE 33-bus system show that the proposed method can guarantee the power supply of key loads in the distribution network with high-tech energy penetration,restore the power supply of more load nodes with the least switching operation,and effectively reduce the line loss,which verifies the effectiveness and superiority of the method.展开更多
The surface injection and production system(SIPS)is a critical component for effective injection and production processes in underground natural gas storage.As a vital channel,the rational design of the surface inject...The surface injection and production system(SIPS)is a critical component for effective injection and production processes in underground natural gas storage.As a vital channel,the rational design of the surface injection and production(SIP)pipeline significantly impacts efficiency.This paper focuses on the SIP pipeline and aims to minimize the investment costs of surface projects.An optimization model under harmonized injection and production conditions was constructed to transform the optimization problem of the SIP pipeline design parameters into a detailed analysis of the injection condition model and the production condition model.This paper proposes a hybrid genetic algorithm generalized reduced gradient(HGA-GRG)method,and compares it with the traditional genetic algorithm(GA)in a practical case study.The HGA-GRG demonstrated significant advantages in optimization outcomes,reducing the initial cost by 345.371×10^(4) CNY compared to the GA,validating the effectiveness of the model.By adjusting algorithm parameters,the optimal iterative results of the HGA-GRG were obtained,providing new research insights for the optimal design of a SIPS.展开更多
The intelligent optimization of a multi-objective evolutionary algorithm is combined with a gradient algorithm. The hybrid multi-objective gradient algorithm is framed by the real number. Test functions are used to an...The intelligent optimization of a multi-objective evolutionary algorithm is combined with a gradient algorithm. The hybrid multi-objective gradient algorithm is framed by the real number. Test functions are used to analyze the efficiency of the algorithm. In the simulation case of the water phantom, the algorithm is applied to an inverse planning process of intensity modulated radiation treatment （IMRT）. The objective functions of planning target volume （PTV） and normal tissue （NT） are based on the average dose distribution. The obtained intensity profile shows that the hybrid multi-objective gradient algorithm saves the computational time and has good accuracy, thus meeting the requirements of practical applications.展开更多
The classical discrete element approach(DEM)based on Newtonian dynamics can be divided into two major groups,event-driven methods(EDM)and timedriven methods(TDM).Generally speaking,TDM simulations are suited for cases...The classical discrete element approach(DEM)based on Newtonian dynamics can be divided into two major groups,event-driven methods(EDM)and timedriven methods(TDM).Generally speaking,TDM simulations are suited for cases with high volume fractions where there are collisions between multiple objects.EDM simulations are suited for cases with low volume fractions from the viewpoint of CPU time.A method combining EDM and TDM called Hybrid Algorithm of event-driven and time-driven methods(HAET)is presented in this paper.The HAET method employs TDM for the areas with high volume fractions and EDM for the remaining areas with low volume fractions.It can decrease the CPU time for simulating granular flows with strongly non-uniform volume fractions.In addition,a modified EDM algorithm using a constant time as the lower time step limit is presented.Finally,an example is presented to demonstrate the hybrid algorithm.展开更多
In order to analyze the influence rule of experimental parameters on the energy-absorption characteristics and effectively forecast energy-absorption characteristic of thin-walled structure, the forecast model of GA-B...In order to analyze the influence rule of experimental parameters on the energy-absorption characteristics and effectively forecast energy-absorption characteristic of thin-walled structure, the forecast model of GA-BP hybrid algorithm was presented by uniting respective applicability of back-propagation artificial neural network （BP-ANN） and genetic algorithm （GA）. The detailed process was as follows. Firstly, the GA trained the best weights and thresholds as the initial values of BP-ANN to initialize the neural network. Then, the BP-ANN after initialization was trained until the errors converged to the required precision. Finally, the network model, which met the requirements after being examined by the test samples, was applied to energy-absorption forecast of thin-walled cylindrical structure impacting. After example analysis, the GA-BP network model was trained until getting the desired network error only by 46 steps, while the single BP-ANN model achieved the same network error by 992 steps, which obviously shows that the GA-BP hybrid algorithm has faster convergence rate. The average relative forecast error （ARE） of the SEA predictive results obtained by GA-BP hybrid algorithm is 1.543%, while the ARE of the SEA predictive results obtained by BP-ANN is 2.950%, which clearly indicates that the forecast precision of the GA-BP hybrid algorithm is higher than that of the BP-ANN.展开更多
Vibration dynamic characteristics have been a major issue in the modeling and mechanical analysis of large hydro generators. An algorithm is developed for identifying vibration dynamic characteristics by means of hybr...Vibration dynamic characteristics have been a major issue in the modeling and mechanical analysis of large hydro generators. An algorithm is developed for identifying vibration dynamic characteristics by means of hybrid genetic algorithm. From the measured dynamic responses of a hydro generator, an appropriate estimation algorithm is needed to identify the loading parameters, including the main frequencies and amplitudes of vibrating forces. In order to identify parameters in an efficient and robust manner, an optimization method is proposed that combines genetic algorithm with simulated annealing and elitist strategy. The hybrid genetic algorithm is then used to tackle an ill-posed problem of parameter identification, in which the effectiveness of the proposed optimization method is confirmed by its comparison with actual observation data.展开更多
This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Op...This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Optimization(SFO)algorithm.The primary objective is to address multi-objective optimization challenges within mechanical engineering,with a specific emphasis on planetary gearbox optimization.The algorithm is equipped with the ability to dynamically select the optimal mutation operator,contingent upon an adaptive normalized population spacing parameter.The efficacy of HMODESFO has been substantiated through rigorous validation against estab-lished industry benchmarks,including a suite of Zitzler-Deb-Thiele(ZDT)and Zeb-Thiele-Laumanns-Zitzler(DTLZ)problems,where it exhibited superior performance.The outcomes underscore the algorithm’s markedly enhanced optimization capabilities relative to existing methods,particularly in tackling highly intricate multi-objective planetary gearbox optimization problems.Additionally,the performance of HMODESFO is evaluated against selected well-known mechanical engineering test problems,further accentuating its adeptness in resolving complex optimization challenges within this domain.展开更多
In this study,a novel hybrid Water Cycle Moth-Flame Optimization(WCMFO)algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance(MR)image slices.WCMFO constitutes a hybrid betwee...In this study,a novel hybrid Water Cycle Moth-Flame Optimization(WCMFO)algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance(MR)image slices.WCMFO constitutes a hybrid between the two techniques,comprising the water cycle and moth-flame optimization algorithms.The optimal thresholds are obtained by maximizing the between class variance(Otsu’s function)of the image.To test the performance of threshold searching process,the proposed algorithm has been evaluated on standard benchmark of ten axial T2-weighted brain MR images for image segmentation.The experimental outcomes infer that it produces better optimal threshold values at a greater and quicker convergence rate.In contrast to other state-of-the-art methods,namely Adaptive Wind Driven Optimization(AWDO),Adaptive Bacterial Foraging(ABF)and Particle Swarm Optimization(PSO),the proposed algorithm has been found to be better at producing the best objective function,Peak Signal-to-Noise Ratio(PSNR),Standard Deviation(STD)and lower computational time values.Further,it was observed thatthe segmented image gives greater detail when the threshold level increases.Moreover,the statistical test result confirms that the best and mean values are almost zero and the average difference between best and mean value 1.86 is obtained through the 30 executions of the proposed algorithm.Thus,these images will lead to better segments of gray,white and cerebrospinal fluid that enable better clinical choices and diagnoses using a proposed algorithm.展开更多
Conventional design of pier structures is based on the assumption of fully rigid joints. In practice, the real connections are semi-rigid that cause changes in dynamic characteristics. In this study, quality of the jo...Conventional design of pier structures is based on the assumption of fully rigid joints. In practice, the real connections are semi-rigid that cause changes in dynamic characteristics. In this study, quality of the joints is investigated by considering changes in natural frequencies. For this purpose, numerical and experimental modal analyses are carried out on related physical model of a pier type structure. When numerical results are evaluated,natural frequencies generally do not match the expected experimental results. Uncertainties in different aspects of engineering problems are always a challenge for researchers. The numerical models which are constructed on the basis of highly idealized scheme may not be able to represent all of the physical aspects of the physical one. For this study, determination of percentage of semi-rigid joints is considered as an optimization problem based on the numerical and experimental frequencies. Probabilistic sensitivity analysis is also used to determine the search space.A new technique of optimization problem is solved by a combination of smart particle swarm optimization(PSO)and genetic algorithms, and a complicated and efficient system for model updating process is introduced. It is observed that the hybrid PSO-Genetic algorithm is applicable and appropriate in model updating process. It performs better than PSO algorithm, considering the good agreement between theoretical frequencies and experimental ones,before and after model updating.展开更多
Although the phase-shift seismic processing method has characteristics of high accuracy, good stability, high efficiency, and high-dip imaging, it is not able to adapt to strong lateral velocity variation. To overcome...Although the phase-shift seismic processing method has characteristics of high accuracy, good stability, high efficiency, and high-dip imaging, it is not able to adapt to strong lateral velocity variation. To overcome this defect, a finite-difference method in the frequency-space domain is introduced in the migration process, because it can adapt to strong lateral velocity variation and the coefficient is optimized by a hybrid genetic and simulated annealing algorithm. The two measures improve the precision of the approximation dispersion equation. Thus, the imaging effect is improved for areas of high-dip structure and strong lateral velocity variation. The migration imaging of a 2-D SEG/EAGE salt dome model proves that a better imaging effect in these areas is achieved by optimized phase-shift migration operator plus a finite-difference method based on a hybrid genetic and simulated annealing algorithm. The method proposed in this paper is better than conventional methods in imaging of areas of high-dip angle and strong lateral velocity variation.展开更多
Aerodynamic parameters are important factors that affect projectile flight movement. To obtain accurate aerodynamic parameters, a hybrid genetic algorithm is proposed to identify and optimize the aerodynamic parameter...Aerodynamic parameters are important factors that affect projectile flight movement. To obtain accurate aerodynamic parameters, a hybrid genetic algorithm is proposed to identify and optimize the aerodynamic parameters of projectile. By combining the traditional simulated annealing method that is easy to fall into local optimum solution but hard to get global parameters with the genetic algorithm that has good global optimization ability but slow local optimization ability, the hybrid genetic algo- rithm makes full use of the advantages of the two algorithms for the optimization of projectile aerodynamic parameters. The simulation results show that the hybrid genetic algorithm is better than a single algorithm.展开更多
The genetic/gradient-based hybrid algorithm is introduced and used in the design studies of aeroelastic optimization of large aircraft wings to attain skin distribution,stiffness distribution and design sensitivity.Th...The genetic/gradient-based hybrid algorithm is introduced and used in the design studies of aeroelastic optimization of large aircraft wings to attain skin distribution,stiffness distribution and design sensitivity.The program of genetic algorithm is developed by the authors while the gradient-based algorithm borrows from the modified method for feasible direction in MSC/NASTRAN software.In the hybrid algorithm,the genetic algorithm is used to perform global search to avoid to fall into local optima,and then the excellent individuals of every generation optimized by the genetic algorithm are further fine-tuned by the modified method for feasible direction to attain the local optima and hence to get global optima.Moreover,the application effects of hybrid genetic algorithm in aeroelastic multidisciplinary design optimization of large aircraft wing are discussed,which satisfy multiple constraints of strength,displacement,aileron efficiency,and flutter speed.The application results show that the genetic/gradient-based hybrid algorithm is available for aeroelastic optimization of large aircraft wings in initial design phase as well as detailed design phase,and the optimization results are very consistent.Therefore,the design modifications can be decreased using the genetic/gradient-based hybrid algorithm.展开更多
The gear transmission system has been widely applied in mechanical systems,and many high-performance applications of these systems require low weight.With the aid of establishing the optimization model of the gear tra...The gear transmission system has been widely applied in mechanical systems,and many high-performance applications of these systems require low weight.With the aid of establishing the optimization model of the gear transmission system that consists of an objective function and some constraints(for example,the bending stress,the contact stress,the torsional strength,etc.),the optimal weight design of the gear transmission system can be transformed into the optimization problem for the objective function under the constraints.Moreover,both the shaft and the gear of the gear transmission system are considered simultaneously in our design.The hybrid Taguchi-genetic algorithm(HTGA)is employed to find the optimal design variables and the optimal weight of the system.An illustrated example for the single spur gear reducer is given to show that the optimal weight design problem can be successfully solved using the proposed design scheme.It also proves the high efficiency and feasibility of the algorithm in the gear design.展开更多
Accurate prediction of the movement trajectory of sea surface targets holds significant importance in achieving an advantageous position in the sea battle field.This prediction plays a crucial role in ensuring securit...Accurate prediction of the movement trajectory of sea surface targets holds significant importance in achieving an advantageous position in the sea battle field.This prediction plays a crucial role in ensuring security defense and confrontation,and is essential for effective deployment of military strategy.Accurately predicting the trajectory of sea surface targets using AIS(Automatic Identification System)information is crucial for security defense and confrontation,and holds significant importance for military strategy deployment.In response to the problem of insufficient accuracy in ship trajectory prediction,this study proposes a hybrid genetic algorithm to optimize the Long Short-Term Memory(LSTM)algorithm.The HGA-LSTM algorithm is proposed for ship trajectory prediction.It can converge faster and obtain better parameter solutions,thereby improving the effectiveness of ship trajectory prediction.Compared to traditional LSTM and GA-LSTM algorithms,experimental results demonstrate that this algorithm outperforms them in both single-step and multi-step prediction.展开更多
Previous studies about optimizing earthquake structural energy dissipation systems indicated that most existing techniques employ merely one or a few parameters as design variables in the optimization process,and ther...Previous studies about optimizing earthquake structural energy dissipation systems indicated that most existing techniques employ merely one or a few parameters as design variables in the optimization process,and thereby are only applicable only to simple,single,or multiple degree-of-freedom structures.The current approaches to optimization procedures take a specific damper with its properties and observe the effect of applying time history data to the building;however,there are many different dampers and isolators that can be used.Furthermore,there is a lack of studies regarding the optimum location for various viscous and wall dampers.The main aim of this study is hybridization of the particle swarm optimization(PSO) and gravitational search algorithm(GSA) to optimize the performance of earthquake energy dissipation systems(i.e.,damper devices) simultaneously with optimizing the characteristics of the structure.Four types of structural dampers device are considered in this study:(ⅰ) variable stiffness bracing(VSB) system,(ⅱ) rubber wall damper(RWD),(ⅲ) nonlinear conical spring bracing(NCSB) device,(iv) and multi-action stiffener(MAS) device.Since many parameters may affect the design of seismic resistant structures,this study proposes a hybrid of PSO and GSA to develop a hybrid,multi-objective optimization method to resolve the aforementioned problems.The characteristics of the above-mentioned damper devices as well as the section size for structural beams and columns are considered as variables for development of the PSO-GSA optimization algorithm to minimize structural seismic response in terms of nodal displacement(in three directions) as well as plastic hinge formation in structural members simultaneously with the weight of the structure.After that,the optimization algorithm is implemented to identify the best position of the damper device in the structural frame to have the maximum effect and minimize the seismic structure response.To examine the performance of the proposed PSO-GSA optimization method,it has been applied to a three-story reinforced structure equipped with a seismic damper device.The results revealed that the method successfully optimized the earthquake energy dissipation systems and reduced the effects of earthquakes on structures,which significantly increase the building’s stability and safety during seismic excitation.The analysis results showed a reduction in the seismic response of the structure regarding the formation of plastic hinges in structural members as well as the displacement of each story to approximately 99.63%,60.5%,79.13% and 57.42% for the VSB device,RWD,NCSB device,and MAS device,respectively.This shows that using the PSO-GSA optimization algorithm and optimized damper devices in the structure resulted in no structural damage due to earthquake vibration.展开更多
Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on f...Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on forecasting daily stock market returns,especially when using powerful machine learning techniques,such as deep neural networks(DNNs),to perform the analyses.DNNs employ various deep learning algorithms based on the combination of network structure,activation function,and model parameters,with their performance depending on the format of the data representation.This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF(ticker symbol:SPY)based on 60 financial and economic features.DNNs and traditional artificial neural networks(ANNs)are then deployed over the entire preprocessed but untransformed dataset,along with two datasets transformed via principal component analysis(PCA),to predict the daily direction of future stock market index returns.While controlling for overfitting,a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000.Moreover,a set of hypothesis testing procedures are implemented on the classification,and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset,as well as several other hybrid machine learning algorithms.In addition,the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested,including in a comparison against two standard benchmarks.展开更多
The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this wor...The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.展开更多
Secure storage yard is one of the optimal core goals of container transportation;thus,making the necessary storage arrangements has become the most crucial part of the container terminal management systems（CTMS）.Thi...Secure storage yard is one of the optimal core goals of container transportation;thus,making the necessary storage arrangements has become the most crucial part of the container terminal management systems（CTMS）.This paper investigates a random hybrid stacking algorithm（RHSA） for outbound containers that randomly enter the yard.In the first stage of RHSA,the distribution among blocks was analyzed with respect to the utilization ratio.In the second stage,the optimization of bay configuration was carried out by using the hybrid genetic algorithm.Moreover,an experiment was performed to test the RHSA.The results show that the explored algorithm is useful to increase the efficiency.展开更多
For massive order allocation problem of the third party logistics （TPL） in ecommerce, this paper proposes a general order allocation model based on cloud architecture and hybrid genetic algorithm （GA）, implementin...For massive order allocation problem of the third party logistics （TPL） in ecommerce, this paper proposes a general order allocation model based on cloud architecture and hybrid genetic algorithm （GA）, implementing cloud deployable MapReduce （MR） code to parallelize allocation process, using heuristic rule to fix illegal chromosome during encoding process and adopting mixed integer programming （MIP） as fitness flmction to guarantee rationality of chromosome fitness. The simulation experiment shows that in mass processing of orders, the model performance in a multi-server cluster environment is remarkable superior to that in stand-alone environment. This model can be directly applied to cloud based logistics information platform （LIP） in near future, implementing fast auto-allocation for massive concurrent orders, with great application value.展开更多
文摘In medical science for envisaging human body’s phenomenal structure a major part has been driven by image processing techniques.Major objective of this work is to detect of cerebral atherosclerosis for image segmentation applica-tion.Detection of some abnormal structures in human body has become a difficult task to complete with some simple images.For expounding and distinguishing neural architecture of human brain in an effective manner,MRI(Magnetic Reso-nance Imaging)is one of the most suitable and significant technique.Here we work on detection of Cerebral Atherosclerosis from MRI images of patients.Cer-ebral Atherosclerosis is a cerebral vascular disease causes narrowing of the arteries due to buildup of fatty plaque inside the blood vessels of the brain.It leads to Ischemic stroke if not diagnosed early.Stroke affects majorly old age people and percentage of affected women is more compared to men.Results:Preproces-sing is done by using alpha trimmed meanfilter which is used to remove noise and also it enhances the image.Segmentation of cerebral atherosclerosis is done by using K-means clustering,Contextual clustering,and proposed Hybrid algo-rithm.Various parameters like Correlation,Pixel density,energy is determined and from the analysis of parameters it is determined that proposed Hybrid algo-rithm is efficient.
基金supported by the Installation of OCS Distribution Network Program Control 2.0 and Other Functions for Dongguan Power Supply Bureau of Guangdong Power Grid Co.,Ltd.(No.:031900GS62220049).
文摘With the high proportion of new energy access,the traditional fault self-healing mechanism of the distribution network is challenged.Aiming at the demand for fast recovery of new distribution network faults,this paper proposes a fault self-healing cooperative strategy for the new energy distribution network based on an improved ant colony-genetic hybrid algorithm.Firstly,the graph theory adjacency matrix is used to characterize the topology of the distribution network,and the dynamic positioning of new energy nodes is realized.Secondly,based on the output model and load characteristic model of wind,photovoltaic,and energy storage,a two-layer cooperative self-healing model of the distribution network is constructed.The upper layer is based on the improved depth-breadth hybrid search(DFS-BFS)to divide the island,with the maximum weight load recovery and the minimum number of switching actions as the goal,combined with the load priority to dynamically restore the key load.The lower layer uses the improved ant colony-genetic hybrid algorithm to solve the fault recovery path with the minimum total power loss load and the minimum network loss as the goal,generate the optimal switching sequence,and verify the power flow constraints.Finally,the simulation results based on the IEEE 33-bus system show that the proposed method can guarantee the power supply of key loads in the distribution network with high-tech energy penetration,restore the power supply of more load nodes with the least switching operation,and effectively reduce the line loss,which verifies the effectiveness and superiority of the method.
基金the National Natural Science Foundation of China,grant numbers 51704253 and 52474084.
文摘The surface injection and production system(SIPS)is a critical component for effective injection and production processes in underground natural gas storage.As a vital channel,the rational design of the surface injection and production(SIP)pipeline significantly impacts efficiency.This paper focuses on the SIP pipeline and aims to minimize the investment costs of surface projects.An optimization model under harmonized injection and production conditions was constructed to transform the optimization problem of the SIP pipeline design parameters into a detailed analysis of the injection condition model and the production condition model.This paper proposes a hybrid genetic algorithm generalized reduced gradient(HGA-GRG)method,and compares it with the traditional genetic algorithm(GA)in a practical case study.The HGA-GRG demonstrated significant advantages in optimization outcomes,reducing the initial cost by 345.371×10^(4) CNY compared to the GA,validating the effectiveness of the model.By adjusting algorithm parameters,the optimal iterative results of the HGA-GRG were obtained,providing new research insights for the optimal design of a SIPS.
基金Supported by the National Basic Research Program of China ("973" Program)the National Natural Science Foundation of China (60872112, 10805012)+1 种基金the Natural Science Foundation of Zhejiang Province(Z207588)the College Science Research Project of Anhui Province (KJ2008B268)~~
文摘The intelligent optimization of a multi-objective evolutionary algorithm is combined with a gradient algorithm. The hybrid multi-objective gradient algorithm is framed by the real number. Test functions are used to analyze the efficiency of the algorithm. In the simulation case of the water phantom, the algorithm is applied to an inverse planning process of intensity modulated radiation treatment （IMRT）. The objective functions of planning target volume （PTV） and normal tissue （NT） are based on the average dose distribution. The obtained intensity profile shows that the hybrid multi-objective gradient algorithm saves the computational time and has good accuracy, thus meeting the requirements of practical applications.
基金supported by a grant from Department of Energy and Process Engineering,Norwegian University of Science and Technology,Institute for Energy Technology(IFE)and SINTEF through the FACE(Multiphase Flow Assurance Innovation Center)project.
文摘The classical discrete element approach(DEM)based on Newtonian dynamics can be divided into two major groups,event-driven methods(EDM)and timedriven methods(TDM).Generally speaking,TDM simulations are suited for cases with high volume fractions where there are collisions between multiple objects.EDM simulations are suited for cases with low volume fractions from the viewpoint of CPU time.A method combining EDM and TDM called Hybrid Algorithm of event-driven and time-driven methods(HAET)is presented in this paper.The HAET method employs TDM for the areas with high volume fractions and EDM for the remaining areas with low volume fractions.It can decrease the CPU time for simulating granular flows with strongly non-uniform volume fractions.In addition,a modified EDM algorithm using a constant time as the lower time step limit is presented.Finally,an example is presented to demonstrate the hybrid algorithm.
基金Project(50175110) supported by the National Natural Science Foundation of ChinaProject(2009bsxt019) supported by the Graduate Degree Thesis Innovation Foundation of Central South University, China
文摘In order to analyze the influence rule of experimental parameters on the energy-absorption characteristics and effectively forecast energy-absorption characteristic of thin-walled structure, the forecast model of GA-BP hybrid algorithm was presented by uniting respective applicability of back-propagation artificial neural network （BP-ANN） and genetic algorithm （GA）. The detailed process was as follows. Firstly, the GA trained the best weights and thresholds as the initial values of BP-ANN to initialize the neural network. Then, the BP-ANN after initialization was trained until the errors converged to the required precision. Finally, the network model, which met the requirements after being examined by the test samples, was applied to energy-absorption forecast of thin-walled cylindrical structure impacting. After example analysis, the GA-BP network model was trained until getting the desired network error only by 46 steps, while the single BP-ANN model achieved the same network error by 992 steps, which obviously shows that the GA-BP hybrid algorithm has faster convergence rate. The average relative forecast error （ARE） of the SEA predictive results obtained by GA-BP hybrid algorithm is 1.543%, while the ARE of the SEA predictive results obtained by BP-ANN is 2.950%, which clearly indicates that the forecast precision of the GA-BP hybrid algorithm is higher than that of the BP-ANN.
基金The project supported by the National Natural Science Foundation of China (10472025)
文摘Vibration dynamic characteristics have been a major issue in the modeling and mechanical analysis of large hydro generators. An algorithm is developed for identifying vibration dynamic characteristics by means of hybrid genetic algorithm. From the measured dynamic responses of a hydro generator, an appropriate estimation algorithm is needed to identify the loading parameters, including the main frequencies and amplitudes of vibrating forces. In order to identify parameters in an efficient and robust manner, an optimization method is proposed that combines genetic algorithm with simulated annealing and elitist strategy. The hybrid genetic algorithm is then used to tackle an ill-posed problem of parameter identification, in which the effectiveness of the proposed optimization method is confirmed by its comparison with actual observation data.
基金supported by the Serbian Ministry of Education and Science under Grant No.TR35006 and COST Action:CA23155—A Pan-European Network of Ocean Tribology(OTC)The research of B.Rosic and M.Rosic was supported by the Serbian Ministry of Education and Science under Grant TR35029.
文摘This paper introduces a hybrid multi-objective optimization algorithm,designated HMODESFO,which amalgamates the exploratory prowess of Differential Evolution(DE)with the rapid convergence attributes of the Sailfish Optimization(SFO)algorithm.The primary objective is to address multi-objective optimization challenges within mechanical engineering,with a specific emphasis on planetary gearbox optimization.The algorithm is equipped with the ability to dynamically select the optimal mutation operator,contingent upon an adaptive normalized population spacing parameter.The efficacy of HMODESFO has been substantiated through rigorous validation against estab-lished industry benchmarks,including a suite of Zitzler-Deb-Thiele(ZDT)and Zeb-Thiele-Laumanns-Zitzler(DTLZ)problems,where it exhibited superior performance.The outcomes underscore the algorithm’s markedly enhanced optimization capabilities relative to existing methods,particularly in tackling highly intricate multi-objective planetary gearbox optimization problems.Additionally,the performance of HMODESFO is evaluated against selected well-known mechanical engineering test problems,further accentuating its adeptness in resolving complex optimization challenges within this domain.
文摘In this study,a novel hybrid Water Cycle Moth-Flame Optimization(WCMFO)algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance(MR)image slices.WCMFO constitutes a hybrid between the two techniques,comprising the water cycle and moth-flame optimization algorithms.The optimal thresholds are obtained by maximizing the between class variance(Otsu’s function)of the image.To test the performance of threshold searching process,the proposed algorithm has been evaluated on standard benchmark of ten axial T2-weighted brain MR images for image segmentation.The experimental outcomes infer that it produces better optimal threshold values at a greater and quicker convergence rate.In contrast to other state-of-the-art methods,namely Adaptive Wind Driven Optimization(AWDO),Adaptive Bacterial Foraging(ABF)and Particle Swarm Optimization(PSO),the proposed algorithm has been found to be better at producing the best objective function,Peak Signal-to-Noise Ratio(PSNR),Standard Deviation(STD)and lower computational time values.Further,it was observed thatthe segmented image gives greater detail when the threshold level increases.Moreover,the statistical test result confirms that the best and mean values are almost zero and the average difference between best and mean value 1.86 is obtained through the 30 executions of the proposed algorithm.Thus,these images will lead to better segments of gray,white and cerebrospinal fluid that enable better clinical choices and diagnoses using a proposed algorithm.
文摘Conventional design of pier structures is based on the assumption of fully rigid joints. In practice, the real connections are semi-rigid that cause changes in dynamic characteristics. In this study, quality of the joints is investigated by considering changes in natural frequencies. For this purpose, numerical and experimental modal analyses are carried out on related physical model of a pier type structure. When numerical results are evaluated,natural frequencies generally do not match the expected experimental results. Uncertainties in different aspects of engineering problems are always a challenge for researchers. The numerical models which are constructed on the basis of highly idealized scheme may not be able to represent all of the physical aspects of the physical one. For this study, determination of percentage of semi-rigid joints is considered as an optimization problem based on the numerical and experimental frequencies. Probabilistic sensitivity analysis is also used to determine the search space.A new technique of optimization problem is solved by a combination of smart particle swarm optimization(PSO)and genetic algorithms, and a complicated and efficient system for model updating process is introduced. It is observed that the hybrid PSO-Genetic algorithm is applicable and appropriate in model updating process. It performs better than PSO algorithm, considering the good agreement between theoretical frequencies and experimental ones,before and after model updating.
基金the Open Fund(PLC201104)of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Chengdu University of Technology)the National Natural Science Foundation of China(No.61072073)the Key Project of Education Commission of Sichuan Province(No.10ZA072)
文摘Although the phase-shift seismic processing method has characteristics of high accuracy, good stability, high efficiency, and high-dip imaging, it is not able to adapt to strong lateral velocity variation. To overcome this defect, a finite-difference method in the frequency-space domain is introduced in the migration process, because it can adapt to strong lateral velocity variation and the coefficient is optimized by a hybrid genetic and simulated annealing algorithm. The two measures improve the precision of the approximation dispersion equation. Thus, the imaging effect is improved for areas of high-dip structure and strong lateral velocity variation. The migration imaging of a 2-D SEG/EAGE salt dome model proves that a better imaging effect in these areas is achieved by optimized phase-shift migration operator plus a finite-difference method based on a hybrid genetic and simulated annealing algorithm. The method proposed in this paper is better than conventional methods in imaging of areas of high-dip angle and strong lateral velocity variation.
文摘Aerodynamic parameters are important factors that affect projectile flight movement. To obtain accurate aerodynamic parameters, a hybrid genetic algorithm is proposed to identify and optimize the aerodynamic parameters of projectile. By combining the traditional simulated annealing method that is easy to fall into local optimum solution but hard to get global parameters with the genetic algorithm that has good global optimization ability but slow local optimization ability, the hybrid genetic algo- rithm makes full use of the advantages of the two algorithms for the optimization of projectile aerodynamic parameters. The simulation results show that the hybrid genetic algorithm is better than a single algorithm.
基金Supported by the National Natural Science Foundation of China(1117202591116)
文摘The genetic/gradient-based hybrid algorithm is introduced and used in the design studies of aeroelastic optimization of large aircraft wings to attain skin distribution,stiffness distribution and design sensitivity.The program of genetic algorithm is developed by the authors while the gradient-based algorithm borrows from the modified method for feasible direction in MSC/NASTRAN software.In the hybrid algorithm,the genetic algorithm is used to perform global search to avoid to fall into local optima,and then the excellent individuals of every generation optimized by the genetic algorithm are further fine-tuned by the modified method for feasible direction to attain the local optima and hence to get global optima.Moreover,the application effects of hybrid genetic algorithm in aeroelastic multidisciplinary design optimization of large aircraft wing are discussed,which satisfy multiple constraints of strength,displacement,aileron efficiency,and flutter speed.The application results show that the genetic/gradient-based hybrid algorithm is available for aeroelastic optimization of large aircraft wings in initial design phase as well as detailed design phase,and the optimization results are very consistent.Therefore,the design modifications can be decreased using the genetic/gradient-based hybrid algorithm.
基金Supported by the Fundamental Research Funds for the Central Universities(20102080201000085)the National Natural Science Foundation of China(50875189)
文摘The gear transmission system has been widely applied in mechanical systems,and many high-performance applications of these systems require low weight.With the aid of establishing the optimization model of the gear transmission system that consists of an objective function and some constraints(for example,the bending stress,the contact stress,the torsional strength,etc.),the optimal weight design of the gear transmission system can be transformed into the optimization problem for the objective function under the constraints.Moreover,both the shaft and the gear of the gear transmission system are considered simultaneously in our design.The hybrid Taguchi-genetic algorithm(HTGA)is employed to find the optimal design variables and the optimal weight of the system.An illustrated example for the single spur gear reducer is given to show that the optimal weight design problem can be successfully solved using the proposed design scheme.It also proves the high efficiency and feasibility of the algorithm in the gear design.
文摘Accurate prediction of the movement trajectory of sea surface targets holds significant importance in achieving an advantageous position in the sea battle field.This prediction plays a crucial role in ensuring security defense and confrontation,and is essential for effective deployment of military strategy.Accurately predicting the trajectory of sea surface targets using AIS(Automatic Identification System)information is crucial for security defense and confrontation,and holds significant importance for military strategy deployment.In response to the problem of insufficient accuracy in ship trajectory prediction,this study proposes a hybrid genetic algorithm to optimize the Long Short-Term Memory(LSTM)algorithm.The HGA-LSTM algorithm is proposed for ship trajectory prediction.It can converge faster and obtain better parameter solutions,thereby improving the effectiveness of ship trajectory prediction.Compared to traditional LSTM and GA-LSTM algorithms,experimental results demonstrate that this algorithm outperforms them in both single-step and multi-step prediction.
基金University Putra Malaysia under Putra Grant No.9531200。
文摘Previous studies about optimizing earthquake structural energy dissipation systems indicated that most existing techniques employ merely one or a few parameters as design variables in the optimization process,and thereby are only applicable only to simple,single,or multiple degree-of-freedom structures.The current approaches to optimization procedures take a specific damper with its properties and observe the effect of applying time history data to the building;however,there are many different dampers and isolators that can be used.Furthermore,there is a lack of studies regarding the optimum location for various viscous and wall dampers.The main aim of this study is hybridization of the particle swarm optimization(PSO) and gravitational search algorithm(GSA) to optimize the performance of earthquake energy dissipation systems(i.e.,damper devices) simultaneously with optimizing the characteristics of the structure.Four types of structural dampers device are considered in this study:(ⅰ) variable stiffness bracing(VSB) system,(ⅱ) rubber wall damper(RWD),(ⅲ) nonlinear conical spring bracing(NCSB) device,(iv) and multi-action stiffener(MAS) device.Since many parameters may affect the design of seismic resistant structures,this study proposes a hybrid of PSO and GSA to develop a hybrid,multi-objective optimization method to resolve the aforementioned problems.The characteristics of the above-mentioned damper devices as well as the section size for structural beams and columns are considered as variables for development of the PSO-GSA optimization algorithm to minimize structural seismic response in terms of nodal displacement(in three directions) as well as plastic hinge formation in structural members simultaneously with the weight of the structure.After that,the optimization algorithm is implemented to identify the best position of the damper device in the structural frame to have the maximum effect and minimize the seismic structure response.To examine the performance of the proposed PSO-GSA optimization method,it has been applied to a three-story reinforced structure equipped with a seismic damper device.The results revealed that the method successfully optimized the earthquake energy dissipation systems and reduced the effects of earthquakes on structures,which significantly increase the building’s stability and safety during seismic excitation.The analysis results showed a reduction in the seismic response of the structure regarding the formation of plastic hinges in structural members as well as the displacement of each story to approximately 99.63%,60.5%,79.13% and 57.42% for the VSB device,RWD,NCSB device,and MAS device,respectively.This shows that using the PSO-GSA optimization algorithm and optimized damper devices in the structure resulted in no structural damage due to earthquake vibration.
文摘Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on forecasting daily stock market returns,especially when using powerful machine learning techniques,such as deep neural networks(DNNs),to perform the analyses.DNNs employ various deep learning algorithms based on the combination of network structure,activation function,and model parameters,with their performance depending on the format of the data representation.This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF(ticker symbol:SPY)based on 60 financial and economic features.DNNs and traditional artificial neural networks(ANNs)are then deployed over the entire preprocessed but untransformed dataset,along with two datasets transformed via principal component analysis(PCA),to predict the daily direction of future stock market index returns.While controlling for overfitting,a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000.Moreover,a set of hypothesis testing procedures are implemented on the classification,and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset,as well as several other hybrid machine learning algorithms.In addition,the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested,including in a comparison against two standard benchmarks.
基金Projects(61573144,61773165,61673175,61174040)supported by the National Natural Science Foundation of ChinaProject(222201717006)supported by the Fundamental Research Funds for the Central Universities,China
文摘The hybrid flow shop scheduling problem with unrelated parallel machine is a typical NP-hard combinatorial optimization problem, and it exists widely in chemical, manufacturing and pharmaceutical industry. In this work, a novel mathematic model for the hybrid flow shop scheduling problem with unrelated parallel machine(HFSPUPM) was proposed. Additionally, an effective hybrid estimation of distribution algorithm was proposed to solve the HFSPUPM, taking advantage of the features in the mathematic model. In the optimization algorithm, a new individual representation method was adopted. The(EDA) structure was used for global search while the teaching learning based optimization(TLBO) strategy was used for local search. Based on the structure of the HFSPUPM, this work presents a series of discrete operations. Simulation results show the effectiveness of the proposed hybrid algorithm compared with other algorithms.
基金Supported by the Research Grants from Shanghai Municipal Natural Science Foundation（No.10190502500） Shanghai Maritime University Start-up Funds,Shanghai Science＆Technology Commission Projects（No.09DZ2250400） Shanghai Education Commission Project（No.J50604）
文摘Secure storage yard is one of the optimal core goals of container transportation;thus,making the necessary storage arrangements has become the most crucial part of the container terminal management systems（CTMS）.This paper investigates a random hybrid stacking algorithm（RHSA） for outbound containers that randomly enter the yard.In the first stage of RHSA,the distribution among blocks was analyzed with respect to the utilization ratio.In the second stage,the optimization of bay configuration was carried out by using the hybrid genetic algorithm.Moreover,an experiment was performed to test the RHSA.The results show that the explored algorithm is useful to increase the efficiency.
基金Foundation item： the National Science ＆ Technology Pillar Program （Nos. 2011BAH21B02 and 2011BAH21B03） and the Chengdu Major Scientific and Technological Achievements （No. 11zHzD038）
文摘For massive order allocation problem of the third party logistics （TPL） in ecommerce, this paper proposes a general order allocation model based on cloud architecture and hybrid genetic algorithm （GA）, implementing cloud deployable MapReduce （MR） code to parallelize allocation process, using heuristic rule to fix illegal chromosome during encoding process and adopting mixed integer programming （MIP） as fitness flmction to guarantee rationality of chromosome fitness. The simulation experiment shows that in mass processing of orders, the model performance in a multi-server cluster environment is remarkable superior to that in stand-alone environment. This model can be directly applied to cloud based logistics information platform （LIP） in near future, implementing fast auto-allocation for massive concurrent orders, with great application value.
