Since real world communication channels are not error free, the coded data transmitted on them may be corrupted, and block based image coding systems are vulnerable to transmission impairment. So the best neighborh...Since real world communication channels are not error free, the coded data transmitted on them may be corrupted, and block based image coding systems are vulnerable to transmission impairment. So the best neighborhood match method using genetic algorithm is used to conceal the error blocks. Experimental results show that the searching space can be greatly reduced by using genetic algorithm compared with exhaustive searching method, and good image quality is achieved. The peak signal noise ratios(PSNRs) of the restored images are increased greatly.展开更多
In order to improve the picking efficiency,reduce the picking time,this paper take artificial picking operation of a certain distribution center which has double-area warehouse as the studying object.Discuss the picki...In order to improve the picking efficiency,reduce the picking time,this paper take artificial picking operation of a certain distribution center which has double-area warehouse as the studying object.Discuss the picking task allocation and routing problems.Establish the TSP model of order-picking system.Create a heuristic algorithm bases on the Genetic Algorithm(GA)which help to solve the task allocating problem and to get the associated order-picking routes.And achieve the simulation experiment with the Visual 6.0C++platform to prove the rationality of the model and the effectiveness of the arithmetic.展开更多
The uncertain duration of each job in each machine in flow shop problem was regarded as an independent random variable and was described by mathematical expectation.And then,an immune based partheno-genetic algorithm ...The uncertain duration of each job in each machine in flow shop problem was regarded as an independent random variable and was described by mathematical expectation.And then,an immune based partheno-genetic algorithm was proposed by making use of concepts and principles introduced from immune system and genetic system in nature.In this method,processing se- quence of products could be expressed by the character encoding and each antibody represents a feasible schedule.Affinity was used to measure the matching degree between antibody and antigen.Then several antibodies producing operators,such as swopping,mov- ing,inverting,etc,were worked out.This algorithm was combined with evolution function of the genetic algorithm and density mechanism in organisms immune system.Promotion and inhibition of antibodies were realized by expected propagation ratio of an- tibodies,and in this way,premature convergence was improved.The simulation proved that this algorithm is effective.展开更多
This paper investigates the problem of synchronization for offset quadrature amplitude modulation based orthogonal frequency division multiplexing(OFDM/OQAM) systems based on the genetic algorithm. In order to increas...This paper investigates the problem of synchronization for offset quadrature amplitude modulation based orthogonal frequency division multiplexing(OFDM/OQAM) systems based on the genetic algorithm. In order to increase the spectrum efficiency,an improved preamble structure without guard symbols is derived at first. On this basis, instead of deriving the log likelihood function of power spectral density, joint estimation of the symbol timing offset and carrier frequency offset based on the preamble proposed is formulated into a bivariate optimization problem. After that, an improved genetic algorithm is used to find its global optimum solution. Conclusions can be drawn from simulation results that the proposed method has advantages in the joint estimation of synchronization.展开更多
The K-means method is one of the most widely used clustering methods and has been implemented in many fields of science and technology. One of the major problems of the k-means algorithm is that it may produce empty c...The K-means method is one of the most widely used clustering methods and has been implemented in many fields of science and technology. One of the major problems of the k-means algorithm is that it may produce empty clusters depending on initial center vectors. Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary principles of natural selection and genetics. This paper presents a hybrid version of the k-means algorithm with GAs that efficiently eliminates this empty cluster problem. Results of simulation experiments using several data sets prove our claim.展开更多
The dimensionality of data is increasing very rapidly,which creates challenges for most of the current mining and learning algorithms,such as large memory requirements and high computational costs.The literature inclu...The dimensionality of data is increasing very rapidly,which creates challenges for most of the current mining and learning algorithms,such as large memory requirements and high computational costs.The literature includes much research on feature selection for supervised learning.However,feature selection for unsupervised learning has only recently been studied.Finding the subset of features in unsupervised learning that enhances the performance is challenging since the clusters are indeterminate.This work proposes a hybrid technique for unsupervised feature selection called GAk-MEANS,which combines the genetic algorithm(GA)approach with the classical k-Means algorithm.In the proposed algorithm,a new fitness func-tion is designed in addition to new smart crossover and mutation operators.The effectiveness of this algorithm is demonstrated on various datasets.Fur-thermore,the performance of GAk-MEANS has been compared with other genetic algorithms,such as the genetic algorithm using the Sammon Error Function and the genetic algorithm using the Sum of Squared Error Function.Additionally,the performance of GAk-MEANS is compared with the state-of-the-art statistical unsupervised feature selection techniques.Experimental results show that GAk-MEANS consistently selects subsets of features that result in better classification accuracy compared to others.In particular,GAk-MEANS is able to significantly reduce the size of the subset of selected features by an average of 86.35%(72%–96.14%),which leads to an increase of the accuracy by an average of 3.78%(1.05%–6.32%)compared to using all features.When compared with the genetic algorithm using the Sammon Error Function,GAk-MEANS is able to reduce the size of the subset of selected features by 41.29%on average,improve the accuracy by 5.37%,and reduce the time by 70.71%.When compared with the genetic algorithm using the Sum of Squared Error Function,GAk-MEANS on average is able to reduce the size of the subset of selected features by 15.91%,and improve the accuracy by 9.81%,but the time is increased by a factor of 3.When compared with the machine-learning based methods,we observed that GAk-MEANS is able to increase the accuracy by 13.67%on average with an 88.76%average increase in time.展开更多
This paper presents a new algorithm for solving unit commitment (UC) problems using a binary-real coded genetic algorithm based on k-means clustering technique. UC is a NP-hard nonlinear mixed-integer optimization pro...This paper presents a new algorithm for solving unit commitment (UC) problems using a binary-real coded genetic algorithm based on k-means clustering technique. UC is a NP-hard nonlinear mixed-integer optimization problem, encountered as one of the toughest problems in power systems, in which some power generating units are to be scheduled in such a way that the forecasted demand is met at minimum production cost over a time horizon. In the proposed algorithm, the algorithm integrates the main features of a binary-real coded genetic algorithm (GA) and k-means clustering technique. The binary coded GA is used to obtain a feasible commitment schedule for each generating unit;while the power amounts generated by committed units are determined by using real coded GA for the feasible commitment obtained in each interval. k-means clustering algorithm divides population into a specific number of subpopulations with dynamic size. In this way, using k-means clustering algorithm allows the use of different GA operators with the whole population and avoids the local problem minima. The effectiveness of the proposed technique is validated on a test power system available in the literature. The proposed algorithm performance is found quite satisfactory in comparison with the previously reported results.展开更多
The combination of case-based reasoning （CBR） and genetic algorithm （GA） is considered in the problem of failure mode identification in aeronautical component failure analysis. Several imple- mentation issues such...The combination of case-based reasoning （CBR） and genetic algorithm （GA） is considered in the problem of failure mode identification in aeronautical component failure analysis. Several imple- mentation issues such as matching attributes selection, similarity measure calculation, weights learning and training evaluation policies are carefully studied. The testing applications illustrate that an accuracy of 74.67 % can be achieved with 75 balanced-distributed failure cases covering 3 failure modes, and that the resulting learning weight vector can be well applied to the other 2 failure modes, achieving 73.3 % of recognition accuracy. It is also proved that its popularizing capability is good to the recognition of even more mixed failure modes.展开更多
In this paper, the problem of reliability-based optimal design of simple offshore platform is studied, and a nonlinear fatigue damage model based on damage mechanics and genetic algorithms are used in the fatigue reli...In this paper, the problem of reliability-based optimal design of simple offshore platform is studied, and a nonlinear fatigue damage model based on damage mechanics and genetic algorithms are used in the fatigue reliability optimum design of the structure under stochastic wave load. The fatigue damage model and the yield failure reliability analyzing model are used in the paper. The reliability of the models and the effectiveness of genetic algorithm are shown by the results of optimum design.展开更多
CO_(2)flooding for enhanced oil recovery(EOR)not only enables underground carbon storage but also plays a critical role in tertiary oil recovery.However,its displacement efficiency is constrained by whether CO_(2)and ...CO_(2)flooding for enhanced oil recovery(EOR)not only enables underground carbon storage but also plays a critical role in tertiary oil recovery.However,its displacement efficiency is constrained by whether CO_(2)and crude oil achieve miscibility,necessitating precise prediction of the minimum miscibility pressure(MMP)for CO_(2)-oil systems.Traditional methods,such as experimental measurements and empirical correlations,face challenges including time-consuming procedures and limited applicability.In contrast,artificial intelligence(AI)algorithms have emerged as superior alternatives due to their efficiency,broad applicability,and high prediction accuracy.This study employs four AI algorithms—Random Forest Regression(RFR),Genetic Algorithm Based Back Propagation Artificial Neural Network(GA-BPNN),Support Vector Regression(SVR),and Gaussian Process Regression(GPR)—to establish predictive models for CO_(2)-oil MMP.A comprehensive database comprising 151 data entries was utilized for model development.The performance of these models was rigorously evaluated using five distinct statistical metrics and visualized comparisons.Validation results confirm their accuracy.Field applications demonstrate that all four models are effective for predicting MMP in ultra-deep reservoirs(burial depth>5000 m)with complex crude oil compositions.Among them,the RFR and GA-BPNN models outperform SVR and GPR,achieving root mean square errors(RMSE)of 0.33%and 2.23%,and average absolute percentage relative errors(AAPRE)of 0.01%and 0.04%,respectively.Sensitivity analysis of MMP-influencing factors reveals that reservoir temperature(T_(R))exerts the most significant impact on MMP,while Xint(mole fraction of intermediate oil components,including C_(2)-C_(4),CO_(2),and H_(2)S)exhibits the least influence.展开更多
The purpose of this paper is to solve the problem of Ad Hoc network routing protocol using a Genetic Algorithm based approach. In particular, the greater reliability and efficiency, in term of duration of communicatio...The purpose of this paper is to solve the problem of Ad Hoc network routing protocol using a Genetic Algorithm based approach. In particular, the greater reliability and efficiency, in term of duration of communication paths, due to the introduction of Genetic Classifier is demonstrated.展开更多
The direct torque control of the dual star induction motor(DTC-DSIM) using conventional PI controllers is characterized by unsatisfactory performance, such as high ripples of torque and flux, and sensitivity to parame...The direct torque control of the dual star induction motor(DTC-DSIM) using conventional PI controllers is characterized by unsatisfactory performance, such as high ripples of torque and flux, and sensitivity to parametric variations. Among the most evoked control strategies adopted in this field to overcome these drawbacks presented in classical drive, it is worth mentioning the use of the second order sliding mode control(SOSMC) based on the super twisting algorithm(STA) combined with the fuzzy logic control(FSOSMC). In order to realize the optimal control performance, the FSOSMC parameters are adjusted using an optimization algorithm based on the genetic algorithm(GA). The performances of the envisaged control scheme, called G-FSOSMC, are investigated against G-SOSMC, G-PI and BBO-FSOSMC algorithms. The proposed controller scheme is efficient in reducing the torque and flux ripples, and successfully suppresses chattering. The effects of parametric uncertainties do not affect system performance.展开更多
This paper examines the optimization of the lifetime and energy consumption of Wireless Sensor Networks (WSNs). These two competing objectives have a deep influence over the service qualification of networks and accor...This paper examines the optimization of the lifetime and energy consumption of Wireless Sensor Networks (WSNs). These two competing objectives have a deep influence over the service qualification of networks and according to recent studies, cluster formation is an appropriate solution for their achievement. To transmit aggregated data to the Base Station (BS), logical nodes called Cluster Heads (CHs) are required to relay data from the fixed-range sensing nodes located in the ground to high altitude aircraft. This study investigates the Genetic Algorithm (GA) as a dynamic technique to find optimum states. It is a simple framework that includes a proposed mathematical formula, which increasing in coverage is benchmarked against lifetime. Finally, the implementation of the proposed algorithm indicates a better efficiency compared to other simulated works.展开更多
Dynamic optimization problems are a kind of optimization problems that involve changes over time. They pose a serious challenge to traditional optimization methods as well as conventional genetic algorithms since the ...Dynamic optimization problems are a kind of optimization problems that involve changes over time. They pose a serious challenge to traditional optimization methods as well as conventional genetic algorithms since the goal is no longer to search for the optimal solution（s） of a fixed problem but to track the moving optimum over time. Dynamic optimization problems have attracted a growing interest from the genetic algorithm community in recent years. Several approaches have been developed to enhance the performance of genetic algorithms in dynamic environments. One approach is to maintain the diversity of the population via random immigrants. This paper proposes a hybrid immigrants scheme that combines the concepts of elitism, dualism and random immigrants for genetic algorithms to address dynamic optimization problems. In this hybrid scheme, the best individual, i.e., the elite, from the previous generation and its dual individual are retrieved as the bases to create immigrants via traditional mutation scheme. These elitism-based and dualism-based immigrants together with some random immigrants are substituted into the current population, replacing the worst individuals in the population. These three kinds of immigrants aim to address environmental changes of slight, medium and significant degrees respectively and hence efficiently adapt genetic algorithms to dynamic environments that are subject to different severities of changes. Based on a series of systematically constructed dynamic test problems, experiments are carried out to investigate the performance of genetic algorithms with the hybrid immigrants scheme and traditional random immigrants scheme. Experimental results validate the efficiency of the proposed hybrid immigrants scheme for improving the performance of genetic algorithms in dynamic environments.展开更多
In this paper, a hybrid genetic algorithm （GA） is proposed for the traveling salesman problem （TSP） with pickup and delivery （TSPPD）. In our algorithm, a novel pheromone-based crossover operator is advanced that...In this paper, a hybrid genetic algorithm （GA） is proposed for the traveling salesman problem （TSP） with pickup and delivery （TSPPD）. In our algorithm, a novel pheromone-based crossover operator is advanced that utilizes both local and global information to construct offspring. In addition, a local search procedure is integrated into the GA to accelerate convergence. The proposed GA has been tested on benchmark instances, and the computational results show that it gives better convergence than existing heuristics.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
Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experien...Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experience-based criteria. In order to eliminate linguistic criteria resulted from experience-based judgments and account for uncertainties in determining class boundaries developed by SMR system,the system classification results were corrected using two clustering algorithms, namely K-means and fuzzy c-means(FCM), for the ratings obtained via continuous and discrete functions. By applying clustering algorithms in SMR classification system, no in-advance experience-based judgment was made on the number of extracted classes in this system, and it was only after all steps of the clustering algorithms were accomplished that new classification scheme was proposed for SMR system under different failure modes based on the ratings obtained via continuous and discrete functions. The results of this study showed that, engineers can achieve more reliable and objective evaluations over slope stability by using SMR system based on the ratings calculated via continuous and discrete functions.展开更多
An efficient method is proposed for the design of finite impulse response(FIR) filter with arbitrary pass band edge,stop band edge frequencies and transition width.The proposed FIR band stop filter is designed using c...An efficient method is proposed for the design of finite impulse response(FIR) filter with arbitrary pass band edge,stop band edge frequencies and transition width.The proposed FIR band stop filter is designed using craziness based particle swarm optimization(CRPSO) approach.Given the filter specifications to be realized,the CRPSO algorithm generates a set of optimal filter coefficients and tries to meet the ideal frequency response characteristics.In this paper,for the given problem,the realizations of the optimal FIR band pass filters of different orders have been performed.The simulation results have been compared with those obtained by the well accepted evolutionary algorithms,such as Parks and McClellan algorithm(PMA),genetic algorithm(GA) and classical particle swarm optimization(PSO).Several numerical design examples justify that the proposed optimal filter design approach using CRPSO outperforms PMA and PSO,not only in the accuracy of the designed filter but also in the convergence speed and solution quality.展开更多
How to obtain accurate channel state information(CSI)at the transmitter with less pilot overhead for frequency division duplexing(FDD) massive multiple-input multiple-output(MIMO)system is a challenging issue due to t...How to obtain accurate channel state information(CSI)at the transmitter with less pilot overhead for frequency division duplexing(FDD) massive multiple-input multiple-output(MIMO)system is a challenging issue due to the large number of antennas. To reduce the overwhelming pilot overhead, a hybrid orthogonal and non-orthogonal pilot distribution at the base station(BS),which is a generalization of the existing pilot distribution scheme,is proposed by exploiting the common sparsity of channel due to the compact antenna arrangement. Then the block sparsity for antennas with hybrid pilot distribution is derived respectively and can be used to obtain channel impulse response. By employing the theoretical analysis of block sparse recovery, the total coherence criterion is proposed to optimize the sensing matrix composed by orthogonal pilots. Due to the huge complexity of optimal pilot acquisition, a genetic algorithm based pilot allocation(GAPA) algorithm is proposed to acquire optimal pilot distribution locations with fast convergence. Furthermore, the Cramer Rao lower bound is derived for non-orthogonal pilot-based channel estimation and can be asymptotically approached by the prior support set, especially when the optimized pilot is employed.展开更多
文摘Since real world communication channels are not error free, the coded data transmitted on them may be corrupted, and block based image coding systems are vulnerable to transmission impairment. So the best neighborhood match method using genetic algorithm is used to conceal the error blocks. Experimental results show that the searching space can be greatly reduced by using genetic algorithm compared with exhaustive searching method, and good image quality is achieved. The peak signal noise ratios(PSNRs) of the restored images are increased greatly.
文摘In order to improve the picking efficiency,reduce the picking time,this paper take artificial picking operation of a certain distribution center which has double-area warehouse as the studying object.Discuss the picking task allocation and routing problems.Establish the TSP model of order-picking system.Create a heuristic algorithm bases on the Genetic Algorithm(GA)which help to solve the task allocating problem and to get the associated order-picking routes.And achieve the simulation experiment with the Visual 6.0C++platform to prove the rationality of the model and the effectiveness of the arithmetic.
文摘The uncertain duration of each job in each machine in flow shop problem was regarded as an independent random variable and was described by mathematical expectation.And then,an immune based partheno-genetic algorithm was proposed by making use of concepts and principles introduced from immune system and genetic system in nature.In this method,processing se- quence of products could be expressed by the character encoding and each antibody represents a feasible schedule.Affinity was used to measure the matching degree between antibody and antigen.Then several antibodies producing operators,such as swopping,mov- ing,inverting,etc,were worked out.This algorithm was combined with evolution function of the genetic algorithm and density mechanism in organisms immune system.Promotion and inhibition of antibodies were realized by expected propagation ratio of an- tibodies,and in this way,premature convergence was improved.The simulation proved that this algorithm is effective.
基金supported by the National Natural Science Foundation of China(61671468)。
文摘This paper investigates the problem of synchronization for offset quadrature amplitude modulation based orthogonal frequency division multiplexing(OFDM/OQAM) systems based on the genetic algorithm. In order to increase the spectrum efficiency,an improved preamble structure without guard symbols is derived at first. On this basis, instead of deriving the log likelihood function of power spectral density, joint estimation of the symbol timing offset and carrier frequency offset based on the preamble proposed is formulated into a bivariate optimization problem. After that, an improved genetic algorithm is used to find its global optimum solution. Conclusions can be drawn from simulation results that the proposed method has advantages in the joint estimation of synchronization.
文摘The K-means method is one of the most widely used clustering methods and has been implemented in many fields of science and technology. One of the major problems of the k-means algorithm is that it may produce empty clusters depending on initial center vectors. Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary principles of natural selection and genetics. This paper presents a hybrid version of the k-means algorithm with GAs that efficiently eliminates this empty cluster problem. Results of simulation experiments using several data sets prove our claim.
文摘The dimensionality of data is increasing very rapidly,which creates challenges for most of the current mining and learning algorithms,such as large memory requirements and high computational costs.The literature includes much research on feature selection for supervised learning.However,feature selection for unsupervised learning has only recently been studied.Finding the subset of features in unsupervised learning that enhances the performance is challenging since the clusters are indeterminate.This work proposes a hybrid technique for unsupervised feature selection called GAk-MEANS,which combines the genetic algorithm(GA)approach with the classical k-Means algorithm.In the proposed algorithm,a new fitness func-tion is designed in addition to new smart crossover and mutation operators.The effectiveness of this algorithm is demonstrated on various datasets.Fur-thermore,the performance of GAk-MEANS has been compared with other genetic algorithms,such as the genetic algorithm using the Sammon Error Function and the genetic algorithm using the Sum of Squared Error Function.Additionally,the performance of GAk-MEANS is compared with the state-of-the-art statistical unsupervised feature selection techniques.Experimental results show that GAk-MEANS consistently selects subsets of features that result in better classification accuracy compared to others.In particular,GAk-MEANS is able to significantly reduce the size of the subset of selected features by an average of 86.35%(72%–96.14%),which leads to an increase of the accuracy by an average of 3.78%(1.05%–6.32%)compared to using all features.When compared with the genetic algorithm using the Sammon Error Function,GAk-MEANS is able to reduce the size of the subset of selected features by 41.29%on average,improve the accuracy by 5.37%,and reduce the time by 70.71%.When compared with the genetic algorithm using the Sum of Squared Error Function,GAk-MEANS on average is able to reduce the size of the subset of selected features by 15.91%,and improve the accuracy by 9.81%,but the time is increased by a factor of 3.When compared with the machine-learning based methods,we observed that GAk-MEANS is able to increase the accuracy by 13.67%on average with an 88.76%average increase in time.
文摘This paper presents a new algorithm for solving unit commitment (UC) problems using a binary-real coded genetic algorithm based on k-means clustering technique. UC is a NP-hard nonlinear mixed-integer optimization problem, encountered as one of the toughest problems in power systems, in which some power generating units are to be scheduled in such a way that the forecasted demand is met at minimum production cost over a time horizon. In the proposed algorithm, the algorithm integrates the main features of a binary-real coded genetic algorithm (GA) and k-means clustering technique. The binary coded GA is used to obtain a feasible commitment schedule for each generating unit;while the power amounts generated by committed units are determined by using real coded GA for the feasible commitment obtained in each interval. k-means clustering algorithm divides population into a specific number of subpopulations with dynamic size. In this way, using k-means clustering algorithm allows the use of different GA operators with the whole population and avoids the local problem minima. The effectiveness of the proposed technique is validated on a test power system available in the literature. The proposed algorithm performance is found quite satisfactory in comparison with the previously reported results.
文摘The combination of case-based reasoning （CBR） and genetic algorithm （GA） is considered in the problem of failure mode identification in aeronautical component failure analysis. Several imple- mentation issues such as matching attributes selection, similarity measure calculation, weights learning and training evaluation policies are carefully studied. The testing applications illustrate that an accuracy of 74.67 % can be achieved with 75 balanced-distributed failure cases covering 3 failure modes, and that the resulting learning weight vector can be well applied to the other 2 failure modes, achieving 73.3 % of recognition accuracy. It is also proved that its popularizing capability is good to the recognition of even more mixed failure modes.
基金This work was financially supported by the National Science Foundation of China
文摘In this paper, the problem of reliability-based optimal design of simple offshore platform is studied, and a nonlinear fatigue damage model based on damage mechanics and genetic algorithms are used in the fatigue reliability optimum design of the structure under stochastic wave load. The fatigue damage model and the yield failure reliability analyzing model are used in the paper. The reliability of the models and the effectiveness of genetic algorithm are shown by the results of optimum design.
文摘CO_(2)flooding for enhanced oil recovery(EOR)not only enables underground carbon storage but also plays a critical role in tertiary oil recovery.However,its displacement efficiency is constrained by whether CO_(2)and crude oil achieve miscibility,necessitating precise prediction of the minimum miscibility pressure(MMP)for CO_(2)-oil systems.Traditional methods,such as experimental measurements and empirical correlations,face challenges including time-consuming procedures and limited applicability.In contrast,artificial intelligence(AI)algorithms have emerged as superior alternatives due to their efficiency,broad applicability,and high prediction accuracy.This study employs four AI algorithms—Random Forest Regression(RFR),Genetic Algorithm Based Back Propagation Artificial Neural Network(GA-BPNN),Support Vector Regression(SVR),and Gaussian Process Regression(GPR)—to establish predictive models for CO_(2)-oil MMP.A comprehensive database comprising 151 data entries was utilized for model development.The performance of these models was rigorously evaluated using five distinct statistical metrics and visualized comparisons.Validation results confirm their accuracy.Field applications demonstrate that all four models are effective for predicting MMP in ultra-deep reservoirs(burial depth>5000 m)with complex crude oil compositions.Among them,the RFR and GA-BPNN models outperform SVR and GPR,achieving root mean square errors(RMSE)of 0.33%and 2.23%,and average absolute percentage relative errors(AAPRE)of 0.01%and 0.04%,respectively.Sensitivity analysis of MMP-influencing factors reveals that reservoir temperature(T_(R))exerts the most significant impact on MMP,while Xint(mole fraction of intermediate oil components,including C_(2)-C_(4),CO_(2),and H_(2)S)exhibits the least influence.
文摘The purpose of this paper is to solve the problem of Ad Hoc network routing protocol using a Genetic Algorithm based approach. In particular, the greater reliability and efficiency, in term of duration of communication paths, due to the introduction of Genetic Classifier is demonstrated.
基金Project supported by the LEB Research LaboratoryDepartment of Electrical Engineering,University of Batna 2, Algeria。
文摘The direct torque control of the dual star induction motor(DTC-DSIM) using conventional PI controllers is characterized by unsatisfactory performance, such as high ripples of torque and flux, and sensitivity to parametric variations. Among the most evoked control strategies adopted in this field to overcome these drawbacks presented in classical drive, it is worth mentioning the use of the second order sliding mode control(SOSMC) based on the super twisting algorithm(STA) combined with the fuzzy logic control(FSOSMC). In order to realize the optimal control performance, the FSOSMC parameters are adjusted using an optimization algorithm based on the genetic algorithm(GA). The performances of the envisaged control scheme, called G-FSOSMC, are investigated against G-SOSMC, G-PI and BBO-FSOSMC algorithms. The proposed controller scheme is efficient in reducing the torque and flux ripples, and successfully suppresses chattering. The effects of parametric uncertainties do not affect system performance.
文摘This paper examines the optimization of the lifetime and energy consumption of Wireless Sensor Networks (WSNs). These two competing objectives have a deep influence over the service qualification of networks and according to recent studies, cluster formation is an appropriate solution for their achievement. To transmit aggregated data to the Base Station (BS), logical nodes called Cluster Heads (CHs) are required to relay data from the fixed-range sensing nodes located in the ground to high altitude aircraft. This study investigates the Genetic Algorithm (GA) as a dynamic technique to find optimum states. It is a simple framework that includes a proposed mathematical formula, which increasing in coverage is benchmarked against lifetime. Finally, the implementation of the proposed algorithm indicates a better efficiency compared to other simulated works.
基金This work was supported by UK EPSRC(No.EP/E060722/01)Broil FAPESP(Proc.04/04289-6).
文摘Dynamic optimization problems are a kind of optimization problems that involve changes over time. They pose a serious challenge to traditional optimization methods as well as conventional genetic algorithms since the goal is no longer to search for the optimal solution（s） of a fixed problem but to track the moving optimum over time. Dynamic optimization problems have attracted a growing interest from the genetic algorithm community in recent years. Several approaches have been developed to enhance the performance of genetic algorithms in dynamic environments. One approach is to maintain the diversity of the population via random immigrants. This paper proposes a hybrid immigrants scheme that combines the concepts of elitism, dualism and random immigrants for genetic algorithms to address dynamic optimization problems. In this hybrid scheme, the best individual, i.e., the elite, from the previous generation and its dual individual are retrieved as the bases to create immigrants via traditional mutation scheme. These elitism-based and dualism-based immigrants together with some random immigrants are substituted into the current population, replacing the worst individuals in the population. These three kinds of immigrants aim to address environmental changes of slight, medium and significant degrees respectively and hence efficiently adapt genetic algorithms to dynamic environments that are subject to different severities of changes. Based on a series of systematically constructed dynamic test problems, experiments are carried out to investigate the performance of genetic algorithms with the hybrid immigrants scheme and traditional random immigrants scheme. Experimental results validate the efficiency of the proposed hybrid immigrants scheme for improving the performance of genetic algorithms in dynamic environments.
文摘In this paper, a hybrid genetic algorithm （GA） is proposed for the traveling salesman problem （TSP） with pickup and delivery （TSPPD）. In our algorithm, a novel pheromone-based crossover operator is advanced that utilizes both local and global information to construct offspring. In addition, a local search procedure is integrated into the GA to accelerate convergence. The proposed GA has been tested on benchmark instances, and the computational results show that it gives better convergence than existing heuristics.
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.
文摘Classification systems such as Slope Mass Rating(SMR) are currently being used to undertake slope stability analysis. In SMR classification system, data is allocated to certain classes based on linguistic and experience-based criteria. In order to eliminate linguistic criteria resulted from experience-based judgments and account for uncertainties in determining class boundaries developed by SMR system,the system classification results were corrected using two clustering algorithms, namely K-means and fuzzy c-means(FCM), for the ratings obtained via continuous and discrete functions. By applying clustering algorithms in SMR classification system, no in-advance experience-based judgment was made on the number of extracted classes in this system, and it was only after all steps of the clustering algorithms were accomplished that new classification scheme was proposed for SMR system under different failure modes based on the ratings obtained via continuous and discrete functions. The results of this study showed that, engineers can achieve more reliable and objective evaluations over slope stability by using SMR system based on the ratings calculated via continuous and discrete functions.
文摘An efficient method is proposed for the design of finite impulse response(FIR) filter with arbitrary pass band edge,stop band edge frequencies and transition width.The proposed FIR band stop filter is designed using craziness based particle swarm optimization(CRPSO) approach.Given the filter specifications to be realized,the CRPSO algorithm generates a set of optimal filter coefficients and tries to meet the ideal frequency response characteristics.In this paper,for the given problem,the realizations of the optimal FIR band pass filters of different orders have been performed.The simulation results have been compared with those obtained by the well accepted evolutionary algorithms,such as Parks and McClellan algorithm(PMA),genetic algorithm(GA) and classical particle swarm optimization(PSO).Several numerical design examples justify that the proposed optimal filter design approach using CRPSO outperforms PMA and PSO,not only in the accuracy of the designed filter but also in the convergence speed and solution quality.
基金supported by the National Natural Science Foundation of China(61671176 61671173)the Fundamental Research Funds for the Center Universities(HIT.MKSTISP.2016 13)
文摘How to obtain accurate channel state information(CSI)at the transmitter with less pilot overhead for frequency division duplexing(FDD) massive multiple-input multiple-output(MIMO)system is a challenging issue due to the large number of antennas. To reduce the overwhelming pilot overhead, a hybrid orthogonal and non-orthogonal pilot distribution at the base station(BS),which is a generalization of the existing pilot distribution scheme,is proposed by exploiting the common sparsity of channel due to the compact antenna arrangement. Then the block sparsity for antennas with hybrid pilot distribution is derived respectively and can be used to obtain channel impulse response. By employing the theoretical analysis of block sparse recovery, the total coherence criterion is proposed to optimize the sensing matrix composed by orthogonal pilots. Due to the huge complexity of optimal pilot acquisition, a genetic algorithm based pilot allocation(GAPA) algorithm is proposed to acquire optimal pilot distribution locations with fast convergence. Furthermore, the Cramer Rao lower bound is derived for non-orthogonal pilot-based channel estimation and can be asymptotically approached by the prior support set, especially when the optimized pilot is employed.
