In this paper, a unique combination among probabilistic roadmap, modified ant colony optimization, and third order B-spline curve has been proposed to solve path planning problems?in complex and very complex environme...In this paper, a unique combination among probabilistic roadmap, modified ant colony optimization, and third order B-spline curve has been proposed to solve path planning problems?in complex and very complex environments. This proposed approach can be divided into three stages. First stage involves constructing a random roadmap depending on the environment complexity using probabilistic roadmap algorithm. Roadmap can be constructed by distributing N nodes randomly in complex and very complex static environments then pairing these nodes together according to some criteria or conditions. The constructed roadmap contains a huge number of possible random paths that may lead to connecting?the start and the goal points together. Second stage includes finding path within the pre-constructed roadmap. Modified ant colony optimization has been proposed to find or to search the best path between start and goal points, where in addition to the proposed combination, ACO has been modified to increase its ability to find shorter path. Finally, the third stage uses B-spline curve?to smooth and reduce the total length of the found path in the previous stage. The results of the proposed approach ensure?the?feasible?path between start and goal points in complex and very complex environments. Also, the path is guaranteed to be short, smooth, continuous?and safe.展开更多
A design analysis of a mixing nozzle was performed using a combination of probabilistic and optimization techniques. A novel approach was utilized where probabilistic analysis was used to reduce the number of geometri...A design analysis of a mixing nozzle was performed using a combination of probabilistic and optimization techniques. A novel approach was utilized where probabilistic analysis was used to reduce the number of geometric constraints based on sensitivity factors. An optimization algorithm used only the most significant parameters to maximize mixing. A second probabilistic analysis was performed after optimization was com-plete in order to quantitatively predict the effects of manufacturing tolerances on mixing performance. This process for automated design is attractive over full parameter optimization techniques due to the computa-tional efficiency resulting from an intelligent reduction in evaluated variables.展开更多
This paper proposes an enhanced arithmetic optimization algorithm(AOA)called PSAOA that incorporates the proposed probabilistic search strategy to increase the searching quality of the original AOA.Furthermore,an adju...This paper proposes an enhanced arithmetic optimization algorithm(AOA)called PSAOA that incorporates the proposed probabilistic search strategy to increase the searching quality of the original AOA.Furthermore,an adjustable parameter is also developed to balance the exploration and exploitation operations.In addition,a jump mechanism is included in the PSAOAto assist individuals in jumping out of local optima.Using 29 classical benchmark functions,the proposed PSAOA is extensively tested.Compared to the AOA and other well-known methods,the experiments demonstrated that the proposed PSAOA beats existing comparison algorithms on the majority of the test functions.展开更多
Rule-based portfolio construction strategies are rising as investmentdemand grows, and smart beta strategies are becoming a trend amonginstitutional investors. Smart beta strategies have high transparency, lowmanageme...Rule-based portfolio construction strategies are rising as investmentdemand grows, and smart beta strategies are becoming a trend amonginstitutional investors. Smart beta strategies have high transparency, lowmanagement costs, and better long-term performance, but are at the risk ofsevere short-term declines due to a lack of Risk Control tools. Although thereare some methods to use historical volatility for Risk Control, it is still difficultto adapt to the rapid switch of market styles. How to strengthen the RiskControl management of the portfolio while maintaining the original advantagesof smart beta has become a new issue of concern in the industry. Thispaper demonstrates the scientific validity of using a probability prediction forposition optimization through an optimization theory and proposes a novelnatural gradient boosting (NGBoost)-based portfolio optimization method,which predicts stock prices and their probability distributions based on non-Bayesian methods and maximizes the Sharpe ratio expectation of positionoptimization. This paper validates the effectiveness and practicality of themodel by using the Chinese stock market, and the experimental results showthat the proposed method in this paper can reduce the volatility by 0.08 andincrease the expected portfolio cumulative return (reaching a maximum of67.1%) compared with the mainstream methods in the industry.展开更多
Probabilistic method requires a lot of sample information to describe the probability distributions of uncertain variables and has difficulty in dealing with the optimization problem with uncertain parameters which co...Probabilistic method requires a lot of sample information to describe the probability distributions of uncertain variables and has difficulty in dealing with the optimization problem with uncertain parameters which contains unsufficient information.To solve this problem,a robust optimization operation method based on information gap decision theory(IGDT) is presented considering the non-probabilistic uncertainties of parameters.By the proposed method the maximum resistance to the disturbance of uncertain parameters is achieved and the optimization strategies with uncertain parameters are presented.Finally,numerical simulation is performed on the modified IEEE-14 bus system.Numerical results show the effectiveness of the proposed approach.展开更多
This paper addresses the high dimension sample problem in discriminate analysis under nonparametric and supervised assumptions. Since there is a kind of equivalence between the probabilistic dependence measure and the...This paper addresses the high dimension sample problem in discriminate analysis under nonparametric and supervised assumptions. Since there is a kind of equivalence between the probabilistic dependence measure and the Bayes classification error probability, we propose to use an iterative algorithm to optimize the dimension reduction for classification with a probabilistic approach to achieve the Bayes classifier. The estimated probabilities of different errors encountered along the different phases of the system are realized by the Kernel estimate which is adjusted in a means of the smoothing parameter. Experiment results suggest that the proposed approach performs well.展开更多
Polynomial-time randomized algorithms were constructed to approximately solve optimal robust performance controller design problems in probabilistic sense and the rigorous mathematical justification of the approach wa...Polynomial-time randomized algorithms were constructed to approximately solve optimal robust performance controller design problems in probabilistic sense and the rigorous mathematical justification of the approach was given. The randomized algorithms here were based on a property from statistical learning theory known as (uniform) convergence of empirical means (UCEM). It is argued that in order to assess the performance of a controller as the plant varies over a pre-specified family, it is better to use the average performance of the controller as the objective function to be optimized, rather than its worst-case performance. The approach is illustrated to be efficient through an example.展开更多
The rapid expansion of offshore wind energy necessitates robust and cost-effective electrical collector system(ECS)designs that prioritize lifetime operational reliability.Traditional optimization approaches often sim...The rapid expansion of offshore wind energy necessitates robust and cost-effective electrical collector system(ECS)designs that prioritize lifetime operational reliability.Traditional optimization approaches often simplify reliability considerations or fail to holistically integrate them with economic and technical constraints.This paper introduces a novel,two-stage optimization framework for offshore wind farm(OWF)ECS planning that systematically incorporates reliability.The first stage employs Mixed-Integer Linear Programming(MILP)to determine an optimal radial network topology,considering linearized reliability approximations and geographical constraints.The second stage enhances this design by strategically placing tie-lines using a Mixed-Integer Quadratically Constrained Program(MIQCP).This stage leverages a dynamic-aware adaptation of Multi-Source Multi-Terminal Network Reliability(MSMT-NR)assessment,with its inherent nonlinear equations successfully transformed into a solvable MIQCP form for loopy networks.A benchmark case study demonstrates the framework’s efficacy,illustrating how increasing the emphasis on reliability leads to more distributed and interconnected network topologies,effectively balancing investment costs against enhanced system resilience.展开更多
Aiming at the problems of increasing uncertainty of low-carbon generation energy in active distribution network(ADN)and the difficulty of security assessment of distribution network,this paper proposes a two-phase sch...Aiming at the problems of increasing uncertainty of low-carbon generation energy in active distribution network(ADN)and the difficulty of security assessment of distribution network,this paper proposes a two-phase scheduling model for flexible resources in ADN based on probabilistic risk perception.First,a full-cycle probabilistic trend sequence is constructed based on the source-load historical data,and in the day-ahead scheduling phase,the response interval of the flexibility resources on the load and storage side is optimized based on the probabilistic trend,with the probability of the security boundary as the security constraint,and with the economy as the objective.Then in the intraday phase,the core security and economic operation boundary of theADNis screened in real time.Fromthere,it quantitatively senses the degree of threat to the core security and economic operation boundary under the current source-load prediction information,and identifies the strictly secure and low/high-risk time periods.Flexibility resources within the response interval are dynamically adjusted in real-time by focusing on high-risk periods to cope with future core risks of the distribution grid.Finally,the improved IEEE 33-node distribution system is simulated to obtain the flexibility resource scheduling scheme on the load and storage side.Thescheduling results are evaluated from the perspectives of risk probability and flexible resource utilization efficiency,and the analysis shows that the scheduling model in this paper can promote the consumption of low-carbon energy from wind and photovoltaic sourceswhile reducing the operational risk of the distribution network.展开更多
The probabilistic modeling is applied to calculate microstructural features of the thin complex smprolloy turbine blades cast by the vacuum investment process. The random distribution, orientation and physical mechani...The probabilistic modeling is applied to calculate microstructural features of the thin complex smprolloy turbine blades cast by the vacuum investment process. The random distribution, orientation and physical mechanism of the nucleation, the growth kinetics of dendrites and the columnar-to-equiaxed transition (CET) are considered.Capitalizing on these simulating schemes, the comprehensive influence of key process variables on the scale and uniformity of grains has been involved quantitatively. The validity of the modeling is confirmed by selection of the optimum process variables.展开更多
Ride and handling are two paramount factors in design and development of vehicle suspension systems. Conflicting trends in ride and handling characteristics propel engineers toward employing multi-objective optimizati...Ride and handling are two paramount factors in design and development of vehicle suspension systems. Conflicting trends in ride and handling characteristics propel engineers toward employing multi-objective optimization methods capable of providing the best trade-off designs compromising both criteria simultaneously. Although many studies have been performed on multi-objective optimization of vehicle suspension system, only a few of them have used probabilistic approaches considering effects of uncertainties in the design. However, it has been proved that optimum point obtained from deterministic optimization without taking into account the effects of uncertainties may lead to high-risk points instead of optimum ones. In this work, reliability-based robust multi-objective optimization of a 5 degree of freedom （5-DOF） vehicle suspension system is performed using method of non-dominated sorting genetic algorithm-II （NSGA-II） in conjunction with Monte Carlo simulation （MCS） to obtain best designs considering both comfort and handling. Road profile is modeled as a random function using power spectral density （PSD） which is in better accordance with reality. To accommodate the robust approach, the variance of all objective functions is also considered to be minimized. Also, to take into account the reliability criterion, a reliability-based constraint is considered in the optimization. A deterministic optimization has also been performed to compare the results with probabilistic study and some other deterministic studies in the literature. In addition, sensitivity analysis has been performed to reveal the effects of different design variables on objective functions. To introduce the best trade-off points from the obtained Pareto fronts, TOPSIS method has been employed. Results show that optimum design point obtained from probabilistic optimization in this work provides better performance while demonstrating very good reliability and robustness. However, other optimum points from deterministic optimizations violate the regarded constraints in the presence of uncertainties.展开更多
In order to improve the fine structure inversion ability of igneous rocks for the exploration of underlying strata, based on particle swarm optimization(PSO), we have developed a method for seismic wave impedance inve...In order to improve the fine structure inversion ability of igneous rocks for the exploration of underlying strata, based on particle swarm optimization(PSO), we have developed a method for seismic wave impedance inversion. Through numerical simulation, we tested the effects of different algorithm parameters and different model parameterization methods on PSO wave impedance inversion, and analyzed the characteristics of PSO method. Under the conclusions drawn from numerical simulation, we propose the scheme of combining a cross-moving strategy based on a divided block model and high-frequency filtering technology for PSO inversion. By analyzing the inversion results of a wedge model of a pitchout coal seam and a coal coking model with igneous rock intrusion, we discuss the vertical and horizontal resolution, stability and reliability of PSO inversion. Based on the actual seismic and logging data from an igneous area, by taking a seismic profile through wells as an example, we discuss the characteristics of three inversion methods, including model-based wave impedance inversion, multi-attribute seismic inversion based on probabilistic neural network(PNN) and wave impedance inversion based on PSO.And we draw the conclusion that the inversion based on PSO method has a better result for this igneous area.展开更多
A large number of mathematical models were developed for supporting agricultural production structure optimization decisions; however, few of them can address various uncertainties existing in many factors （e.g., eco...A large number of mathematical models were developed for supporting agricultural production structure optimization decisions; however, few of them can address various uncertainties existing in many factors （e.g., eco-social benefit maximization, food security, employment stability and ecosystem balance）. In this study, an interval-probabilistic agricultural production structure optimization model （IPAPSOM） is formulated for tackling uncertainty presented as discrete intervals and/or probability distribution. The developed model improves upon the existing probabilistic programming and inexact optimization approaches. The IPAPSOM considers not only food security policy constraints, but also involves rural households’income increase and eco-environmental conversation, which can effectively reflect various interrelations among different aspects in an agricultural production structure optimization system. Moreover, it can also help examine the reliability of satisfying （or risk of violating） system constraints under uncertainty. The model is applied to a real case of long-term agricultural production structure optimization in Dancheng County, which is located in Henan Province of Central China as one of the major grain producing areas. Interval solutions associated with different risk levels of constraint violation are obtained. The results are useful for generating a range of decision alternatives under various system benefit conditions, and thus helping decision makers to identify the desired agricultural production structure optimization strategy under uncertainty.展开更多
Cost reduction in electric power generation is a major management concern, and it is therefore necessary to reduce maintenance expenses while upholding plant reliability. A maintenance optimization system 'FREEDOM...Cost reduction in electric power generation is a major management concern, and it is therefore necessary to reduce maintenance expenses while upholding plant reliability. A maintenance optimization system 'FREEDOM', which uses RBM technique, DCF (discounted cash flow) and NPV (net present value) calculation functions, has been newly developed. This system probabilistically evaluates the lifetime of boiler and turbine and quantitatively calculates the risk defined as the cumulative probability of failure multiplied by the consequence of failure. Economically optimized timing of inspection and alternative countermeasure such as repair and replacement are then recommended. This system has already been applied to seven plants in Japan, and its effectiveness has been confirmed.展开更多
In order to take into account the uncertainties linked to the variables in the evaluation of the statistical properties of structural response, a reliability approach with probabilistic aspect was considered. This is ...In order to take into account the uncertainties linked to the variables in the evaluation of the statistical properties of structural response, a reliability approach with probabilistic aspect was considered. This is called the Probabilistic Transformation Method (PTM). This method is readily applicable when the function between the input and the output of the system is explicit. However, the situation is much more involved when it is necessary to perform the evaluation of implicit function between the input and the output of the system through numerical models. In this work, we propose a technique that combines Finite Element Analysis (FEA) and Probabilistic Transformation Method (PTM) to evaluate the Probability Density Function (PDF) of response where the function between the input and the output of the system is implicit. This technique is based on the numerical simulations of the Finite Element Analysis (FEA) and the Probabilistic Transformation Method (PTM) using an interface between Finite Element software and Matlab. Some problems of structures are treated in order to prove the applicability of the proposed technique. Moreover, the obtained results are compared to those obtained by the reference method of Monte Carlo. A second aim of this work is to develop an algorithm of global optimization using the local method SQP, because of its effectiveness and its rapidity of convergence. For this reason, we have combined the method SQP with the Multi start method. This developed algorithm is tested on test functions comparing with other methods such as the method of Particle Swarm Optimization (PSO). In order to test the applicability of the proposed approach, a structure is optimized under reliability constraints.展开更多
Fuzzy regression analysis is an important regression analysis method to predict uncertain information in the real world. In this paper, the input data are crisp with randomness;the output data are trapezoid fuzzy numb...Fuzzy regression analysis is an important regression analysis method to predict uncertain information in the real world. In this paper, the input data are crisp with randomness;the output data are trapezoid fuzzy number, and three different risk preferences and chaos optimization algorithm are introduced to establish fuzzy regression model. On the basis of the principle of the minimum total spread between the observed and the estimated values, risk-neutral, risk-averse, and risk-seeking fuzzy regression model are developed to obtain the parameters of fuzzy linear regression model. Chaos optimization algorithm is used to determine the digital characteristic of random variables. The mean absolute percentage error and variance of errors are adopted to compare the modeling results. A stock rating case is used to evaluate the fuzzy regression models. The comparisons with five existing methods show that our proposed method has satisfactory performance.展开更多
In view of incomplete probability information multi-objective question, it used probabilistic perturbation method and Edgeworth series technique to study reliability optimization design. The first four moments of basi...In view of incomplete probability information multi-objective question, it used probabilistic perturbation method and Edgeworth series technique to study reliability optimization design. The first four moments of basic random variables are known under condition. It used the Ant Colony Algorithm to design cutting head roadheader, the optimized result indicated that cutting head load fluctuation and compared energy consumption were reduced obviously at the same time. This result enhanced roadheader operational reliability and energy effectively.展开更多
In the noisy intermediate-scale quantum era,emerging classical-quantum hybrid optimization algorithms,such as variational quantum algorithms(VQAs),can leverage the unique characteristics of quantum devices to accelera...In the noisy intermediate-scale quantum era,emerging classical-quantum hybrid optimization algorithms,such as variational quantum algorithms(VQAs),can leverage the unique characteristics of quantum devices to accelerate computations tailored to specific problems with shallow circuits.However,these algorithms encounter biases and iteration difficulties due to significant noise in quantum processors.These difficulties can only be partially addressed without error correction by optimizing hardware,reducing circuit complexity,or fitting and extrapolating.A compelling solution is applying probabilistic error cancellation(PEC),a quantum error mitigation technique that enables unbiased results without full error correction.Traditional PEC is challenging to apply in VQAs due to its variance amplification,contradicting iterative process assumptions.This paper proposes a novel noise-adaptable strategy that combines PEC with the quantum approximate optimization algorithm(QAOA).It is implemented through invariant sampling circuits(invariant-PEC,or IPEC)and substantially reduces iteration variance.This strategy marks the first successful integration of PEC and QAOA,resulting in efficient convergence.Moreover,we introduce adaptive partial PEC(APPEC),which modulates the error cancellation proportion of IPEC during iteration.We experimentally validate this technique on a superconducting quantum processor,cutting sampling cost by 90.1%.Notably,we find that dynamic adjustments of error levels via APPEC can enhance the ability to escape from local minima and reduce sampling costs.These results open promising avenues for executing VQAs with large-scale,low-noise quantum circuits,paving the way for practical quantum computing advancements.展开更多
文摘In this paper, a unique combination among probabilistic roadmap, modified ant colony optimization, and third order B-spline curve has been proposed to solve path planning problems?in complex and very complex environments. This proposed approach can be divided into three stages. First stage involves constructing a random roadmap depending on the environment complexity using probabilistic roadmap algorithm. Roadmap can be constructed by distributing N nodes randomly in complex and very complex static environments then pairing these nodes together according to some criteria or conditions. The constructed roadmap contains a huge number of possible random paths that may lead to connecting?the start and the goal points together. Second stage includes finding path within the pre-constructed roadmap. Modified ant colony optimization has been proposed to find or to search the best path between start and goal points, where in addition to the proposed combination, ACO has been modified to increase its ability to find shorter path. Finally, the third stage uses B-spline curve?to smooth and reduce the total length of the found path in the previous stage. The results of the proposed approach ensure?the?feasible?path between start and goal points in complex and very complex environments. Also, the path is guaranteed to be short, smooth, continuous?and safe.
文摘A design analysis of a mixing nozzle was performed using a combination of probabilistic and optimization techniques. A novel approach was utilized where probabilistic analysis was used to reduce the number of geometric constraints based on sensitivity factors. An optimization algorithm used only the most significant parameters to maximize mixing. A second probabilistic analysis was performed after optimization was com-plete in order to quantitatively predict the effects of manufacturing tolerances on mixing performance. This process for automated design is attractive over full parameter optimization techniques due to the computa-tional efficiency resulting from an intelligent reduction in evaluated variables.
基金partially supported by the Fundamental Research Funds for the Central Universities(WUT:2022IVA067)the National Natural Science Foundation of China(Grant No.:72172112)the Fundamental Research Funds for the Central Universities(HUST:2019kfyRCPY038).
文摘This paper proposes an enhanced arithmetic optimization algorithm(AOA)called PSAOA that incorporates the proposed probabilistic search strategy to increase the searching quality of the original AOA.Furthermore,an adjustable parameter is also developed to balance the exploration and exploitation operations.In addition,a jump mechanism is included in the PSAOAto assist individuals in jumping out of local optima.Using 29 classical benchmark functions,the proposed PSAOA is extensively tested.Compared to the AOA and other well-known methods,the experiments demonstrated that the proposed PSAOA beats existing comparison algorithms on the majority of the test functions.
基金supported by the National Natural Science Foundation of China[Grant Number 61902349].
文摘Rule-based portfolio construction strategies are rising as investmentdemand grows, and smart beta strategies are becoming a trend amonginstitutional investors. Smart beta strategies have high transparency, lowmanagement costs, and better long-term performance, but are at the risk ofsevere short-term declines due to a lack of Risk Control tools. Although thereare some methods to use historical volatility for Risk Control, it is still difficultto adapt to the rapid switch of market styles. How to strengthen the RiskControl management of the portfolio while maintaining the original advantagesof smart beta has become a new issue of concern in the industry. Thispaper demonstrates the scientific validity of using a probability prediction forposition optimization through an optimization theory and proposes a novelnatural gradient boosting (NGBoost)-based portfolio optimization method,which predicts stock prices and their probability distributions based on non-Bayesian methods and maximizes the Sharpe ratio expectation of positionoptimization. This paper validates the effectiveness and practicality of themodel by using the Chinese stock market, and the experimental results showthat the proposed method in this paper can reduce the volatility by 0.08 andincrease the expected portfolio cumulative return (reaching a maximum of67.1%) compared with the mainstream methods in the industry.
基金National Natural Science Foundation of China(No.61533010)Science and Technology Commission of Shanghai Municipality,China(No.14ZR1415300)
文摘Probabilistic method requires a lot of sample information to describe the probability distributions of uncertain variables and has difficulty in dealing with the optimization problem with uncertain parameters which contains unsufficient information.To solve this problem,a robust optimization operation method based on information gap decision theory(IGDT) is presented considering the non-probabilistic uncertainties of parameters.By the proposed method the maximum resistance to the disturbance of uncertain parameters is achieved and the optimization strategies with uncertain parameters are presented.Finally,numerical simulation is performed on the modified IEEE-14 bus system.Numerical results show the effectiveness of the proposed approach.
文摘This paper addresses the high dimension sample problem in discriminate analysis under nonparametric and supervised assumptions. Since there is a kind of equivalence between the probabilistic dependence measure and the Bayes classification error probability, we propose to use an iterative algorithm to optimize the dimension reduction for classification with a probabilistic approach to achieve the Bayes classifier. The estimated probabilities of different errors encountered along the different phases of the system are realized by the Kernel estimate which is adjusted in a means of the smoothing parameter. Experiment results suggest that the proposed approach performs well.
文摘Polynomial-time randomized algorithms were constructed to approximately solve optimal robust performance controller design problems in probabilistic sense and the rigorous mathematical justification of the approach was given. The randomized algorithms here were based on a property from statistical learning theory known as (uniform) convergence of empirical means (UCEM). It is argued that in order to assess the performance of a controller as the plant varies over a pre-specified family, it is better to use the average performance of the controller as the objective function to be optimized, rather than its worst-case performance. The approach is illustrated to be efficient through an example.
基金supported by the Science and Technology Project of China South Power Grid Co.,Ltd.,Grant Nos.036000KK52222044,GDKJXM20222430。
文摘The rapid expansion of offshore wind energy necessitates robust and cost-effective electrical collector system(ECS)designs that prioritize lifetime operational reliability.Traditional optimization approaches often simplify reliability considerations or fail to holistically integrate them with economic and technical constraints.This paper introduces a novel,two-stage optimization framework for offshore wind farm(OWF)ECS planning that systematically incorporates reliability.The first stage employs Mixed-Integer Linear Programming(MILP)to determine an optimal radial network topology,considering linearized reliability approximations and geographical constraints.The second stage enhances this design by strategically placing tie-lines using a Mixed-Integer Quadratically Constrained Program(MIQCP).This stage leverages a dynamic-aware adaptation of Multi-Source Multi-Terminal Network Reliability(MSMT-NR)assessment,with its inherent nonlinear equations successfully transformed into a solvable MIQCP form for loopy networks.A benchmark case study demonstrates the framework’s efficacy,illustrating how increasing the emphasis on reliability leads to more distributed and interconnected network topologies,effectively balancing investment costs against enhanced system resilience.
基金supported by Key Technology Research and Application of Online Control Simulation and Intelligent Decision Making for Active Distribution Network(5108-202218280A-2-377-XG).
文摘Aiming at the problems of increasing uncertainty of low-carbon generation energy in active distribution network(ADN)and the difficulty of security assessment of distribution network,this paper proposes a two-phase scheduling model for flexible resources in ADN based on probabilistic risk perception.First,a full-cycle probabilistic trend sequence is constructed based on the source-load historical data,and in the day-ahead scheduling phase,the response interval of the flexibility resources on the load and storage side is optimized based on the probabilistic trend,with the probability of the security boundary as the security constraint,and with the economy as the objective.Then in the intraday phase,the core security and economic operation boundary of theADNis screened in real time.Fromthere,it quantitatively senses the degree of threat to the core security and economic operation boundary under the current source-load prediction information,and identifies the strictly secure and low/high-risk time periods.Flexibility resources within the response interval are dynamically adjusted in real-time by focusing on high-risk periods to cope with future core risks of the distribution grid.Finally,the improved IEEE 33-node distribution system is simulated to obtain the flexibility resource scheduling scheme on the load and storage side.Thescheduling results are evaluated from the perspectives of risk probability and flexible resource utilization efficiency,and the analysis shows that the scheduling model in this paper can promote the consumption of low-carbon energy from wind and photovoltaic sourceswhile reducing the operational risk of the distribution network.
文摘The probabilistic modeling is applied to calculate microstructural features of the thin complex smprolloy turbine blades cast by the vacuum investment process. The random distribution, orientation and physical mechanism of the nucleation, the growth kinetics of dendrites and the columnar-to-equiaxed transition (CET) are considered.Capitalizing on these simulating schemes, the comprehensive influence of key process variables on the scale and uniformity of grains has been involved quantitatively. The validity of the modeling is confirmed by selection of the optimum process variables.
文摘Ride and handling are two paramount factors in design and development of vehicle suspension systems. Conflicting trends in ride and handling characteristics propel engineers toward employing multi-objective optimization methods capable of providing the best trade-off designs compromising both criteria simultaneously. Although many studies have been performed on multi-objective optimization of vehicle suspension system, only a few of them have used probabilistic approaches considering effects of uncertainties in the design. However, it has been proved that optimum point obtained from deterministic optimization without taking into account the effects of uncertainties may lead to high-risk points instead of optimum ones. In this work, reliability-based robust multi-objective optimization of a 5 degree of freedom （5-DOF） vehicle suspension system is performed using method of non-dominated sorting genetic algorithm-II （NSGA-II） in conjunction with Monte Carlo simulation （MCS） to obtain best designs considering both comfort and handling. Road profile is modeled as a random function using power spectral density （PSD） which is in better accordance with reality. To accommodate the robust approach, the variance of all objective functions is also considered to be minimized. Also, to take into account the reliability criterion, a reliability-based constraint is considered in the optimization. A deterministic optimization has also been performed to compare the results with probabilistic study and some other deterministic studies in the literature. In addition, sensitivity analysis has been performed to reveal the effects of different design variables on objective functions. To introduce the best trade-off points from the obtained Pareto fronts, TOPSIS method has been employed. Results show that optimum design point obtained from probabilistic optimization in this work provides better performance while demonstrating very good reliability and robustness. However, other optimum points from deterministic optimizations violate the regarded constraints in the presence of uncertainties.
基金provided by the National Science and Technology Major Project(No.2011ZX05004-004)China National Petroleum Corporation Key Projects(No.2014E2105)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)
文摘In order to improve the fine structure inversion ability of igneous rocks for the exploration of underlying strata, based on particle swarm optimization(PSO), we have developed a method for seismic wave impedance inversion. Through numerical simulation, we tested the effects of different algorithm parameters and different model parameterization methods on PSO wave impedance inversion, and analyzed the characteristics of PSO method. Under the conclusions drawn from numerical simulation, we propose the scheme of combining a cross-moving strategy based on a divided block model and high-frequency filtering technology for PSO inversion. By analyzing the inversion results of a wedge model of a pitchout coal seam and a coal coking model with igneous rock intrusion, we discuss the vertical and horizontal resolution, stability and reliability of PSO inversion. Based on the actual seismic and logging data from an igneous area, by taking a seismic profile through wells as an example, we discuss the characteristics of three inversion methods, including model-based wave impedance inversion, multi-attribute seismic inversion based on probabilistic neural network(PNN) and wave impedance inversion based on PSO.And we draw the conclusion that the inversion based on PSO method has a better result for this igneous area.
基金funded by the National Natural Science Foundation of China (41130748, 41101162)the Key Knowledge Innovation Project of Chinese Academy of Sciences (KZCX2-EW-304)
文摘A large number of mathematical models were developed for supporting agricultural production structure optimization decisions; however, few of them can address various uncertainties existing in many factors （e.g., eco-social benefit maximization, food security, employment stability and ecosystem balance）. In this study, an interval-probabilistic agricultural production structure optimization model （IPAPSOM） is formulated for tackling uncertainty presented as discrete intervals and/or probability distribution. The developed model improves upon the existing probabilistic programming and inexact optimization approaches. The IPAPSOM considers not only food security policy constraints, but also involves rural households’income increase and eco-environmental conversation, which can effectively reflect various interrelations among different aspects in an agricultural production structure optimization system. Moreover, it can also help examine the reliability of satisfying （or risk of violating） system constraints under uncertainty. The model is applied to a real case of long-term agricultural production structure optimization in Dancheng County, which is located in Henan Province of Central China as one of the major grain producing areas. Interval solutions associated with different risk levels of constraint violation are obtained. The results are useful for generating a range of decision alternatives under various system benefit conditions, and thus helping decision makers to identify the desired agricultural production structure optimization strategy under uncertainty.
文摘Cost reduction in electric power generation is a major management concern, and it is therefore necessary to reduce maintenance expenses while upholding plant reliability. A maintenance optimization system 'FREEDOM', which uses RBM technique, DCF (discounted cash flow) and NPV (net present value) calculation functions, has been newly developed. This system probabilistically evaluates the lifetime of boiler and turbine and quantitatively calculates the risk defined as the cumulative probability of failure multiplied by the consequence of failure. Economically optimized timing of inspection and alternative countermeasure such as repair and replacement are then recommended. This system has already been applied to seven plants in Japan, and its effectiveness has been confirmed.
文摘In order to take into account the uncertainties linked to the variables in the evaluation of the statistical properties of structural response, a reliability approach with probabilistic aspect was considered. This is called the Probabilistic Transformation Method (PTM). This method is readily applicable when the function between the input and the output of the system is explicit. However, the situation is much more involved when it is necessary to perform the evaluation of implicit function between the input and the output of the system through numerical models. In this work, we propose a technique that combines Finite Element Analysis (FEA) and Probabilistic Transformation Method (PTM) to evaluate the Probability Density Function (PDF) of response where the function between the input and the output of the system is implicit. This technique is based on the numerical simulations of the Finite Element Analysis (FEA) and the Probabilistic Transformation Method (PTM) using an interface between Finite Element software and Matlab. Some problems of structures are treated in order to prove the applicability of the proposed technique. Moreover, the obtained results are compared to those obtained by the reference method of Monte Carlo. A second aim of this work is to develop an algorithm of global optimization using the local method SQP, because of its effectiveness and its rapidity of convergence. For this reason, we have combined the method SQP with the Multi start method. This developed algorithm is tested on test functions comparing with other methods such as the method of Particle Swarm Optimization (PSO). In order to test the applicability of the proposed approach, a structure is optimized under reliability constraints.
文摘Fuzzy regression analysis is an important regression analysis method to predict uncertain information in the real world. In this paper, the input data are crisp with randomness;the output data are trapezoid fuzzy number, and three different risk preferences and chaos optimization algorithm are introduced to establish fuzzy regression model. On the basis of the principle of the minimum total spread between the observed and the estimated values, risk-neutral, risk-averse, and risk-seeking fuzzy regression model are developed to obtain the parameters of fuzzy linear regression model. Chaos optimization algorithm is used to determine the digital characteristic of random variables. The mean absolute percentage error and variance of errors are adopted to compare the modeling results. A stock rating case is used to evaluate the fuzzy regression models. The comparisons with five existing methods show that our proposed method has satisfactory performance.
文摘In view of incomplete probability information multi-objective question, it used probabilistic perturbation method and Edgeworth series technique to study reliability optimization design. The first four moments of basic random variables are known under condition. It used the Ant Colony Algorithm to design cutting head roadheader, the optimized result indicated that cutting head load fluctuation and compared energy consumption were reduced obviously at the same time. This result enhanced roadheader operational reliability and energy effectively.
基金supported by the Innovation Program for Quantum Science and Technology(Grant Nos.2021ZD0301702,and 2024ZD0302000)the Natural Science Foundation of Jiangsu Province(Grant No.BK20232002)+1 种基金the National Natural Science Foundation of China(Grant Nos.U21A20436,and 12074179)the Natural Science Foundation of Shandong Province(Grant No.ZR2023LZH002)。
文摘In the noisy intermediate-scale quantum era,emerging classical-quantum hybrid optimization algorithms,such as variational quantum algorithms(VQAs),can leverage the unique characteristics of quantum devices to accelerate computations tailored to specific problems with shallow circuits.However,these algorithms encounter biases and iteration difficulties due to significant noise in quantum processors.These difficulties can only be partially addressed without error correction by optimizing hardware,reducing circuit complexity,or fitting and extrapolating.A compelling solution is applying probabilistic error cancellation(PEC),a quantum error mitigation technique that enables unbiased results without full error correction.Traditional PEC is challenging to apply in VQAs due to its variance amplification,contradicting iterative process assumptions.This paper proposes a novel noise-adaptable strategy that combines PEC with the quantum approximate optimization algorithm(QAOA).It is implemented through invariant sampling circuits(invariant-PEC,or IPEC)and substantially reduces iteration variance.This strategy marks the first successful integration of PEC and QAOA,resulting in efficient convergence.Moreover,we introduce adaptive partial PEC(APPEC),which modulates the error cancellation proportion of IPEC during iteration.We experimentally validate this technique on a superconducting quantum processor,cutting sampling cost by 90.1%.Notably,we find that dynamic adjustments of error levels via APPEC can enhance the ability to escape from local minima and reduce sampling costs.These results open promising avenues for executing VQAs with large-scale,low-noise quantum circuits,paving the way for practical quantum computing advancements.
