The lack of systematic and scientific top-level arrangement in the field of civil aircraft flight test leads to the problems of long duration and high cost.Based on the flight test activity,mathematical models of flig...The lack of systematic and scientific top-level arrangement in the field of civil aircraft flight test leads to the problems of long duration and high cost.Based on the flight test activity,mathematical models of flight test duration and cost are established to set up the framework of flight test process.The top-level arrangement for flight test is optimized by multi-objective algorithm to reduce the duration and cost of flight test.In order to verify the necessity and validity of the mathematical models and the optimization algorithm of top-level arrangement,real flight test data is used to make an example calculation.Results show that the multi-objective optimization results of the top-level flight arrangement are better than the initial arrangement data,which can shorten the duration,reduce the cost,and improve the efficiency of flight test.展开更多
The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy b...The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy but ignore the interpretability.The single-objective optimization strategy has been applied in the interpretability-accuracy trade-off by inte-grating accuracy and interpretability into an optimization objec-tive.But the integration has a greater impact on optimization results with strong subjectivity.Thus,a multi-objective optimiza-tion framework in the modeling of BRB systems with inter-pretability-accuracy trade-off is proposed in this paper.Firstly,complexity and accuracy are taken as two independent opti-mization goals,and uniformity as a constraint to give the mathe-matical description.Secondly,a classical multi-objective opti-mization algorithm,nondominated sorting genetic algorithm II(NSGA-II),is utilized as an optimization tool to give a set of BRB systems with different accuracy and complexity.Finally,a pipeline leakage detection case is studied to verify the feasibility and effectiveness of the developed multi-objective optimization.The comparison illustrates that the proposed multi-objective optimization framework can effectively avoid the subjectivity of single-objective optimization,and has capability of joint optimiz-ing the structure and parameters of BRB systems with inter-pretability-accuracy trade-off.展开更多
For the deep understanding on combustion of ammonia/diesel,this study develops a reduced mechanism of ammonia/diesel with 227 species and 937 reactions.The sub-mechanism on ammonia/interactions of N-based and C-based ...For the deep understanding on combustion of ammonia/diesel,this study develops a reduced mechanism of ammonia/diesel with 227 species and 937 reactions.The sub-mechanism on ammonia/interactions of N-based and C-based species(N—C)/NOx is optimized using the Non-dominated Sorting Genetic Algorithm II(NSGA-II)with 200 generations.The optimized mechanism(named as 937b)is validated against combustion characteristics of ammonia/methane(which is used to examine the accuracy of N—C interactions)and ammonia/diesel blends.The ignition delay times(IDTs),the laminar flame speeds and most of key intermediate species during the combustion of ammonia/methane blends can be accurately simulated by 937b under a wide range of conditions.As for ammonia/diesel blends with various diesel energy fractions,reasonable predictions on the IDTs under pressures from 1.0 MPa to5.0 MPa as well as the laminar flame speeds are also achieved by 937b.In particular,with regard to the IDT simulations of ammonia/diesel blends,937b makes progress in both aspects of overall accuracy and computational efficiency,compared to a detailed ammonia/diesel mechanism.Further kinetic analysis reveals that the reaction pathway of ammonia during the combustion of ammonia/diesel blend mainly differs in the tendencies of oxygen additions to NH_2 and NH with different equivalence ratios.展开更多
In the process of launching guided projectile under the conventional system, it is difficult to effectively obtain the precise navigation parameters of the projectile in the high dynamic environment. Aiming at this pr...In the process of launching guided projectile under the conventional system, it is difficult to effectively obtain the precise navigation parameters of the projectile in the high dynamic environment. Aiming at this problem, this paper describes a new system of guided ammunition based on tail spin reduction. After analyzing the mechanism of the ammunition's tail spin reduction, a navigation method of large scale difference tail control simple guided ammunition based on speed constraint is proposed. In this method,the corresponding navigation constraints can be carried out by combining the rotation speed state of the ammunition itself, and the optimal solution of navigation parameters during the flight of the missile can be obtained by Extended Kalman Filter(EKF). Finally, the performance of the proposed method was verified by the simulation environment, and the hardware-in-the-loop simulation test and flight test were carried out to verify the performance of the method in the real environment. The experimental results show that the proposed method can achieve the optimal estimation of navigation parameters for simple guided ammunition with large-scale difference tail control. Under the conditions of simulation test and hardware-in-loop simulation test, the position and velocity errors calculated by the method in this paper converged. Under the condition of flight test, the spatial average error calculated by the method described in this paper is 6.17 m, and the spatial error of the final landing point is 3.50 m.Through this method, the accurate acquisition of navigation parameters in the process of projectile launching is effectively realized.展开更多
Multi-objective optimization(MOO)for the microwave metamaterial absorber(MMA)normally adopts evolutionary algo-rithms,and these optimization algorithms require many objec-tive function evaluations.To remedy this issue...Multi-objective optimization(MOO)for the microwave metamaterial absorber(MMA)normally adopts evolutionary algo-rithms,and these optimization algorithms require many objec-tive function evaluations.To remedy this issue,a surrogate-based MOO algorithm is proposed in this paper where Kriging models are employed to approximate objective functions.An efficient sampling strategy is presented to sequentially capture promising samples in the design region for exact evaluations.Firstly,new sample points are generated by the MOO on surro-gate models.Then,new samples are captured by exploiting each objective function.Furthermore,a weighted sum of the improvement of hypervolume(IHV)and the distance to sampled points is calculated to select the new sample.Compared with two well-known MOO algorithms,the proposed algorithm is vali-dated by benchmark problems.In addition,two broadband MMAs are applied to verify the feasibility and efficiency of the proposed algorithm.展开更多
In the existing impact time control guidance (ITCG) laws for moving-targets, the effects of time-varying velocity caused by aerodynamics and gravity cannot be effectively con-sidered. Therefore, an ITCG with field-of-...In the existing impact time control guidance (ITCG) laws for moving-targets, the effects of time-varying velocity caused by aerodynamics and gravity cannot be effectively con-sidered. Therefore, an ITCG with field-of-view (FOV) constraints based on biased proportional navigation guidance (PNG) is developed in this paper. The remaining flight time (time-to-go) estimation method is derived considering aerodynamic force and gravity. The number of differential equations is reduced and the integration step is increased by changing the integral variable, which makes it possible to obtain time-to-go through integration. An impact time controller with FOV constraints is proposed by analyzing the influence of the biased term on time-to-go and FOV constraint. Then, numerical simulations are performed to verify the correctness and superiority of the method.展开更多
The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper coo...The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper cooperative task allocation is superior to over the single UAV.Accordingly,several constraints should be satisfied to realize the efficient cooperation,such as special time-window,variant equipment,specified execution sequence.Hence,a proper task allocation in UAVs is the crucial point for the final success.The task allocation problem of the heterogeneous UAVs can be formulated as a multi-objective optimization problem coupled with the UAV dynamics.To this end,a multi-layer encoding strategy and a constraint scheduling method are designed to handle the critical logical and physical constraints.In addition,four optimization objectives:completion time,target reward,UAV damage,and total range,are introduced to evaluate various allocation plans.Subsequently,to efficiently solve the multi-objective optimization problem,an improved multi-objective quantum-behaved particle swarm optimization(IMOQPSO)algorithm is proposed.During this algorithm,a modified solution evaluation method is designed to guide algorithmic evolution;both the convergence and distribution of particles are considered comprehensively;and boundary solutions which may produce some special allocation plans are preserved.Moreover,adaptive parameter control and mixed update mechanism are also introduced in this algorithm.Finally,both the proposed model and algorithm are verified by simulation experiments.展开更多
In this paper, a new adaptive optimal guidance law with impact angle and seeker’s field-of-view(FOV) angle constraints is proposed. To this end, the generalized optimal guidance law is derived first. A changeable imp...In this paper, a new adaptive optimal guidance law with impact angle and seeker’s field-of-view(FOV) angle constraints is proposed. To this end, the generalized optimal guidance law is derived first. A changeable impact angle weighting(IAW) coefficient is introduced and used to modify the guidance law to make it adaptive for all guidance constraints. After integrating the closed-form solution of the guidance command with linearized engagement kinematics, the analytic predictive models of impact angle and FOV angle are built, and the available range of IAW corresponding to constraints is certain. Next, a calculation scheme is presented to acquire the real-time value of IAW during the entire guidance process. When applying the proposed guidance law, the IAW will keep small to avoid a trajectory climbing up to limit FOV angle at an initial time but will increase with the closing target to improve impact position and angle accuracy, thereby ensuring that the guidance law can juggle orders of guidance accuracy and constraints control.展开更多
When the training data are insufficient, especially when only a small sample size of data is available, domain knowledge will be taken into the process of learning parameters to improve the performance of the Bayesian...When the training data are insufficient, especially when only a small sample size of data is available, domain knowledge will be taken into the process of learning parameters to improve the performance of the Bayesian networks. In this paper, a new monotonic constraint model is proposed to represent a type of common domain knowledge. And then, the monotonic constraint estimation algorithm is proposed to learn the parameters with the monotonic constraint model. In order to demonstrate the superiority of the proposed algorithm, series of experiments are carried out. The experiment results show that the proposed algorithm is able to obtain more accurate parameters compared to some existing algorithms while the complexity is not the highest.展开更多
Constraint-based multicast routing, which aims at identifying a path that satisfies a set of quality of service (QoS) constraints, has became a very important research issue in the areas of networks and distributed sy...Constraint-based multicast routing, which aims at identifying a path that satisfies a set of quality of service (QoS) constraints, has became a very important research issue in the areas of networks and distributed systems. In general, multi-constrained path selection with or without optimization is a NP-complete problem that can not be exactly solved in polynomial time. Hence, accurate constraints-based routing algorithms with a fast running time are scarce, perhaps even non-existent. The expected impact of such a constrained-based routing algorithm has resulted in the proposal of numerous heuristics and a few exact QoS algorithms. This paper aims to give a thorough, concise and fair evaluation of the most important multiple constraint-based QoS multicast routing algorithms known today, and it provides a descriptive overview and simulation results of these multi-constrained routing algorithms.展开更多
The accurate detection of cooperative targets plays a key and foundational role in unmanned aerial vehicle (UAV) landing autonomously. The standard method based on fixed threshold is too susceptible to both illuminati...The accurate detection of cooperative targets plays a key and foundational role in unmanned aerial vehicle (UAV) landing autonomously. The standard method based on fixed threshold is too susceptible to both illumination variations and interference. To overcome issues above, a robust detection algorithm with triple constraints for cooperative targets based on spectral residual (TCSR) is proposed. Firstly, by designing an asymmetric cooperative target, which comprises red background, green H and triangle target, the captured original image is converted into a Lab color space, whose saliency map is yielded by constructing the spectral residual. Then, the triple constraints are developed according to the prior knowledge of the cooperative target. Finally, the salient region in saliency map is considered as the cooperative target, and it meets the triple constraints. Experimental results in complex environments show that the proposed TCSR outperforms the standard methods in higher detection accuracy and lower false alarm rate.展开更多
A mathematical mechanism model was proposed for the description and analysis of the heat-stirring-acid leaching process.The model is proved to be effective by experiment.Afterwards,the leaching problem was formulated ...A mathematical mechanism model was proposed for the description and analysis of the heat-stirring-acid leaching process.The model is proved to be effective by experiment.Afterwards,the leaching problem was formulated as a constrained multi-objective optimization problem based on the mechanism model.A two-stage guide multi-objective particle swarm optimization(TSG-MOPSO) algorithm was proposed to solve this optimization problem,which can accelerate the convergence and guarantee the diversity of pareto-optimal front set as well.Computational experiment was conducted to compare the solution by the proposed algorithm with SIGMA-MOPSO by solving the model and with the manual solution in practice.The results indicate that the proposed algorithm shows better performance than SIGMA-MOPSO,and can improve the current manual solutions significantly.The improvements of production time and economic benefit compared with manual solutions are 10.5% and 7.3%,respectively.展开更多
In this paper, we propose a modified evolutionary programming with dynamic domain for solving nonlinear IP/MIP problems with linear constraints, without involving penalty function or any transformation for the problem...In this paper, we propose a modified evolutionary programming with dynamic domain for solving nonlinear IP/MIP problems with linear constraints, without involving penalty function or any transformation for the problem to a linear model or others. The numerical results show that the new algorithm gives a satisfactory performance in which it works of high speed, and accuracy in IP/MIP problems.展开更多
The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency...The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency. A multi-objective model was presented for the material distribution routing problem in mixed manufacturing systems, and it was solved by a hybrid multi-objective evolutionary algorithm (HMOEA). The characteristics of the HMOEA are as follows: 1) A route pool is employed to preserve the best routes for the population initiation; 2) A specialized best?worst route crossover (BWRC) mode is designed to perform the crossover operators for selecting the best route from Chromosomes 1 to exchange with the worst one in Chromosomes 2, so that the better genes are inherited to the offspring; 3) A route swap mode is used to perform the mutation for improving the convergence speed and preserving the better gene; 4) Local heuristics search methods are applied in this algorithm. Computational study of a practical case shows that the proposed algorithm can decrease the total travel distance by 51.66%, enhance the average vehicle load rate by 37.85%, cut down 15 routes and reduce a deliver vehicle. The convergence speed of HMOEA is faster than that of famous NSGA-II.展开更多
Unmanned aerial vehicle(UAV)was introduced as a novel traffic device to collect road traffic information and its cruise route planning problem was considered.Firstly,a multi-objective optimization model was proposed a...Unmanned aerial vehicle(UAV)was introduced as a novel traffic device to collect road traffic information and its cruise route planning problem was considered.Firstly,a multi-objective optimization model was proposed aiming at minimizing the total cruise distance and the number of UAVs used,which used UAV maximum cruise distance,the number of UAVs available and time window of each monitored target as constraints.Then,a novel multi-objective evolutionary algorithm was proposed.Next,a case study with three time window scenarios was implemented.The results show that both the total cruise distance and the number of UAVs used continue to increase with the time window constraint becoming narrower.Compared with the initial optimal solutions,the optimal total cruise distance and the number of UAVs used fall by an average of 30.93% and 31.74%,respectively.Finally,some concerns using UAV to collect road traffic information were discussed.展开更多
This paper addresses the problem of fault detection（FD） for networked systems with access constraints and packet dropouts.Two independent Markov chains are used to describe the sequences of channels which are availa...This paper addresses the problem of fault detection（FD） for networked systems with access constraints and packet dropouts.Two independent Markov chains are used to describe the sequences of channels which are available for communication at an instant and the packet dropout process,respectively.Performance indexes H∞ and H_ are introduced to describe the robustness of residual against external disturbances and sensitivity of residual to faults,respectively.By using a mode-dependent fault detection filter（FDF） as residual generator,the addressed FD problem is converted into an auxiliary filter design problem with the above index constraints.A sufficient condition for the existence of the FDF is derived in terms of certain linear matrix inequalities（LMIs）.When these LMIs are feasible,the explicit expression of the desired FDF can also be characterized.A numerical example is exploited to show the usefulness of the proposed results.展开更多
To solve the scheduling problem of dual-armed cluster tools for wafer fabrications with residency time and reentrant constraints,a heuristic scheduling algorithm was developed.Firstly,on the basis of formulating sched...To solve the scheduling problem of dual-armed cluster tools for wafer fabrications with residency time and reentrant constraints,a heuristic scheduling algorithm was developed.Firstly,on the basis of formulating scheduling problems domain of dual-armed cluster tools,a non-integer programming model was set up with a minimizing objective function of the makespan.Combining characteristics of residency time and reentrant constraints,a scheduling algorithm of searching the optimal operation path of dual-armed transport module was presented under many kinds of robotic scheduling paths for dual-armed cluster tools.Finally,the experiments were designed to evaluate the proposed algorithm.The results show that the proposed algorithm is feasible and efficient for obtaining an optimal scheduling solution of dual-armed cluster tools with residency time and reentrant constraints.展开更多
To obtain the optimal process parameters of stamping forming, finite element analysis and optimization technique were integrated via transforming multi-objective issue into a single-objective issue. A Pareto-based gen...To obtain the optimal process parameters of stamping forming, finite element analysis and optimization technique were integrated via transforming multi-objective issue into a single-objective issue. A Pareto-based genetic algorithm was applied to optimizing the head stamping forming process. In the proposed optimal model, fracture, wrinkle and thickness varying are a function of several factors, such as fillet radius, draw-bead position, blank size and blank-holding force. Hence, it is necessary to investigate the relationship between the objective functions and the variables in order to make objective functions varying minimized simultaneously. Firstly, the central composite experimental(CCD) with four factors and five levels was applied, and the experimental data based on the central composite experimental were acquired. Then, the response surface model(RSM) was set up and the results of the analysis of variance(ANOVA) show that it is reliable to predict the fracture, wrinkle and thickness varying functions by the response surface model. Finally, a Pareto-based genetic algorithm was used to find out a set of Pareto front, which makes fracture, wrinkle and thickness varying minimized integrally. A head stamping case indicates that the present method has higher precision and practicability compared with the "trial and error" procedure.展开更多
A decision support system, including a multi-objective optimization framework and a multi-attribute decision making approach is proposed for satellite equipment layout. Firstly, given three objectives (to minimize the...A decision support system, including a multi-objective optimization framework and a multi-attribute decision making approach is proposed for satellite equipment layout. Firstly, given three objectives (to minimize the C.G. offset, the cross moments of inertia and the space debris impact risk), we develop a threedimensional layout optimization model. Unlike most of the previous works just focusing on mass characteristics of the system, a space debris impact risk index is developed. Secondly, we develop an efficient optimization framework for the integration of computer-aided design (CAD) software as well as the optimization algorithm to obtain the Pareto front of the layout optimization problem. Thirdly, after obtaining the candidate solutions, we present a multi-attribute decision making approach, which integrates the smart Pareto filter and the correlation coefficient and standard deviation (CCSD) method to select the best tradeoff solutions on the optimal Pareto fronts. Finally, the framework and the decision making approach are applied to a case study of a satellite platform.展开更多
In terms of tandem cold mill productivity and product quality, a multi-objective optimization model of rolling schedule based on cost fimction was proposed to determine the stand reductions, inter-stand tensions and r...In terms of tandem cold mill productivity and product quality, a multi-objective optimization model of rolling schedule based on cost fimction was proposed to determine the stand reductions, inter-stand tensions and rolling speeds for a specified product. The proposed schedule optimization model consists of several single cost fi.mctions, which take rolling force, motor power, inter-stand tension and stand reduction into consideration. The cost function, which can evaluate how far the rolling parameters are from the ideal values, was minimized using the Nelder-Mead simplex method. The proposed rolling schedule optimization method has been applied successfully to the 5-stand tandem cold mill in Tangsteel, and the results from a case study show that the proposed method is superior to those based on empirical formulae.展开更多
基金supported by the National Natural Science Foundation of China(62073267,61903305)the Fundamental Research Funds for the Central Universities(HXGJXM202214).
文摘The lack of systematic and scientific top-level arrangement in the field of civil aircraft flight test leads to the problems of long duration and high cost.Based on the flight test activity,mathematical models of flight test duration and cost are established to set up the framework of flight test process.The top-level arrangement for flight test is optimized by multi-objective algorithm to reduce the duration and cost of flight test.In order to verify the necessity and validity of the mathematical models and the optimization algorithm of top-level arrangement,real flight test data is used to make an example calculation.Results show that the multi-objective optimization results of the top-level flight arrangement are better than the initial arrangement data,which can shorten the duration,reduce the cost,and improve the efficiency of flight test.
基金supported by the National Natural Science Foundation of China(71901212)the Science and Technology Innovation Program of Hunan Province(2020RC4046).
文摘The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy but ignore the interpretability.The single-objective optimization strategy has been applied in the interpretability-accuracy trade-off by inte-grating accuracy and interpretability into an optimization objec-tive.But the integration has a greater impact on optimization results with strong subjectivity.Thus,a multi-objective optimiza-tion framework in the modeling of BRB systems with inter-pretability-accuracy trade-off is proposed in this paper.Firstly,complexity and accuracy are taken as two independent opti-mization goals,and uniformity as a constraint to give the mathe-matical description.Secondly,a classical multi-objective opti-mization algorithm,nondominated sorting genetic algorithm II(NSGA-II),is utilized as an optimization tool to give a set of BRB systems with different accuracy and complexity.Finally,a pipeline leakage detection case is studied to verify the feasibility and effectiveness of the developed multi-objective optimization.The comparison illustrates that the proposed multi-objective optimization framework can effectively avoid the subjectivity of single-objective optimization,and has capability of joint optimiz-ing the structure and parameters of BRB systems with inter-pretability-accuracy trade-off.
基金the National Natural Science Foundation of China(project code:52202470)Jilin Province Natural Science Foundation(project codes:20220101205JC,20220101212JC)+2 种基金Jilin Province Specific Project of Industrial Technology Research&Development(project code:2020C025-2)2021 Interdisciplinary Integration and Innovation Project of Jilin University(project code:XJRCYB07)Free Exploration Project of Changsha Automotive Innovation Research Institute of Jilin University(project code:CAIRIZT20220202)。
文摘For the deep understanding on combustion of ammonia/diesel,this study develops a reduced mechanism of ammonia/diesel with 227 species and 937 reactions.The sub-mechanism on ammonia/interactions of N-based and C-based species(N—C)/NOx is optimized using the Non-dominated Sorting Genetic Algorithm II(NSGA-II)with 200 generations.The optimized mechanism(named as 937b)is validated against combustion characteristics of ammonia/methane(which is used to examine the accuracy of N—C interactions)and ammonia/diesel blends.The ignition delay times(IDTs),the laminar flame speeds and most of key intermediate species during the combustion of ammonia/methane blends can be accurately simulated by 937b under a wide range of conditions.As for ammonia/diesel blends with various diesel energy fractions,reasonable predictions on the IDTs under pressures from 1.0 MPa to5.0 MPa as well as the laminar flame speeds are also achieved by 937b.In particular,with regard to the IDT simulations of ammonia/diesel blends,937b makes progress in both aspects of overall accuracy and computational efficiency,compared to a detailed ammonia/diesel mechanism.Further kinetic analysis reveals that the reaction pathway of ammonia during the combustion of ammonia/diesel blend mainly differs in the tendencies of oxygen additions to NH_2 and NH with different equivalence ratios.
基金supported by the Natural Science Foundation of Beijing Municipality(Grant No.4212003)the Crossdisciplinary Collaboration Project of Beijing Municipal Science and Technology New Star Program(Grant No.202111)。
文摘In the process of launching guided projectile under the conventional system, it is difficult to effectively obtain the precise navigation parameters of the projectile in the high dynamic environment. Aiming at this problem, this paper describes a new system of guided ammunition based on tail spin reduction. After analyzing the mechanism of the ammunition's tail spin reduction, a navigation method of large scale difference tail control simple guided ammunition based on speed constraint is proposed. In this method,the corresponding navigation constraints can be carried out by combining the rotation speed state of the ammunition itself, and the optimal solution of navigation parameters during the flight of the missile can be obtained by Extended Kalman Filter(EKF). Finally, the performance of the proposed method was verified by the simulation environment, and the hardware-in-the-loop simulation test and flight test were carried out to verify the performance of the method in the real environment. The experimental results show that the proposed method can achieve the optimal estimation of navigation parameters for simple guided ammunition with large-scale difference tail control. Under the conditions of simulation test and hardware-in-loop simulation test, the position and velocity errors calculated by the method in this paper converged. Under the condition of flight test, the spatial average error calculated by the method described in this paper is 6.17 m, and the spatial error of the final landing point is 3.50 m.Through this method, the accurate acquisition of navigation parameters in the process of projectile launching is effectively realized.
基金supported by the National Key Research and Development Program(2021YFB3502500).
文摘Multi-objective optimization(MOO)for the microwave metamaterial absorber(MMA)normally adopts evolutionary algo-rithms,and these optimization algorithms require many objec-tive function evaluations.To remedy this issue,a surrogate-based MOO algorithm is proposed in this paper where Kriging models are employed to approximate objective functions.An efficient sampling strategy is presented to sequentially capture promising samples in the design region for exact evaluations.Firstly,new sample points are generated by the MOO on surro-gate models.Then,new samples are captured by exploiting each objective function.Furthermore,a weighted sum of the improvement of hypervolume(IHV)and the distance to sampled points is calculated to select the new sample.Compared with two well-known MOO algorithms,the proposed algorithm is vali-dated by benchmark problems.In addition,two broadband MMAs are applied to verify the feasibility and efficiency of the proposed algorithm.
基金supported by the National Natural Science Foundation of China(U21B2028).
文摘In the existing impact time control guidance (ITCG) laws for moving-targets, the effects of time-varying velocity caused by aerodynamics and gravity cannot be effectively con-sidered. Therefore, an ITCG with field-of-view (FOV) constraints based on biased proportional navigation guidance (PNG) is developed in this paper. The remaining flight time (time-to-go) estimation method is derived considering aerodynamic force and gravity. The number of differential equations is reduced and the integration step is increased by changing the integral variable, which makes it possible to obtain time-to-go through integration. An impact time controller with FOV constraints is proposed by analyzing the influence of the biased term on time-to-go and FOV constraint. Then, numerical simulations are performed to verify the correctness and superiority of the method.
基金Project(61801495)supported by the National Natural Science Foundation of China
文摘The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper cooperative task allocation is superior to over the single UAV.Accordingly,several constraints should be satisfied to realize the efficient cooperation,such as special time-window,variant equipment,specified execution sequence.Hence,a proper task allocation in UAVs is the crucial point for the final success.The task allocation problem of the heterogeneous UAVs can be formulated as a multi-objective optimization problem coupled with the UAV dynamics.To this end,a multi-layer encoding strategy and a constraint scheduling method are designed to handle the critical logical and physical constraints.In addition,four optimization objectives:completion time,target reward,UAV damage,and total range,are introduced to evaluate various allocation plans.Subsequently,to efficiently solve the multi-objective optimization problem,an improved multi-objective quantum-behaved particle swarm optimization(IMOQPSO)algorithm is proposed.During this algorithm,a modified solution evaluation method is designed to guide algorithmic evolution;both the convergence and distribution of particles are considered comprehensively;and boundary solutions which may produce some special allocation plans are preserved.Moreover,adaptive parameter control and mixed update mechanism are also introduced in this algorithm.Finally,both the proposed model and algorithm are verified by simulation experiments.
基金supported by the Aeronautical Science Foundation of China(20150172001)
文摘In this paper, a new adaptive optimal guidance law with impact angle and seeker’s field-of-view(FOV) angle constraints is proposed. To this end, the generalized optimal guidance law is derived first. A changeable impact angle weighting(IAW) coefficient is introduced and used to modify the guidance law to make it adaptive for all guidance constraints. After integrating the closed-form solution of the guidance command with linearized engagement kinematics, the analytic predictive models of impact angle and FOV angle are built, and the available range of IAW corresponding to constraints is certain. Next, a calculation scheme is presented to acquire the real-time value of IAW during the entire guidance process. When applying the proposed guidance law, the IAW will keep small to avoid a trajectory climbing up to limit FOV angle at an initial time but will increase with the closing target to improve impact position and angle accuracy, thereby ensuring that the guidance law can juggle orders of guidance accuracy and constraints control.
基金supported by the National Natural Science Foundation of China(6130513361573285)the Fundamental Research Funds for the Central Universities(3102016CG002)
文摘When the training data are insufficient, especially when only a small sample size of data is available, domain knowledge will be taken into the process of learning parameters to improve the performance of the Bayesian networks. In this paper, a new monotonic constraint model is proposed to represent a type of common domain knowledge. And then, the monotonic constraint estimation algorithm is proposed to learn the parameters with the monotonic constraint model. In order to demonstrate the superiority of the proposed algorithm, series of experiments are carried out. The experiment results show that the proposed algorithm is able to obtain more accurate parameters compared to some existing algorithms while the complexity is not the highest.
文摘Constraint-based multicast routing, which aims at identifying a path that satisfies a set of quality of service (QoS) constraints, has became a very important research issue in the areas of networks and distributed systems. In general, multi-constrained path selection with or without optimization is a NP-complete problem that can not be exactly solved in polynomial time. Hence, accurate constraints-based routing algorithms with a fast running time are scarce, perhaps even non-existent. The expected impact of such a constrained-based routing algorithm has resulted in the proposal of numerous heuristics and a few exact QoS algorithms. This paper aims to give a thorough, concise and fair evaluation of the most important multiple constraint-based QoS multicast routing algorithms known today, and it provides a descriptive overview and simulation results of these multi-constrained routing algorithms.
基金supported by the National Natural Science Foundation of China(61135001)the Scientific Research Program of Shaanxi Provincial Department of Education(16JK1499)+2 种基金the Doctoral Fund of Xi’an University of Science and Technology(2015QDJ007)the Cultivation of Xi’an University of Science and Technology(2014015)the Ministry of Education Key Laboratory of Information Fusion Technology(LIFT2015-G-1)
文摘The accurate detection of cooperative targets plays a key and foundational role in unmanned aerial vehicle (UAV) landing autonomously. The standard method based on fixed threshold is too susceptible to both illumination variations and interference. To overcome issues above, a robust detection algorithm with triple constraints for cooperative targets based on spectral residual (TCSR) is proposed. Firstly, by designing an asymmetric cooperative target, which comprises red background, green H and triangle target, the captured original image is converted into a Lab color space, whose saliency map is yielded by constructing the spectral residual. Then, the triple constraints are developed according to the prior knowledge of the cooperative target. Finally, the salient region in saliency map is considered as the cooperative target, and it meets the triple constraints. Experimental results in complex environments show that the proposed TCSR outperforms the standard methods in higher detection accuracy and lower false alarm rate.
基金Project(2006AA060201) supported by the National High Technology Research and Development Program of China
文摘A mathematical mechanism model was proposed for the description and analysis of the heat-stirring-acid leaching process.The model is proved to be effective by experiment.Afterwards,the leaching problem was formulated as a constrained multi-objective optimization problem based on the mechanism model.A two-stage guide multi-objective particle swarm optimization(TSG-MOPSO) algorithm was proposed to solve this optimization problem,which can accelerate the convergence and guarantee the diversity of pareto-optimal front set as well.Computational experiment was conducted to compare the solution by the proposed algorithm with SIGMA-MOPSO by solving the model and with the manual solution in practice.The results indicate that the proposed algorithm shows better performance than SIGMA-MOPSO,and can improve the current manual solutions significantly.The improvements of production time and economic benefit compared with manual solutions are 10.5% and 7.3%,respectively.
基金This project was supported by the National Natural Science Foundation of China.
文摘In this paper, we propose a modified evolutionary programming with dynamic domain for solving nonlinear IP/MIP problems with linear constraints, without involving penalty function or any transformation for the problem to a linear model or others. The numerical results show that the new algorithm gives a satisfactory performance in which it works of high speed, and accuracy in IP/MIP problems.
基金Project(50775089)supported by the National Natural Science Foundation of ChinaProject(2007AA04Z190,2009AA043301)supported by the National High Technology Research and Development Program of ChinaProject(2005CB724100)supported by the National Basic Research Program of China
文摘The material distribution routing problem in the manufacturing system is a complex combinatorial optimization problem and its main task is to deliver materials to the working stations with low cost and high efficiency. A multi-objective model was presented for the material distribution routing problem in mixed manufacturing systems, and it was solved by a hybrid multi-objective evolutionary algorithm (HMOEA). The characteristics of the HMOEA are as follows: 1) A route pool is employed to preserve the best routes for the population initiation; 2) A specialized best?worst route crossover (BWRC) mode is designed to perform the crossover operators for selecting the best route from Chromosomes 1 to exchange with the worst one in Chromosomes 2, so that the better genes are inherited to the offspring; 3) A route swap mode is used to perform the mutation for improving the convergence speed and preserving the better gene; 4) Local heuristics search methods are applied in this algorithm. Computational study of a practical case shows that the proposed algorithm can decrease the total travel distance by 51.66%, enhance the average vehicle load rate by 37.85%, cut down 15 routes and reduce a deliver vehicle. The convergence speed of HMOEA is faster than that of famous NSGA-II.
基金Project(2009AA11Z220)supported by the National High Technology Research and Development Program of China
文摘Unmanned aerial vehicle(UAV)was introduced as a novel traffic device to collect road traffic information and its cruise route planning problem was considered.Firstly,a multi-objective optimization model was proposed aiming at minimizing the total cruise distance and the number of UAVs used,which used UAV maximum cruise distance,the number of UAVs available and time window of each monitored target as constraints.Then,a novel multi-objective evolutionary algorithm was proposed.Next,a case study with three time window scenarios was implemented.The results show that both the total cruise distance and the number of UAVs used continue to increase with the time window constraint becoming narrower.Compared with the initial optimal solutions,the optimal total cruise distance and the number of UAVs used fall by an average of 30.93% and 31.74%,respectively.Finally,some concerns using UAV to collect road traffic information were discussed.
基金supported by the National Natural Science Foundation of China (6057408860874053)
文摘This paper addresses the problem of fault detection（FD） for networked systems with access constraints and packet dropouts.Two independent Markov chains are used to describe the sequences of channels which are available for communication at an instant and the packet dropout process,respectively.Performance indexes H∞ and H_ are introduced to describe the robustness of residual against external disturbances and sensitivity of residual to faults,respectively.By using a mode-dependent fault detection filter（FDF） as residual generator,the addressed FD problem is converted into an auxiliary filter design problem with the above index constraints.A sufficient condition for the existence of the FDF is derived in terms of certain linear matrix inequalities（LMIs）.When these LMIs are feasible,the explicit expression of the desired FDF can also be characterized.A numerical example is exploited to show the usefulness of the proposed results.
基金Projects(7107111561273035)supported by the National Natural Science Foundation of China
文摘To solve the scheduling problem of dual-armed cluster tools for wafer fabrications with residency time and reentrant constraints,a heuristic scheduling algorithm was developed.Firstly,on the basis of formulating scheduling problems domain of dual-armed cluster tools,a non-integer programming model was set up with a minimizing objective function of the makespan.Combining characteristics of residency time and reentrant constraints,a scheduling algorithm of searching the optimal operation path of dual-armed transport module was presented under many kinds of robotic scheduling paths for dual-armed cluster tools.Finally,the experiments were designed to evaluate the proposed algorithm.The results show that the proposed algorithm is feasible and efficient for obtaining an optimal scheduling solution of dual-armed cluster tools with residency time and reentrant constraints.
基金Project(2012ZX04010-081) supported by the National Science and Technology Major Project of the Ministry of Science and Technology of China
文摘To obtain the optimal process parameters of stamping forming, finite element analysis and optimization technique were integrated via transforming multi-objective issue into a single-objective issue. A Pareto-based genetic algorithm was applied to optimizing the head stamping forming process. In the proposed optimal model, fracture, wrinkle and thickness varying are a function of several factors, such as fillet radius, draw-bead position, blank size and blank-holding force. Hence, it is necessary to investigate the relationship between the objective functions and the variables in order to make objective functions varying minimized simultaneously. Firstly, the central composite experimental(CCD) with four factors and five levels was applied, and the experimental data based on the central composite experimental were acquired. Then, the response surface model(RSM) was set up and the results of the analysis of variance(ANOVA) show that it is reliable to predict the fracture, wrinkle and thickness varying functions by the response surface model. Finally, a Pareto-based genetic algorithm was used to find out a set of Pareto front, which makes fracture, wrinkle and thickness varying minimized integrally. A head stamping case indicates that the present method has higher precision and practicability compared with the "trial and error" procedure.
基金supported by the National Natural Science Foundation of China(51405499)
文摘A decision support system, including a multi-objective optimization framework and a multi-attribute decision making approach is proposed for satellite equipment layout. Firstly, given three objectives (to minimize the C.G. offset, the cross moments of inertia and the space debris impact risk), we develop a threedimensional layout optimization model. Unlike most of the previous works just focusing on mass characteristics of the system, a space debris impact risk index is developed. Secondly, we develop an efficient optimization framework for the integration of computer-aided design (CAD) software as well as the optimization algorithm to obtain the Pareto front of the layout optimization problem. Thirdly, after obtaining the candidate solutions, we present a multi-attribute decision making approach, which integrates the smart Pareto filter and the correlation coefficient and standard deviation (CCSD) method to select the best tradeoff solutions on the optimal Pareto fronts. Finally, the framework and the decision making approach are applied to a case study of a satellite platform.
基金Project(51074051)supported by the National Natural Science Foundation of ChinaProject(N110307001)supported by the Fundamental Research Funds for the Central Universities,China
文摘In terms of tandem cold mill productivity and product quality, a multi-objective optimization model of rolling schedule based on cost fimction was proposed to determine the stand reductions, inter-stand tensions and rolling speeds for a specified product. The proposed schedule optimization model consists of several single cost fi.mctions, which take rolling force, motor power, inter-stand tension and stand reduction into consideration. The cost function, which can evaluate how far the rolling parameters are from the ideal values, was minimized using the Nelder-Mead simplex method. The proposed rolling schedule optimization method has been applied successfully to the 5-stand tandem cold mill in Tangsteel, and the results from a case study show that the proposed method is superior to those based on empirical formulae.
