High fidelity analysis are utilized in modern engineering design optimization problems which involve expensive black-box models.For computation-intensive engineering design problems,efficient global optimization metho...High fidelity analysis are utilized in modern engineering design optimization problems which involve expensive black-box models.For computation-intensive engineering design problems,efficient global optimization methods must be developed to relieve the computational burden.A new metamodel-based global optimization method using fuzzy clustering for design space reduction(MGO-FCR) is presented.The uniformly distributed initial sample points are generated by Latin hypercube design to construct the radial basis function metamodel,whose accuracy is improved with increasing number of sample points gradually.Fuzzy c-mean method and Gath-Geva clustering method are applied to divide the design space into several small interesting cluster spaces for low and high dimensional problems respectively.Modeling efficiency and accuracy are directly related to the design space,so unconcerned spaces are eliminated by the proposed reduction principle and two pseudo reduction algorithms.The reduction principle is developed to determine whether the current design space should be reduced and which space is eliminated.The first pseudo reduction algorithm improves the speed of clustering,while the second pseudo reduction algorithm ensures the design space to be reduced.Through several numerical benchmark functions,comparative studies with adaptive response surface method,approximated unimodal region elimination method and mode-pursuing sampling are carried out.The optimization results reveal that this method captures the real global optimum for all the numerical benchmark functions.And the number of function evaluations show that the efficiency of this method is favorable especially for high dimensional problems.Based on this global design optimization method,a design optimization of a lifting surface in high speed flow is carried out and this method saves about 10 h compared with genetic algorithms.This method possesses favorable performance on efficiency,robustness and capability of global convergence and gives a new optimization strategy for engineering design optimization problems involving expensive black box models.展开更多
Based on the trajectory design of a mission to Saturn, this paper discusses four different trajectories in various swingby cases. We assume a single impulse to be applied in each case when the spacecraft approaches a ...Based on the trajectory design of a mission to Saturn, this paper discusses four different trajectories in various swingby cases. We assume a single impulse to be applied in each case when the spacecraft approaches a celestial body. Some optimal trajectories ofEJS, EMS, EVEJS and EVVEJS flying sequences are obtained using five global optimization algorithms： DE, PSO, DP, the hybrid algorithm PSODE and another hybrid algorithm, DPDE. DE is proved to be supe- rior to other non-hybrid algorithms in the trajectory optimi- zation problem. The hybrid algorithm of PSO and DE can improve the optimization performance of DE, which is vali- dated by the mission to Saturn with given swingby sequences. Finally, the optimization results of four different swingby sequences are compared with those of the ACT of ESA.展开更多
Optimization is a key technique for maximizing or minimizing functions and achieving optimal cost,gains,energy,mass,and so on.In order to solve optimization problems,metaheuristic algorithms are essential.Most of thes...Optimization is a key technique for maximizing or minimizing functions and achieving optimal cost,gains,energy,mass,and so on.In order to solve optimization problems,metaheuristic algorithms are essential.Most of these techniques are influenced by collective knowledge and natural foraging.There is no such thing as the best or worst algorithm;instead,there are more effective algorithms for certain problems.Therefore,in this paper,a new improved variant of a recently proposed metaphorless Runge-Kutta Optimization(RKO)algorithm,called Improved Runge-Kutta Optimization(IRKO)algorithm,is suggested for solving optimization problems.The IRKO is formulated using the basic RKO and local escaping operator to enhance the diversification and intensification capability of the basic RKO version.The performance of the proposed IRKO algorithm is validated on 23 standard benchmark functions and three engineering constrained optimization problems.The outcomes of IRKO are compared with seven state-of-the-art algorithms,including the basic RKO algorithm.Compared to other algorithms,the recommended IRKO algorithm is superior in discovering the optimal results for all selected optimization problems.The runtime of IRKO is less than 0.5 s for most of the 23 benchmark problems and stands first for most of the selected problems,including real-world optimization problems.展开更多
High fidelity analysis models,which are beneficial to improving the design quality,have been more and more widely utilized in the modern engineering design optimization problems.However,the high fidelity analysis mode...High fidelity analysis models,which are beneficial to improving the design quality,have been more and more widely utilized in the modern engineering design optimization problems.However,the high fidelity analysis models are so computationally expensive that the time required in design optimization is usually unacceptable.In order to improve the efficiency of optimization involving high fidelity analysis models,the optimization efficiency can be upgraded through applying surrogates to approximate the computationally expensive models,which can greately reduce the computation time.An efficient heuristic global optimization method using adaptive radial basis function（RBF） based on fuzzy clustering（ARFC） is proposed.In this method,a novel algorithm of maximin Latin hypercube design using successive local enumeration（SLE） is employed to obtain sample points with good performance in both space-filling and projective uniformity properties,which does a great deal of good to metamodels accuracy.RBF method is adopted for constructing the metamodels,and with the increasing the number of sample points the approximation accuracy of RBF is gradually enhanced.The fuzzy c-means clustering method is applied to identify the reduced attractive regions in the original design space.The numerical benchmark examples are used for validating the performance of ARFC.The results demonstrates that for most application examples the global optima are effectively obtained and comparison with adaptive response surface method（ARSM） proves that the proposed method can intuitively capture promising design regions and can efficiently identify the global or near-global design optimum.This method improves the efficiency and global convergence of the optimization problems,and gives a new optimization strategy for engineering design optimization problems involving computationally expensive models.展开更多
A global routing algorithm with performance optimization under multi constraints is proposed,which studies RLC coupling noise,timing performance,and routability simultaneously at global routing level.The algorithm is...A global routing algorithm with performance optimization under multi constraints is proposed,which studies RLC coupling noise,timing performance,and routability simultaneously at global routing level.The algorithm is implemented and the global router is called CEE Gr.The CEE Gr is tested on MCNC benchmarks and the experimental results are promising.展开更多
We present a staggered buffer connection method that provides flexibility for buffer insertion while designing global signal networks using the tile-based FPGA design methodology. An exhaustive algorithm is used to an...We present a staggered buffer connection method that provides flexibility for buffer insertion while designing global signal networks using the tile-based FPGA design methodology. An exhaustive algorithm is used to analyze the trade-off between area and speed of the global signal networks for this staggered buffer insertion scheme, and the criterion for determining the design parameters is presented. The comparative analytic result shows that the methods in this paper are proven to be more efficient for FPGAs with a large array size.展开更多
The current research of complex nonlinear system robust optimization mainly focuses on the features of design parameters, such as probability density functions, boundary conditions, etc. After parameters study, high-d...The current research of complex nonlinear system robust optimization mainly focuses on the features of design parameters, such as probability density functions, boundary conditions, etc. After parameters study, high-dimensional curve or robust control design is used to find an accurate robust solution. However, there may exist complex interaction between parameters and practical engineering system. With the increase of the number of parameters, it is getting hard to determine high-dimensional curves and robust control methods, thus it＇s difficult to get the robust design solutions. In this paper, a method of global sensitivity analysis based on divided variables in groups is proposed. By making relevant variables in one group and keeping each other independent among sets of variables, global sensitivity analysis is conducted in grouped variables and the importance of parameters is evaluated by calculating the contribution value of each parameter to the total variance of system response. By ranking the importance of input parameters, relatively important parameters are chosen to conduct robust design analysis of the system. By applying this method to the robust optimization design of a real complex nonlinear system-a vehicle occupant restraint system with multi-parameter, good solution is gained and the response variance of the objective function is reduced to 0.01, which indicates that the robustness of the occupant restraint system is improved in a great degree and the method is effective and valuable for the robust design of complex nonlinear system. This research proposes a new method which can be used to obtain solutions for complex nonlinear system robust design.展开更多
Many engineering optimization problems frequently encounter continuous variables and discrete variables which adds considerably to the solution complexity. Very few of the existing methods can yield a globally optimal...Many engineering optimization problems frequently encounter continuous variables and discrete variables which adds considerably to the solution complexity. Very few of the existing methods can yield a globally optimal solution when the objective functions are non-convex and non-differentiable. This paper presents a hybrid swarm intelligence ap-proach (HSIA) for solving these nonlinear optimization problems which contain integer, discrete, zero-one and continuous variables. HSIA provides an improvement in global search reliability in a mixed-variable space and converges steadily to a good solution. An approach to handle various kinds of variables and constraints is discussed. Comparison testing of several examples of mixed-variable optimization problems in the literature showed that the proposed approach is superior to current methods for finding the best solution, in terms of both solution quality and algorithm robustness.展开更多
Global strength is a significant item for floating production storage and offloading(FPSO) design, and steel weight plays an important role in the building costs of FPSO. It is the main task to consider and combine th...Global strength is a significant item for floating production storage and offloading(FPSO) design, and steel weight plays an important role in the building costs of FPSO. It is the main task to consider and combine these two aspects by optimizing hull dimensions. There are many optional methods for the global strength analysis. A common method is to use the ABS FPSO Eagle software to analyze the global strength including the rule check and direct strength analysis. And the same method can be adopted for the FPSO hull optimization by changing the depth. After calculation and optimization, the results are compared and analyzed. The results can be used as a reference for the future design or quotation purpose.展开更多
The robust design optimization(RDO)is an effective method to improve product performance with uncertainty factors.The robust optimal solution should be not only satisfied the probabilistic constraints but also less se...The robust design optimization(RDO)is an effective method to improve product performance with uncertainty factors.The robust optimal solution should be not only satisfied the probabilistic constraints but also less sensitive to the variation of design variables.There are some important issues in RDO,such as how to judge robustness,deal with multi-objective problem and black-box situation.In this paper,two criteria are proposed to judge the deterministic optimal solution whether satisfies robustness requirment.The robustness measure based on maximum entropy is proposed.Weighted sum method is improved to deal with the objective function,and the basic framework of metamodel assisted robust optimization is also provided for improving the efficiency.Finally,several engineering examples are used to verify the advantages.展开更多
This paper presents the methods and results for the trajectory design and optimization for the low earth orbit (LEO) satellites in formation to observe the geostationary orbit (GEO) satellites’ beams. The background ...This paper presents the methods and results for the trajectory design and optimization for the low earth orbit (LEO) satellites in formation to observe the geostationary orbit (GEO) satellites’ beams. The background of the trajectory design mission is the 9th China Trajectory Optimization Competition (CTOC9). The formation is designed according to the observation demands. The flying sequence is determined by a reference satellite using a proposed improved ephemeris matching method (IEMM). The formation is changed, maintained and transferred following the reference satellite employing a multi-impulse control method (MICM). Then the total observation value is computed by propagating the orbits of the satellites according to the sequence and transfer strategies. Based on the above methods, we have obtained a fourth prize in the CTOC9. The proposed methods are not only fit for this competition, but can also be used to fulfill the trajectory design missions for similar multi-object explorations.展开更多
A servo press is a new type of mechanical press that is driven by programmable motors and offers superior performance such as low noise, excellent efficiency and high precision for metal forming operations. Similar to...A servo press is a new type of mechanical press that is driven by programmable motors and offers superior performance such as low noise, excellent efficiency and high precision for metal forming operations. Similar to multi-link mechanical presses, a servo mechanical press tends to grow in size as the tonnage increases that calls for larger, heavy duty servo motors, which could be expensive and may not even be available. In this paper, a new concept of servo mechanical press with redundant actuation is proposed firstly using two servo motors driving one input shaft, i.e. one-point-two-motor mode that makes it possible to produce a larger press with available servomotors. Then the punching mechanism design is detailed. The performance indices are set up including mechanical advantage reciprocal and link force ratios. A bounded feasible solution space is constructed for dimensional synthesis based on non-dimensional link lengths and assembly conditions. The performance atlases are depicted over the bounded feasible solution space that lead to a visual solution of the punching mechanism with global optimization. Finally, case studies are given to illustrate the design method with visual global optimization, and a prototype with 200 t punching force is being developed in our laboratory to demonstrate efficacy of the new concept for servo mechanical press. The presented research provides a feasible solution to the development of heavy-duty servo mechanical presses and finds potential applications in the development of other types of heavy equipments with electric drive.展开更多
基金supported by National Natural Science Foundation of China(Grant No.51105040)Aeronautic Science Foundation of China(Grant No.2011ZA72003)Excellent Young Scholars Research Fund of Beijing Institute of Technology(Grant No.2010Y0102)
文摘High fidelity analysis are utilized in modern engineering design optimization problems which involve expensive black-box models.For computation-intensive engineering design problems,efficient global optimization methods must be developed to relieve the computational burden.A new metamodel-based global optimization method using fuzzy clustering for design space reduction(MGO-FCR) is presented.The uniformly distributed initial sample points are generated by Latin hypercube design to construct the radial basis function metamodel,whose accuracy is improved with increasing number of sample points gradually.Fuzzy c-mean method and Gath-Geva clustering method are applied to divide the design space into several small interesting cluster spaces for low and high dimensional problems respectively.Modeling efficiency and accuracy are directly related to the design space,so unconcerned spaces are eliminated by the proposed reduction principle and two pseudo reduction algorithms.The reduction principle is developed to determine whether the current design space should be reduced and which space is eliminated.The first pseudo reduction algorithm improves the speed of clustering,while the second pseudo reduction algorithm ensures the design space to be reduced.Through several numerical benchmark functions,comparative studies with adaptive response surface method,approximated unimodal region elimination method and mode-pursuing sampling are carried out.The optimization results reveal that this method captures the real global optimum for all the numerical benchmark functions.And the number of function evaluations show that the efficiency of this method is favorable especially for high dimensional problems.Based on this global design optimization method,a design optimization of a lifting surface in high speed flow is carried out and this method saves about 10 h compared with genetic algorithms.This method possesses favorable performance on efficiency,robustness and capability of global convergence and gives a new optimization strategy for engineering design optimization problems involving expensive black box models.
基金supported by the National Natural Science Foundation of China (10832004 and 10672084).
文摘Based on the trajectory design of a mission to Saturn, this paper discusses four different trajectories in various swingby cases. We assume a single impulse to be applied in each case when the spacecraft approaches a celestial body. Some optimal trajectories ofEJS, EMS, EVEJS and EVVEJS flying sequences are obtained using five global optimization algorithms： DE, PSO, DP, the hybrid algorithm PSODE and another hybrid algorithm, DPDE. DE is proved to be supe- rior to other non-hybrid algorithms in the trajectory optimi- zation problem. The hybrid algorithm of PSO and DE can improve the optimization performance of DE, which is vali- dated by the mission to Saturn with given swingby sequences. Finally, the optimization results of four different swingby sequences are compared with those of the ACT of ESA.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University,Saudi Arabia,for funding this work through the Research Group Program under Grant No:RGP.2/108/42.
文摘Optimization is a key technique for maximizing or minimizing functions and achieving optimal cost,gains,energy,mass,and so on.In order to solve optimization problems,metaheuristic algorithms are essential.Most of these techniques are influenced by collective knowledge and natural foraging.There is no such thing as the best or worst algorithm;instead,there are more effective algorithms for certain problems.Therefore,in this paper,a new improved variant of a recently proposed metaphorless Runge-Kutta Optimization(RKO)algorithm,called Improved Runge-Kutta Optimization(IRKO)algorithm,is suggested for solving optimization problems.The IRKO is formulated using the basic RKO and local escaping operator to enhance the diversification and intensification capability of the basic RKO version.The performance of the proposed IRKO algorithm is validated on 23 standard benchmark functions and three engineering constrained optimization problems.The outcomes of IRKO are compared with seven state-of-the-art algorithms,including the basic RKO algorithm.Compared to other algorithms,the recommended IRKO algorithm is superior in discovering the optimal results for all selected optimization problems.The runtime of IRKO is less than 0.5 s for most of the 23 benchmark problems and stands first for most of the selected problems,including real-world optimization problems.
基金supported by National Natural Science Foundation of China (Grant Nos. 50875024,51105040)Excellent Young Scholars Research Fund of Beijing Institute of Technology,China (Grant No.2010Y0102)Defense Creative Research Group Foundation of China(Grant No. GFTD0803)
文摘High fidelity analysis models,which are beneficial to improving the design quality,have been more and more widely utilized in the modern engineering design optimization problems.However,the high fidelity analysis models are so computationally expensive that the time required in design optimization is usually unacceptable.In order to improve the efficiency of optimization involving high fidelity analysis models,the optimization efficiency can be upgraded through applying surrogates to approximate the computationally expensive models,which can greately reduce the computation time.An efficient heuristic global optimization method using adaptive radial basis function（RBF） based on fuzzy clustering（ARFC） is proposed.In this method,a novel algorithm of maximin Latin hypercube design using successive local enumeration（SLE） is employed to obtain sample points with good performance in both space-filling and projective uniformity properties,which does a great deal of good to metamodels accuracy.RBF method is adopted for constructing the metamodels,and with the increasing the number of sample points the approximation accuracy of RBF is gradually enhanced.The fuzzy c-means clustering method is applied to identify the reduced attractive regions in the original design space.The numerical benchmark examples are used for validating the performance of ARFC.The results demonstrates that for most application examples the global optima are effectively obtained and comparison with adaptive response surface method（ARSM） proves that the proposed method can intuitively capture promising design regions and can efficiently identify the global or near-global design optimum.This method improves the efficiency and global convergence of the optimization problems,and gives a new optimization strategy for engineering design optimization problems involving computationally expensive models.
文摘A global routing algorithm with performance optimization under multi constraints is proposed,which studies RLC coupling noise,timing performance,and routability simultaneously at global routing level.The algorithm is implemented and the global router is called CEE Gr.The CEE Gr is tested on MCNC benchmarks and the experimental results are promising.
文摘We present a staggered buffer connection method that provides flexibility for buffer insertion while designing global signal networks using the tile-based FPGA design methodology. An exhaustive algorithm is used to analyze the trade-off between area and speed of the global signal networks for this staggered buffer insertion scheme, and the criterion for determining the design parameters is presented. The comparative analytic result shows that the methods in this paper are proven to be more efficient for FPGAs with a large array size.
基金Supported by National Natural Science Foundation of China(Grant No.51275164)
文摘The current research of complex nonlinear system robust optimization mainly focuses on the features of design parameters, such as probability density functions, boundary conditions, etc. After parameters study, high-dimensional curve or robust control design is used to find an accurate robust solution. However, there may exist complex interaction between parameters and practical engineering system. With the increase of the number of parameters, it is getting hard to determine high-dimensional curves and robust control methods, thus it＇s difficult to get the robust design solutions. In this paper, a method of global sensitivity analysis based on divided variables in groups is proposed. By making relevant variables in one group and keeping each other independent among sets of variables, global sensitivity analysis is conducted in grouped variables and the importance of parameters is evaluated by calculating the contribution value of each parameter to the total variance of system response. By ranking the importance of input parameters, relatively important parameters are chosen to conduct robust design analysis of the system. By applying this method to the robust optimization design of a real complex nonlinear system-a vehicle occupant restraint system with multi-parameter, good solution is gained and the response variance of the objective function is reduced to 0.01, which indicates that the robustness of the occupant restraint system is improved in a great degree and the method is effective and valuable for the robust design of complex nonlinear system. This research proposes a new method which can be used to obtain solutions for complex nonlinear system robust design.
基金Project supported by the National Natural Science Foundation ofChina (Nos. 60074040 6022506) and the Teaching and ResearchAward Program for Outstanding Young Teachers in Higher Edu-cation Institutions of China
文摘Many engineering optimization problems frequently encounter continuous variables and discrete variables which adds considerably to the solution complexity. Very few of the existing methods can yield a globally optimal solution when the objective functions are non-convex and non-differentiable. This paper presents a hybrid swarm intelligence ap-proach (HSIA) for solving these nonlinear optimization problems which contain integer, discrete, zero-one and continuous variables. HSIA provides an improvement in global search reliability in a mixed-variable space and converges steadily to a good solution. An approach to handle various kinds of variables and constraints is discussed. Comparison testing of several examples of mixed-variable optimization problems in the literature showed that the proposed approach is superior to current methods for finding the best solution, in terms of both solution quality and algorithm robustness.
基金the sponsors of this project: American Bureau of Shipping
文摘Global strength is a significant item for floating production storage and offloading(FPSO) design, and steel weight plays an important role in the building costs of FPSO. It is the main task to consider and combine these two aspects by optimizing hull dimensions. There are many optional methods for the global strength analysis. A common method is to use the ABS FPSO Eagle software to analyze the global strength including the rule check and direct strength analysis. And the same method can be adopted for the FPSO hull optimization by changing the depth. After calculation and optimization, the results are compared and analyzed. The results can be used as a reference for the future design or quotation purpose.
基金The study is supported by the National Numerical Wind tunnel project(No.2019ZT2-A05)the Nature Science Foundation of China(No.11902254).
文摘The robust design optimization(RDO)is an effective method to improve product performance with uncertainty factors.The robust optimal solution should be not only satisfied the probabilistic constraints but also less sensitive to the variation of design variables.There are some important issues in RDO,such as how to judge robustness,deal with multi-objective problem and black-box situation.In this paper,two criteria are proposed to judge the deterministic optimal solution whether satisfies robustness requirment.The robustness measure based on maximum entropy is proposed.Weighted sum method is improved to deal with the objective function,and the basic framework of metamodel assisted robust optimization is also provided for improving the efficiency.Finally,several engineering examples are used to verify the advantages.
文摘This paper presents the methods and results for the trajectory design and optimization for the low earth orbit (LEO) satellites in formation to observe the geostationary orbit (GEO) satellites’ beams. The background of the trajectory design mission is the 9th China Trajectory Optimization Competition (CTOC9). The formation is designed according to the observation demands. The flying sequence is determined by a reference satellite using a proposed improved ephemeris matching method (IEMM). The formation is changed, maintained and transferred following the reference satellite employing a multi-impulse control method (MICM). Then the total observation value is computed by propagating the orbits of the satellites according to the sequence and transfer strategies. Based on the above methods, we have obtained a fourth prize in the CTOC9. The proposed methods are not only fit for this competition, but can also be used to fulfill the trajectory design missions for similar multi-object explorations.
基金supported by National Natural Science Foundation of China (Grant No. 50875161, No. 50405017)National Hi-Tech Research and Development Program of China (863 Program, Grant No. 2006AA04Z118)
文摘A servo press is a new type of mechanical press that is driven by programmable motors and offers superior performance such as low noise, excellent efficiency and high precision for metal forming operations. Similar to multi-link mechanical presses, a servo mechanical press tends to grow in size as the tonnage increases that calls for larger, heavy duty servo motors, which could be expensive and may not even be available. In this paper, a new concept of servo mechanical press with redundant actuation is proposed firstly using two servo motors driving one input shaft, i.e. one-point-two-motor mode that makes it possible to produce a larger press with available servomotors. Then the punching mechanism design is detailed. The performance indices are set up including mechanical advantage reciprocal and link force ratios. A bounded feasible solution space is constructed for dimensional synthesis based on non-dimensional link lengths and assembly conditions. The performance atlases are depicted over the bounded feasible solution space that lead to a visual solution of the punching mechanism with global optimization. Finally, case studies are given to illustrate the design method with visual global optimization, and a prototype with 200 t punching force is being developed in our laboratory to demonstrate efficacy of the new concept for servo mechanical press. The presented research provides a feasible solution to the development of heavy-duty servo mechanical presses and finds potential applications in the development of other types of heavy equipments with electric drive.
