For dealing with the multi-objective optimization problems of parametric design for aircraft, a novel hybrid parallel multi-objective tabu search （HPMOTS） algorithm is used. First, a new multi-objective tabu search ...For dealing with the multi-objective optimization problems of parametric design for aircraft, a novel hybrid parallel multi-objective tabu search （HPMOTS） algorithm is used. First, a new multi-objective tabu search （MOTS） algorithm is proposed. Comparing with the traditional MOTS algorithm, this proposed algorithm adds some new methods such as the combination of MOTS algorithm and ＂Pareto solution＂, the strategy of ＂searching from many directions＂ and the reservation of good solutions. Second, this article also proposes the improved parallel multi-objective tabu search （PMOTS） algorithm. Finally, a new hybrid algorithm--HPMOTS algorithm which combines the PMOTS algorithm with the non-dominated sorting-based multi-objective genetic algorithm （NSGA） is presented. The computing results of these algorithms are compared with each other and it is shown that the optimal result can be obtained by the HPMOTS algorithm and the computing result of the PMOTS algorithm is better than that of MOTS algorithm.展开更多
Finding an optimal isolator arrangement for asymmetric structures using traditional conceptual design methods that can significantly minimize torsional response while ensuring efficient horizontal seismic isolation is...Finding an optimal isolator arrangement for asymmetric structures using traditional conceptual design methods that can significantly minimize torsional response while ensuring efficient horizontal seismic isolation is cumbersome and inefficient.Thus,this work develops a multi-objective optimization method to enhance the torsional resistance of asymmetric base-isolated structures.The primary objective is to simultaneously minimize the interstory rotation of the superstructure,the rotation of the isolation layer,and the interstory displacement of the superstructure without exceeding the isolator displacement limits.A fast non-dominated sorting genetic algorithm(NSGA-Ⅱ)is employed to satisfy this optimization objective.Subsequently,the isolator arrangement,encompassing both positions and categories,is optimized according to this multi-objective optimization method.Additionally,an optimization design platform is developed to streamline the design operation.This platform integrates the input of optimization parameters,the output of optimization results,the finite element analysis,and the multi-objective optimization method proposed herein.Finally,the application of this multi-objective optimization method and its associated platform are demonstrated on two asymmetric base-isolated structures of varying heights and plan configurations.The results indicate that the optimal isolator arrangement derived from the optimization method can further improve the control over the lateral and torsional responses of asymmetric base-isolated structures compared to conventional conceptual design methods.Notably,the interstory rotation of the optimal base-isolated structure is significantly reduced,constituting only approximately 33.7%of that observed in the original base-isolated structure.The proposed platform facilitates the automatic generation of the optimal design scheme for the isolators of asymmetric base-isolated structures,offering valuable insights and guidance for the burgeoning field of intelligent civil engineering design.展开更多
Designing compositions and processing of biodegradable magnesium(Mg)alloys to synergistically en-hance mechanical properties and corrosion resistance using conventional trial-and-error method is a challenging task.Thi...Designing compositions and processing of biodegradable magnesium(Mg)alloys to synergistically en-hance mechanical properties and corrosion resistance using conventional trial-and-error method is a challenging task.This study presents a Bayesian optimization(BO)-based multi-objective framework inte-grated with explainable machine learning(ML)to efficiently explore and optimize the high-dimensional design space of biodegradable Mg alloys.Using ultimate tensile strength(UTS),elongation(EL)and cor-rosion potential(E_(corr))as objective properties,the framework balances these conflicting objectives and identifies optimal solutions.A novel biodegradable Mg alloy(Mg-4.6Zn-0.3Y-0.2Mn-0.1Nd-0.1Gd,wt.%)was successfully designed,demonstrating a UTS of 320 MPa,EL of 22%and E_(corr) of−1.60 V(tested in 37℃ simulated body fluid).Compared to JDBM,the UTS has increased by 13 MPa,the EL has improved by 6.1%,and the E_(corr) has risen by 0.02 V.The experimental results presented close agreement with predicted values,validating the proposed framework.The Shapley Additive Explanation method was em-ployed to interpret the ML models,revealing extrusion temperature and Zn content as key parameters driving the optimization design.The strategy provided in this study is universal and offers a potential approach for addressing high-dimensional multi-objective optimization challenges in material develop-ment.展开更多
Typical multidisciplinary design optimization（MDO） has gradually been proposed to balance performances of lightweight, noise, vibration and harshness（NVH） and safety for instrument panel（IP） structure in the aut...Typical multidisciplinary design optimization（MDO） has gradually been proposed to balance performances of lightweight, noise, vibration and harshness（NVH） and safety for instrument panel（IP） structure in the automotive development. Nevertheless, plastic constitutive relation of Polypropylene（PP） under different strain rates, has not been taken into consideration in current reliability-based and collaborative IP MDO design. In this paper, based on tensile test under different strain rates, the constitutive relation of Polypropylene material is studied. Impact simulation tests for head and knee bolster are carried out to meet the regulation of FMVSS 201 and FMVSS 208, respectively. NVH analysis is performed to obtain mainly the natural frequencies and corresponding mode shapes, while the crashworthiness analysis is employed to examine the crash behavior of IP structure. With the consideration of lightweight, NVH, head and knee bolster impact performance, design of experiment（DOE）, response surface model（RSM）, and collaborative optimization（CO） are applied to realize the determined and reliability-based optimizations, respectively. Furthermore, based on multi-objective genetic algorithm（MOGA）, the optimal Pareto sets are completed to solve the multi-objective optimization（MOO） problem. The proposed research ensures the smoothness of Pareto set, enhances the ability of engineers to make a comprehensive decision about multi-objectives and choose the optimal design, and improves the quality and efficiency of MDO.展开更多
Low Earth Orbit(LEO)satellite for navigation augmentation applications can significantly reduce the precise positioning convergence time and attract increasing attention recently.A few LEO Navigation Augmentation(LEO-...Low Earth Orbit(LEO)satellite for navigation augmentation applications can significantly reduce the precise positioning convergence time and attract increasing attention recently.A few LEO Navigation Augmentation(LEO-NA)constellations have been proposed,while corresponding constellation design methodologies have not been systematically studied.The LEO-NA constellation generally consists of a huge number of LEO satellites and it strives for multiple optimization purposes.It is essentially different from the communication constellation or earth observing constellation design problem.In this study,we modeled the LEO-NA constellation design problem as a multi-objective optimization problem and solve this problem with the MultiObjective Particle Swarm Optimization(MOPSO)algorithm.Three objectives are used to strive for the best tradeoff between the augmentation performance and deployment efficiency,namely the Position Dilution of Precision(PDOP),visible LEO satellites and the orbit altitude.A fuzzy set approach is used to select the final constellation from a set of Pareto optimal solutions given by the MOPSO algorithm.To evaluate the performance of the optimized constellation,we tested two constellations with 144 and 288 satellites and each constellation has three optimization schemes:the polar constellation,the single-layer constellation and the two-layer constellation.The results indicate that the optimized two-layer constellation achieves the best global coverage and is followed by the single-layer constellation.The MOPSO algorithm can help to improve the constellation design and is suitable for solving the LEO-NA constellation design problem.展开更多
It is a complicated problem for the bottom-to-top adaptive conceptual design of complicated products between structure and function. Reliable theories demand to be found in order to determine whether the structure acc...It is a complicated problem for the bottom-to-top adaptive conceptual design of complicated products between structure and function. Reliable theories demand to be found in order to determine whether the structure accords with the requirement of design. For the requirement generally is dynamic variety as time passes, new requirements will come, and some initial requirements can no longer be used. The number of product requirements, the gene length expressing requirements, the structure of the product, and the correlation matrix are varied with individuation of customer requirements of the product. By researching on the calculation mechanisms of dynamic variety, the approaches of gene expression and variable length gene expression are proposed. According to the diversity of structure selection in conceptual design and mutual relations between structure and function as well as structure and structure, the correlation matrixes between structure and function as well as structure and structure are defined. By the approach of making the sum of the elements of correlation matrix maximum, the mathematical models of multi-object optimization for structure design are provided based on variable requirements. An improved genetic algorithm called segment genetic algorithm is proposed based on optimization preservation simple genetic algorithm. The models of multi-object optimization are calculated by the segment genetic algorithm and hybrid genetic algorithm. An example for the conceptual design of a washing machine is given to show that the proposed method is able to realize the optimization structure design fitting for variable requirements. In addition, the proposed approach can provide good Pareto optimization solutions, and the individuation customer requirements for structures of products are able to be resolved effectively.展开更多
Although various types of anti-roll torsion bars have been developed to inhibit excessive roll angle of the electric multiple unit(EMU)car body,it is critical to ensure the reliability of structural design due to the ...Although various types of anti-roll torsion bars have been developed to inhibit excessive roll angle of the electric multiple unit(EMU)car body,it is critical to ensure the reliability of structural design due to the complexity of the problems involving time and uncertainties.To address this issue,amulti-objective fuzzy design optimization model is constructed considering time-variant stiffness and strength reliability constraints for the anti-roll torsion bar.A hybrid optimization strategy combining the design of experiment(DoE)sampling and non-linear programming by quadratic lagrangian(NLPQL)is presented to deal with the design optimization model.To characterize the effect of time on the structural performance of the torsion bar,the continuous-time model combined with Ito lemma is proposed to establish the time-variant stiffness and strength reliability constraints.Fuzzy mathematics is employed to conduct uncertainty quantification for the design parameters of the torsion bar.A physical programming approach is used to improve the designer’s preference and to make the optimization results more consistent with engineering practices.Moreover,the effectiveness of the proposed method has been validated by comparing with current methods in a practical engineering case.展开更多
Thin-walled structures have been used in many fields due to their superior mechanical properties.In this paper,two types of hierarchical multi-cell tubes,inspired by the self-similarity of Pinus sylvestris,are propose...Thin-walled structures have been used in many fields due to their superior mechanical properties.In this paper,two types of hierarchical multi-cell tubes,inspired by the self-similarity of Pinus sylvestris,are proposed to enhance structural energy absorption performance.The finite element models of the hierarchical structures are established to validate the crashworthiness performance under axial dynamic load.The theoreticalmodel of themean crushing force is also derived based on the simplified super folded element theory.The finite element results demonstrate that the energy absorption characteristics and deformation mode of the bionic hierarchical thin-walled tubes are further improved with the increase of hierarchical sub-structures.It can be also obtained that the energy absorption performance of corner self-similar tubes is better than edge self-similar tubes.Furthermore,multiobjective optimization of the hierarchical tubes is constructed by employing the response surface method and genetic algorithm,and the corresponding Pareto front diagram is obtained.This research provides a new idea for the crashworthiness design of thin-walled structures.展开更多
In many circumstances, chemical process design can be formulated as a multi-objective optimization （MOO） problem. Examples include bi-objective optimization problems, where the economic objective is maximized and en...In many circumstances, chemical process design can be formulated as a multi-objective optimization （MOO） problem. Examples include bi-objective optimization problems, where the economic objective is maximized and environmental impact is minimized simultaneously. Moreover, the random behavior in the process,property, market fluctuation, errors in model prediction and so on would affect the performance of a process. Therefore, it is essential to develop a MOO methodology under uncertainty. In this article, the authors propose a generic and systematic optimization methodology for chemical process design under uncertainty. It aims at identifying the optimal design from a number of candidates. The utility of this methodology is demonstrated by a case study based on the design of a condensate treatment unit in an ammonia plant.展开更多
For an optimal design of a surface-mounted permanent magnet synchronous motor(SPMSM),many objective functions should be considered.The classical optimization methods,which have been habitually designed based on magnet...For an optimal design of a surface-mounted permanent magnet synchronous motor(SPMSM),many objective functions should be considered.The classical optimization methods,which have been habitually designed based on magnetic circuit law or finite element analysis(FEA),have inaccuracy or calculation time problems when solving the multi-objective problems.To address these problems,the multi-independent-population genetic algorithm(MGA)combined with subdomain(SD)model are proposed to improve the performance of SPMSM such as magnetic field distribution,cost and efficiency.In order to analyze the flux density harmonics accurately,the accurate SD model is first established.Then,the MGA with time-saving SD model are employed to search for solutions which belong to the Pareto optimal set.Finally,for the purpose of validation,the electromagnetic performance of the new design motor are investigated by FEA,comparing with the initial design and conventional GA optimal design to demonstrate the advantage of MGA optimization method.展开更多
Purpose–The nose length is the key design parameter affecting the aerodynamic performance of high-speed maglev train,and the horizontal profile has a significant impact on the aerodynamic lift of the leading and trai...Purpose–The nose length is the key design parameter affecting the aerodynamic performance of high-speed maglev train,and the horizontal profile has a significant impact on the aerodynamic lift of the leading and trailing cars Hence,the study analyzes aerodynamic parameters with multi-objective optimization design.Design/methodology/approach–The nose of normal temperature and normal conduction high-speed maglev train is divided into streamlined part and equipment cabin according to its geometric characteristics.Then the modified vehicle modeling function(VMF)parameterization method and surface discretization method are adopted for the parametric design of the nose.For the 12 key design parameters extracted,combined with computational fluid dynamics(CFD),support vector machine(SVR)model and multi-objective particle swarm optimization(MPSO)algorithm,the multi-objective aerodynamic optimization design of highspeed maglev train nose and the sensitivity analysis of design parameters are carried out with aerodynamic drag coefficient of the whole vehicle and the aerodynamic lift coefficient of the trailing car as the optimization objectives and the aerodynamic lift coefficient of the leading car as the constraint.The engineering improvement and wind tunnel test verification of the optimized shape are done.Findings–Results show that the parametric design method can use less design parameters to describe the nose shape of high-speed maglev train.The prediction accuracy of the SVR model with the reduced amount of calculation and improved optimization efficiency meets the design requirements.Originality/value–Compared with the original shape,the aerodynamic drag coefficient of the whole vehicle is reduced by 19.2%,and the aerodynamic lift coefficients of the leading and trailing cars are reduced by 24.8 and 51.3%,respectively,after adopting the optimized shape modified according to engineering design requirements.展开更多
This paper describes one approach to the design of reinforced concrete (RC) bridge piers, using a three-hybrid multi- objective simulated annealing (SA) algorithm with a neighborhood move based on the mutation operato...This paper describes one approach to the design of reinforced concrete (RC) bridge piers, using a three-hybrid multi- objective simulated annealing (SA) algorithm with a neighborhood move based on the mutation operator from the genetic algorithms (GAs), namely MOSAMO1, MOSAMO2 and MOSAMO3. The procedure is applied to three objective functions: the economic cost, the reinforcing steel congestion and the embedded CO 2 emissions. Additional results for a random walk and a descent local search multi-objective algorithm are presented. The evaluation of solutions follows the Spanish Code for structural concrete. The methodology was applied to a typical bridge pier of 23.97 m in height. This example involved 110 design variables. Results indicate that algorithm MOSAMO2 outperforms other algorithms regarding the definition of Pareto fronts. Further, the proposed procedure will help structural engineers to enhance their bridge pier designs.展开更多
The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system perf...The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system performance and control cost are defined by H2 or H∞ norms. During this optimization process, the weights are varying with the increasing generation instead of fixed values. The proposed strategy together with the linear matrix inequality （LMI） or the Riccati controller design method can find a series of uniformly distributed nondominated solutions in a single run. Therefore, this method can greatly reduce the computation intensity of the integrated optimization problem compared with the weight-based single objective genetic algorithm. Active automotive suspension is adopted as an example to illustrate the effectiveness of the proposed method.展开更多
In the current scenario of global competition and short product life cycles, customer-defined satisfaction has attracted interest in artifact design. Accordingly, intelligent decision-making through multi-objective op...In the current scenario of global competition and short product life cycles, customer-defined satisfaction has attracted interest in artifact design. Accordingly, intelligent decision-making through multi-objective optimization has been proposed as an efficient method for human-centered manufacturing. However, previous vast researches on optimization have been mainly focused on optimization theory and optimization techniques and paid little interests on the process of problem formulation itself. In this paper, therefore, the authors present a total framework for supporting multi-objective decision making. Then, the authors try to solve the formulated multi-objective optimization problem that involves both qualitative and quantitative performance measures as a general consequence from the above procedure. Taking especially quality as a qualitative measure, the authors gave a new idea to evaluate the quality quantitatively. Additionally, to facilitate the portability of the proposed method in multidisciplinary decision-making environments, the authors implement the proposal algorithm in an Excel spreadsheet and validate the effectiveness of the approach through a case study.展开更多
For high-speed heavy-duty gears in operation is prone to high tooth surface temperature rise and thus produce tooth surface gluing leading to transmission failure and other adverse effects,but in the gear optimization...For high-speed heavy-duty gears in operation is prone to high tooth surface temperature rise and thus produce tooth surface gluing leading to transmission failure and other adverse effects,but in the gear optimization design and little consideration of thermal transmission errors and thermal resonance and other factors,while the conventional multi-objective optimization design methods are difficult to achieve the optimum of each objective.Based on this,the paper proposes a gear multi-objective reliability optimisation design method based on the APCK-SORA model.The PC-Kriging model and the adaptive k-means clustering method are combined to construct an adaptive reliability analysis method(APCK for short),which is then integrated with the SORA optimisation algorithm.The objective function is the lightweight of gear pair,the maximum overlap degree and the maximum anti-glue strength;the basic parameters of the gear and the sensitivity parameters affecting the thermal deformation and thermal resonance of the gear are used as design variables;the amount of thermal deformation and thermal resonance,as well as the contact strength of the tooth face and the bending strength of the tooth root are used as constraints;the optimisation results show that:the mass of the gear is reduced by 0.13kg,the degree of overlap is increased by 0.016 and the coefficient of safety against galling Compared with other methods,the proposed method is more efficient than the other methods in meeting the multi-objective reliability design requirements of lightweighting,ensuring smoothness and anti-galling capability of high-speed heavy-duty gears.展开更多
This paper proposes a hybrid architecture based on Multi-disciplinary Design Optimization(MDO) with the Variable Complexity Modeling(VCM) method, to solve the problem of general design optimization for a stratosphere ...This paper proposes a hybrid architecture based on Multi-disciplinary Design Optimization(MDO) with the Variable Complexity Modeling(VCM) method, to solve the problem of general design optimization for a stratosphere airship. Firstly, MDO based on the Concurrent SubSpace Optimization(CSSO) strategy is improved for handling the subsystem coupling problem in stratosphere airship design which contains aerodynamics, structure, and energy. Secondly, the VCM method based on the surrogate model is presented for reducing the computational complexity in high-fidelity modeling without loss of accuracy. Moreover, the global-to-local optimization strategy is added to the architecture to enhance the process. Finally, the result gives a prominent stratosphere airship general solution that validates the feasibility and efficiency of the optimization architecture. Besides, a sensitivity analysis is conducted to outline the critical impact upon stratosphere airship design.展开更多
Blended-Wing-Body(BWB) configuration, as an innovative transport concept, has become a worldwide research focus in the field of civil transports development. Relative to the conventional Tube-And-Wing(TAW) configurati...Blended-Wing-Body(BWB) configuration, as an innovative transport concept, has become a worldwide research focus in the field of civil transports development. Relative to the conventional Tube-And-Wing(TAW) configuration, the BWB shows integrated benefits and serves as a most promising candidate for future ‘‘green aviation'. The objective of the present work is to figure out the effects of the stability margin and Thrust Specific Fuel Consumption(TSFC) on the BWB design in the framework of Multi-Disciplinary Optimization(MDO). A physically-based platform was promoted to study the effect static stability margin and engine technology level. Low-order physically based models are applied to the evaluation of the weight and the aerodynamic performance. The modules and methods are illustrated in detail, and the validation of the methods shows feasibility and confidence for the conceptual design of BWB aircrafts. In order to find out the relation between planform changes and the selection of stability and engine technology level, two sets of optimizations are conducted separately. The study proves that these two factors have dominant effects towards the optimized BWB designs in both aerodynamic shapes, weight distribution, which needs to be considered during the MDO design process. A balance diagram analysis is applied to find out a reasonable static stability margin range. It can be concluded that a recommended stability margin of a practical BWB commercial aircraft can be half of that of a conventional TAW design.展开更多
In this paper,an Uncertainty-based Multi-disciplinary Design Optimization (UMDO)method combining with fuzzy theory and Multi-Discipline Feasible (MDF) method is developed for the conceptual design of a Hybrid Rocket M...In this paper,an Uncertainty-based Multi-disciplinary Design Optimization (UMDO)method combining with fuzzy theory and Multi-Discipline Feasible (MDF) method is developed for the conceptual design of a Hybrid Rocket Motor (HRM) powered Launch Vehicle (LV).In the method proposed,membership functions are used to represent the uncertain factors,the fuzzy statistical experiment is introduced to analyze the propagation of uncertainties,and means,standard deviations and credibility measures are used to delineate uncertain responses.A geometric programming problem is solved to verify the feasibility of the Fuzzy-based Multi-Discipline Feasible(F-MDF) method.A multi-disciplinary analysis of a three-stage HRM powered LV involving the disciplines of propulsion,structure,aerodynamics and trajectory is implemented,and the mathematical models corresponding to the F-MDF method and the MDF method are established.A two-phase optimization method is proposed for multi-disciplinary design optimization of the LV,including the orbital capacity optimization phase based on the Ziolkowski formula,and the scheme trajectory verification phase based on the 3-degree-of-freedom point trajectory simulation.The correlation coefficients and the quadratic Response Surface Method (RSM) based on Latin Hypercube Sampling (LHS) are adopted for sensitive analysis of uncertain factors,and the Multi-Island Genetic Algorithm (MIGA) is adopted as the optimization algorithm.The results show that the F-MDF method is applicable in LV conceptual design,and the design with the F-MDF method is more reliable and robust than that with the MDF method.展开更多
In order to compare two advanced multi-objective evolutionary algorithms,a multi-objective water distribution problem is formulated in this paper.The multi-objective optimization has received more attention in the wat...In order to compare two advanced multi-objective evolutionary algorithms,a multi-objective water distribution problem is formulated in this paper.The multi-objective optimization has received more attention in the water distribution system design.On the one hand the cost of water distribution system including capital,operational,and maintenance cost is mostly concerned issue by the utilities all the time;on the other hand improving the performance of water distribution systems is of equivalent importance,which is often conflicting with the previous goal.Many performance metrics of water networks are developed in recent years,including total or maximum pressure deficit,resilience,inequity,probabilistic robustness,and risk measure.In this paper,a new resilience metric based on the energy analysis of water distribution systems is proposed.Two optimization objectives are comprised of capital cost and the new resilience index.A heuristic algorithm,speedconstrained multi-objective particle swarm optimization( SMPSO) extended on the basis of the multi-objective particle swarm algorithm,is introduced to compare with another state-of-the-art heuristic algorithm,NSGA-II.The solutions are evaluated by two metrics,namely spread and hypervolume.To illustrate the capability of SMPSO to efficiently identify good designs,two benchmark problems( two-loop network and Hanoi network) are employed.From several aspects the results demonstrate that SMPSO is a competitive and potential tool to tackle with the optimization problem of complex systems.展开更多
The 2MW wind turbine tower is considered as the baseline configuration for structural optimization.The design variables consist of the thickness and height located at the top tower junction.The relationships between t...The 2MW wind turbine tower is considered as the baseline configuration for structural optimization.The design variables consist of the thickness and height located at the top tower junction.The relationships between the design variables and the optimization objectives(mass,equivalent stress,top displacement and fatigue life)are mapped on the basis of uniform design and regression analysis.Subsequently,five solutions are developed by an algorithm,NSGA-III.According to their efficiency and applicability,the most suitable solution is found.This approach yields a decrease of 0.48%in the mass,a decrease of 54.48%in the equivalent stress and an increase of 8.14%in fatigue life,as compared with existing tower designs.An improved wind turbine tower is obtained for this practice.展开更多
基金National Science Fund for Distinguished Young Scholars (10425208)Programme of Introducing Talents of Discipline to Universities (B07009)
文摘For dealing with the multi-objective optimization problems of parametric design for aircraft, a novel hybrid parallel multi-objective tabu search （HPMOTS） algorithm is used. First, a new multi-objective tabu search （MOTS） algorithm is proposed. Comparing with the traditional MOTS algorithm, this proposed algorithm adds some new methods such as the combination of MOTS algorithm and ＂Pareto solution＂, the strategy of ＂searching from many directions＂ and the reservation of good solutions. Second, this article also proposes the improved parallel multi-objective tabu search （PMOTS） algorithm. Finally, a new hybrid algorithm--HPMOTS algorithm which combines the PMOTS algorithm with the non-dominated sorting-based multi-objective genetic algorithm （NSGA） is presented. The computing results of these algorithms are compared with each other and it is shown that the optimal result can be obtained by the HPMOTS algorithm and the computing result of the PMOTS algorithm is better than that of MOTS algorithm.
基金National Natural Science Foundation of China under Grant No.52278490。
文摘Finding an optimal isolator arrangement for asymmetric structures using traditional conceptual design methods that can significantly minimize torsional response while ensuring efficient horizontal seismic isolation is cumbersome and inefficient.Thus,this work develops a multi-objective optimization method to enhance the torsional resistance of asymmetric base-isolated structures.The primary objective is to simultaneously minimize the interstory rotation of the superstructure,the rotation of the isolation layer,and the interstory displacement of the superstructure without exceeding the isolator displacement limits.A fast non-dominated sorting genetic algorithm(NSGA-Ⅱ)is employed to satisfy this optimization objective.Subsequently,the isolator arrangement,encompassing both positions and categories,is optimized according to this multi-objective optimization method.Additionally,an optimization design platform is developed to streamline the design operation.This platform integrates the input of optimization parameters,the output of optimization results,the finite element analysis,and the multi-objective optimization method proposed herein.Finally,the application of this multi-objective optimization method and its associated platform are demonstrated on two asymmetric base-isolated structures of varying heights and plan configurations.The results indicate that the optimal isolator arrangement derived from the optimization method can further improve the control over the lateral and torsional responses of asymmetric base-isolated structures compared to conventional conceptual design methods.Notably,the interstory rotation of the optimal base-isolated structure is significantly reduced,constituting only approximately 33.7%of that observed in the original base-isolated structure.The proposed platform facilitates the automatic generation of the optimal design scheme for the isolators of asymmetric base-isolated structures,offering valuable insights and guidance for the burgeoning field of intelligent civil engineering design.
基金financially supported by the National Natu-ral Science Foundation of China(No.52301133)the China Post-doctoral Science Foundation(No.2023M730276)+1 种基金the Young Elite Scientists Sponsorship Program by China Association for Science and Technology(No.YESS20210415)the Graduate Innovation Pro-gram of Chongqing University of Science and Technology(No.YKJCX2320218).
文摘Designing compositions and processing of biodegradable magnesium(Mg)alloys to synergistically en-hance mechanical properties and corrosion resistance using conventional trial-and-error method is a challenging task.This study presents a Bayesian optimization(BO)-based multi-objective framework inte-grated with explainable machine learning(ML)to efficiently explore and optimize the high-dimensional design space of biodegradable Mg alloys.Using ultimate tensile strength(UTS),elongation(EL)and cor-rosion potential(E_(corr))as objective properties,the framework balances these conflicting objectives and identifies optimal solutions.A novel biodegradable Mg alloy(Mg-4.6Zn-0.3Y-0.2Mn-0.1Nd-0.1Gd,wt.%)was successfully designed,demonstrating a UTS of 320 MPa,EL of 22%and E_(corr) of−1.60 V(tested in 37℃ simulated body fluid).Compared to JDBM,the UTS has increased by 13 MPa,the EL has improved by 6.1%,and the E_(corr) has risen by 0.02 V.The experimental results presented close agreement with predicted values,validating the proposed framework.The Shapley Additive Explanation method was em-ployed to interpret the ML models,revealing extrusion temperature and Zn content as key parameters driving the optimization design.The strategy provided in this study is universal and offers a potential approach for addressing high-dimensional multi-objective optimization challenges in material develop-ment.
基金supported by National Hi-tech Research and Development Program of China(863 Program, Grant No. 2007AA04Z132)National Natural Science Foundation of China(Grant No. 51175379)
文摘Typical multidisciplinary design optimization（MDO） has gradually been proposed to balance performances of lightweight, noise, vibration and harshness（NVH） and safety for instrument panel（IP） structure in the automotive development. Nevertheless, plastic constitutive relation of Polypropylene（PP） under different strain rates, has not been taken into consideration in current reliability-based and collaborative IP MDO design. In this paper, based on tensile test under different strain rates, the constitutive relation of Polypropylene material is studied. Impact simulation tests for head and knee bolster are carried out to meet the regulation of FMVSS 201 and FMVSS 208, respectively. NVH analysis is performed to obtain mainly the natural frequencies and corresponding mode shapes, while the crashworthiness analysis is employed to examine the crash behavior of IP structure. With the consideration of lightweight, NVH, head and knee bolster impact performance, design of experiment（DOE）, response surface model（RSM）, and collaborative optimization（CO） are applied to realize the determined and reliability-based optimizations, respectively. Furthermore, based on multi-objective genetic algorithm（MOGA）, the optimal Pareto sets are completed to solve the multi-objective optimization（MOO） problem. The proposed research ensures the smoothness of Pareto set, enhances the ability of engineers to make a comprehensive decision about multi-objectives and choose the optimal design, and improves the quality and efficiency of MDO.
基金the National Natural Science Foundation of China(Nos.41704002,91638203,41904038)。
文摘Low Earth Orbit(LEO)satellite for navigation augmentation applications can significantly reduce the precise positioning convergence time and attract increasing attention recently.A few LEO Navigation Augmentation(LEO-NA)constellations have been proposed,while corresponding constellation design methodologies have not been systematically studied.The LEO-NA constellation generally consists of a huge number of LEO satellites and it strives for multiple optimization purposes.It is essentially different from the communication constellation or earth observing constellation design problem.In this study,we modeled the LEO-NA constellation design problem as a multi-objective optimization problem and solve this problem with the MultiObjective Particle Swarm Optimization(MOPSO)algorithm.Three objectives are used to strive for the best tradeoff between the augmentation performance and deployment efficiency,namely the Position Dilution of Precision(PDOP),visible LEO satellites and the orbit altitude.A fuzzy set approach is used to select the final constellation from a set of Pareto optimal solutions given by the MOPSO algorithm.To evaluate the performance of the optimized constellation,we tested two constellations with 144 and 288 satellites and each constellation has three optimization schemes:the polar constellation,the single-layer constellation and the two-layer constellation.The results indicate that the optimized two-layer constellation achieves the best global coverage and is followed by the single-layer constellation.The MOPSO algorithm can help to improve the constellation design and is suitable for solving the LEO-NA constellation design problem.
基金supported by National Natural Science Foundation of China(Grant No.50975033,Grant No.60875046)Program of Education Office of Liaoning Province,China(Grant No.LT2010074)
文摘It is a complicated problem for the bottom-to-top adaptive conceptual design of complicated products between structure and function. Reliable theories demand to be found in order to determine whether the structure accords with the requirement of design. For the requirement generally is dynamic variety as time passes, new requirements will come, and some initial requirements can no longer be used. The number of product requirements, the gene length expressing requirements, the structure of the product, and the correlation matrix are varied with individuation of customer requirements of the product. By researching on the calculation mechanisms of dynamic variety, the approaches of gene expression and variable length gene expression are proposed. According to the diversity of structure selection in conceptual design and mutual relations between structure and function as well as structure and structure, the correlation matrixes between structure and function as well as structure and structure are defined. By the approach of making the sum of the elements of correlation matrix maximum, the mathematical models of multi-object optimization for structure design are provided based on variable requirements. An improved genetic algorithm called segment genetic algorithm is proposed based on optimization preservation simple genetic algorithm. The models of multi-object optimization are calculated by the segment genetic algorithm and hybrid genetic algorithm. An example for the conceptual design of a washing machine is given to show that the proposed method is able to realize the optimization structure design fitting for variable requirements. In addition, the proposed approach can provide good Pareto optimization solutions, and the individuation customer requirements for structures of products are able to be resolved effectively.
基金This work was supported by Sichuan Science and Technology Program under the Contract No.2020JDJQ0036.
文摘Although various types of anti-roll torsion bars have been developed to inhibit excessive roll angle of the electric multiple unit(EMU)car body,it is critical to ensure the reliability of structural design due to the complexity of the problems involving time and uncertainties.To address this issue,amulti-objective fuzzy design optimization model is constructed considering time-variant stiffness and strength reliability constraints for the anti-roll torsion bar.A hybrid optimization strategy combining the design of experiment(DoE)sampling and non-linear programming by quadratic lagrangian(NLPQL)is presented to deal with the design optimization model.To characterize the effect of time on the structural performance of the torsion bar,the continuous-time model combined with Ito lemma is proposed to establish the time-variant stiffness and strength reliability constraints.Fuzzy mathematics is employed to conduct uncertainty quantification for the design parameters of the torsion bar.A physical programming approach is used to improve the designer’s preference and to make the optimization results more consistent with engineering practices.Moreover,the effectiveness of the proposed method has been validated by comparing with current methods in a practical engineering case.
基金The authors are grateful to the National Natural Science Foundation of China(Grant No.11902183)the Doctoral Research Foundation of Shandong University of Technology(Grant No.4041/418017).
文摘Thin-walled structures have been used in many fields due to their superior mechanical properties.In this paper,two types of hierarchical multi-cell tubes,inspired by the self-similarity of Pinus sylvestris,are proposed to enhance structural energy absorption performance.The finite element models of the hierarchical structures are established to validate the crashworthiness performance under axial dynamic load.The theoreticalmodel of themean crushing force is also derived based on the simplified super folded element theory.The finite element results demonstrate that the energy absorption characteristics and deformation mode of the bionic hierarchical thin-walled tubes are further improved with the increase of hierarchical sub-structures.It can be also obtained that the energy absorption performance of corner self-similar tubes is better than edge self-similar tubes.Furthermore,multiobjective optimization of the hierarchical tubes is constructed by employing the response surface method and genetic algorithm,and the corresponding Pareto front diagram is obtained.This research provides a new idea for the crashworthiness design of thin-walled structures.
基金Supported by Dalian University of Technology, the US National Science Foundation （No.CTS-0407494） and the Texas Advanced Technology program （No.003581-0044-2003）
文摘In many circumstances, chemical process design can be formulated as a multi-objective optimization （MOO） problem. Examples include bi-objective optimization problems, where the economic objective is maximized and environmental impact is minimized simultaneously. Moreover, the random behavior in the process,property, market fluctuation, errors in model prediction and so on would affect the performance of a process. Therefore, it is essential to develop a MOO methodology under uncertainty. In this article, the authors propose a generic and systematic optimization methodology for chemical process design under uncertainty. It aims at identifying the optimal design from a number of candidates. The utility of this methodology is demonstrated by a case study based on the design of a condensate treatment unit in an ammonia plant.
基金This work was supported in part by the National Natural Science Foundation of China under Grant51507016。
文摘For an optimal design of a surface-mounted permanent magnet synchronous motor(SPMSM),many objective functions should be considered.The classical optimization methods,which have been habitually designed based on magnetic circuit law or finite element analysis(FEA),have inaccuracy or calculation time problems when solving the multi-objective problems.To address these problems,the multi-independent-population genetic algorithm(MGA)combined with subdomain(SD)model are proposed to improve the performance of SPMSM such as magnetic field distribution,cost and efficiency.In order to analyze the flux density harmonics accurately,the accurate SD model is first established.Then,the MGA with time-saving SD model are employed to search for solutions which belong to the Pareto optimal set.Finally,for the purpose of validation,the electromagnetic performance of the new design motor are investigated by FEA,comparing with the initial design and conventional GA optimal design to demonstrate the advantage of MGA optimization method.
文摘Purpose–The nose length is the key design parameter affecting the aerodynamic performance of high-speed maglev train,and the horizontal profile has a significant impact on the aerodynamic lift of the leading and trailing cars Hence,the study analyzes aerodynamic parameters with multi-objective optimization design.Design/methodology/approach–The nose of normal temperature and normal conduction high-speed maglev train is divided into streamlined part and equipment cabin according to its geometric characteristics.Then the modified vehicle modeling function(VMF)parameterization method and surface discretization method are adopted for the parametric design of the nose.For the 12 key design parameters extracted,combined with computational fluid dynamics(CFD),support vector machine(SVR)model and multi-objective particle swarm optimization(MPSO)algorithm,the multi-objective aerodynamic optimization design of highspeed maglev train nose and the sensitivity analysis of design parameters are carried out with aerodynamic drag coefficient of the whole vehicle and the aerodynamic lift coefficient of the trailing car as the optimization objectives and the aerodynamic lift coefficient of the leading car as the constraint.The engineering improvement and wind tunnel test verification of the optimized shape are done.Findings–Results show that the parametric design method can use less design parameters to describe the nose shape of high-speed maglev train.The prediction accuracy of the SVR model with the reduced amount of calculation and improved optimization efficiency meets the design requirements.Originality/value–Compared with the original shape,the aerodynamic drag coefficient of the whole vehicle is reduced by 19.2%,and the aerodynamic lift coefficients of the leading and trailing cars are reduced by 24.8 and 51.3%,respectively,after adopting the optimized shape modified according to engineering design requirements.
基金supported by the Spanish Ministry of Science and Innovation(No. BIA2011-23602)the European Community with the European Regional Development Fund (FEDER), Spain
文摘This paper describes one approach to the design of reinforced concrete (RC) bridge piers, using a three-hybrid multi- objective simulated annealing (SA) algorithm with a neighborhood move based on the mutation operator from the genetic algorithms (GAs), namely MOSAMO1, MOSAMO2 and MOSAMO3. The procedure is applied to three objective functions: the economic cost, the reinforcing steel congestion and the embedded CO 2 emissions. Additional results for a random walk and a descent local search multi-objective algorithm are presented. The evaluation of solutions follows the Spanish Code for structural concrete. The methodology was applied to a typical bridge pier of 23.97 m in height. This example involved 110 design variables. Results indicate that algorithm MOSAMO2 outperforms other algorithms regarding the definition of Pareto fronts. Further, the proposed procedure will help structural engineers to enhance their bridge pier designs.
文摘The evolutionary strategy with a dynamic weighting schedule is proposed to find all the compromised solutions of the multi-objective integrated structure and control optimization problem, where the optimal system performance and control cost are defined by H2 or H∞ norms. During this optimization process, the weights are varying with the increasing generation instead of fixed values. The proposed strategy together with the linear matrix inequality （LMI） or the Riccati controller design method can find a series of uniformly distributed nondominated solutions in a single run. Therefore, this method can greatly reduce the computation intensity of the integrated optimization problem compared with the weight-based single objective genetic algorithm. Active automotive suspension is adopted as an example to illustrate the effectiveness of the proposed method.
文摘In the current scenario of global competition and short product life cycles, customer-defined satisfaction has attracted interest in artifact design. Accordingly, intelligent decision-making through multi-objective optimization has been proposed as an efficient method for human-centered manufacturing. However, previous vast researches on optimization have been mainly focused on optimization theory and optimization techniques and paid little interests on the process of problem formulation itself. In this paper, therefore, the authors present a total framework for supporting multi-objective decision making. Then, the authors try to solve the formulated multi-objective optimization problem that involves both qualitative and quantitative performance measures as a general consequence from the above procedure. Taking especially quality as a qualitative measure, the authors gave a new idea to evaluate the quality quantitatively. Additionally, to facilitate the portability of the proposed method in multidisciplinary decision-making environments, the authors implement the proposal algorithm in an Excel spreadsheet and validate the effectiveness of the approach through a case study.
基金financed with the means of Yingkou Institute of Technology Introduction of doctors to start the fund project (YJRC202109).
文摘For high-speed heavy-duty gears in operation is prone to high tooth surface temperature rise and thus produce tooth surface gluing leading to transmission failure and other adverse effects,but in the gear optimization design and little consideration of thermal transmission errors and thermal resonance and other factors,while the conventional multi-objective optimization design methods are difficult to achieve the optimum of each objective.Based on this,the paper proposes a gear multi-objective reliability optimisation design method based on the APCK-SORA model.The PC-Kriging model and the adaptive k-means clustering method are combined to construct an adaptive reliability analysis method(APCK for short),which is then integrated with the SORA optimisation algorithm.The objective function is the lightweight of gear pair,the maximum overlap degree and the maximum anti-glue strength;the basic parameters of the gear and the sensitivity parameters affecting the thermal deformation and thermal resonance of the gear are used as design variables;the amount of thermal deformation and thermal resonance,as well as the contact strength of the tooth face and the bending strength of the tooth root are used as constraints;the optimisation results show that:the mass of the gear is reduced by 0.13kg,the degree of overlap is increased by 0.016 and the coefficient of safety against galling Compared with other methods,the proposed method is more efficient than the other methods in meeting the multi-objective reliability design requirements of lightweighting,ensuring smoothness and anti-galling capability of high-speed heavy-duty gears.
基金supported in part by the National Key R&D Program of China(No.2016YFB1200100)
文摘This paper proposes a hybrid architecture based on Multi-disciplinary Design Optimization(MDO) with the Variable Complexity Modeling(VCM) method, to solve the problem of general design optimization for a stratosphere airship. Firstly, MDO based on the Concurrent SubSpace Optimization(CSSO) strategy is improved for handling the subsystem coupling problem in stratosphere airship design which contains aerodynamics, structure, and energy. Secondly, the VCM method based on the surrogate model is presented for reducing the computational complexity in high-fidelity modeling without loss of accuracy. Moreover, the global-to-local optimization strategy is added to the architecture to enhance the process. Finally, the result gives a prominent stratosphere airship general solution that validates the feasibility and efficiency of the optimization architecture. Besides, a sensitivity analysis is conducted to outline the critical impact upon stratosphere airship design.
基金supported by the Fundamental Research Funds for the Central Universities (Nos. 3102019JC009 and G2016KY0002)
文摘Blended-Wing-Body(BWB) configuration, as an innovative transport concept, has become a worldwide research focus in the field of civil transports development. Relative to the conventional Tube-And-Wing(TAW) configuration, the BWB shows integrated benefits and serves as a most promising candidate for future ‘‘green aviation'. The objective of the present work is to figure out the effects of the stability margin and Thrust Specific Fuel Consumption(TSFC) on the BWB design in the framework of Multi-Disciplinary Optimization(MDO). A physically-based platform was promoted to study the effect static stability margin and engine technology level. Low-order physically based models are applied to the evaluation of the weight and the aerodynamic performance. The modules and methods are illustrated in detail, and the validation of the methods shows feasibility and confidence for the conceptual design of BWB aircrafts. In order to find out the relation between planform changes and the selection of stability and engine technology level, two sets of optimizations are conducted separately. The study proves that these two factors have dominant effects towards the optimized BWB designs in both aerodynamic shapes, weight distribution, which needs to be considered during the MDO design process. A balance diagram analysis is applied to find out a reasonable static stability margin range. It can be concluded that a recommended stability margin of a practical BWB commercial aircraft can be half of that of a conventional TAW design.
基金supported by National Natural Science Foundation of China (No. 51305014)
文摘In this paper,an Uncertainty-based Multi-disciplinary Design Optimization (UMDO)method combining with fuzzy theory and Multi-Discipline Feasible (MDF) method is developed for the conceptual design of a Hybrid Rocket Motor (HRM) powered Launch Vehicle (LV).In the method proposed,membership functions are used to represent the uncertain factors,the fuzzy statistical experiment is introduced to analyze the propagation of uncertainties,and means,standard deviations and credibility measures are used to delineate uncertain responses.A geometric programming problem is solved to verify the feasibility of the Fuzzy-based Multi-Discipline Feasible(F-MDF) method.A multi-disciplinary analysis of a three-stage HRM powered LV involving the disciplines of propulsion,structure,aerodynamics and trajectory is implemented,and the mathematical models corresponding to the F-MDF method and the MDF method are established.A two-phase optimization method is proposed for multi-disciplinary design optimization of the LV,including the orbital capacity optimization phase based on the Ziolkowski formula,and the scheme trajectory verification phase based on the 3-degree-of-freedom point trajectory simulation.The correlation coefficients and the quadratic Response Surface Method (RSM) based on Latin Hypercube Sampling (LHS) are adopted for sensitive analysis of uncertain factors,and the Multi-Island Genetic Algorithm (MIGA) is adopted as the optimization algorithm.The results show that the F-MDF method is applicable in LV conceptual design,and the design with the F-MDF method is more reliable and robust than that with the MDF method.
基金Sponsored by the Project of Application Technology Research and Development Plan in Heilongjiang Province(Grant No.GA13C302)
文摘In order to compare two advanced multi-objective evolutionary algorithms,a multi-objective water distribution problem is formulated in this paper.The multi-objective optimization has received more attention in the water distribution system design.On the one hand the cost of water distribution system including capital,operational,and maintenance cost is mostly concerned issue by the utilities all the time;on the other hand improving the performance of water distribution systems is of equivalent importance,which is often conflicting with the previous goal.Many performance metrics of water networks are developed in recent years,including total or maximum pressure deficit,resilience,inequity,probabilistic robustness,and risk measure.In this paper,a new resilience metric based on the energy analysis of water distribution systems is proposed.Two optimization objectives are comprised of capital cost and the new resilience index.A heuristic algorithm,speedconstrained multi-objective particle swarm optimization( SMPSO) extended on the basis of the multi-objective particle swarm algorithm,is introduced to compare with another state-of-the-art heuristic algorithm,NSGA-II.The solutions are evaluated by two metrics,namely spread and hypervolume.To illustrate the capability of SMPSO to efficiently identify good designs,two benchmark problems( two-loop network and Hanoi network) are employed.From several aspects the results demonstrate that SMPSO is a competitive and potential tool to tackle with the optimization problem of complex systems.
基金the National Natural Science Founda-tion of China(Nos.51965034 and 51565028)LanzhouTalent Innovation and Entrepreneurship Project(No.2018-RC-25)。
文摘The 2MW wind turbine tower is considered as the baseline configuration for structural optimization.The design variables consist of the thickness and height located at the top tower junction.The relationships between the design variables and the optimization objectives(mass,equivalent stress,top displacement and fatigue life)are mapped on the basis of uniform design and regression analysis.Subsequently,five solutions are developed by an algorithm,NSGA-III.According to their efficiency and applicability,the most suitable solution is found.This approach yields a decrease of 0.48%in the mass,a decrease of 54.48%in the equivalent stress and an increase of 8.14%in fatigue life,as compared with existing tower designs.An improved wind turbine tower is obtained for this practice.
