The need to transport goods across countries and islands has resulted in a high demand for commercial vessels.Owing to such trends,shipyards must efficiently produce ships to reduce production costs.Layout and materia...The need to transport goods across countries and islands has resulted in a high demand for commercial vessels.Owing to such trends,shipyards must efficiently produce ships to reduce production costs.Layout and material flow are among the crucial aspects determining the efficiency of the production at a shipyard.This paper presents the initial design optimization of a shipyard layout using Nondominated Sorting Algorithm-Ⅱ(NSGA-Ⅱ)to find the optimal configuration of workstations in a shipyard layout.The proposed method focuses on simultaneously minimizing two material handling costs,namely work-based material handling and duration-based material handling.NSGA-Ⅱ determines the order of workstations in the shipyard layout.The semiflexible bay structure is then used in the workstation placement process from the sequence formed in NSGA-Ⅱ into a complete design.Considering that this study is a case of multiobjective optimization,the performance for both objectives at each iteration is presented in a 3D graph.Results indicate that after 500 iterations,the optimal configuration yields a work-based MHC of 163670.0 WBM-units and a duration-based MHC of 34750 DBM-units.Starting from a random solution,the efficiency of NSGA-Ⅱ demonstrates significant improvements,achieving a 50.19%reduction in work-based MHC and a 48.58%reduction in duration-based MHC.展开更多
In situ recycling is one of the most effective methods to dispose of earth pressure balance(EPB)shield waste muck with residual foaming agents with high moisture content.In this context,response surface methodology(RS...In situ recycling is one of the most effective methods to dispose of earth pressure balance(EPB)shield waste muck with residual foaming agents with high moisture content.In this context,response surface methodology(RSM)was employed to quantify the effects of independent variables,including flocculant dosage,defoamer dosage,and muck drying mass(MDM)and their interactions on defoaming-flocculation-dewatering indices.The polymeric aluminum chloride(PACL)and hydroxy silicone oil-glycerol polypropylene ether(H-G)were selected as the flocculant and defoamer.The contents of surfactants and foam stabilizers in residual foaming agents were determined using the proposed empirical equation.The defoaming ratio,antifoaming ratio,turbidity,moisture content,filtration loss ratio,and fall cone penetration depth were considered as dependent variables.The accuracy of developed RSM models was verified by the analysis results of variance,residuals,and paired t-test.Combined with the desirability approach,an optimal mixing ratio of 0.078 wt%PACL,0.016 wt%H-G,and 27.882 wt%MDM was recommended,leading to a defoaming ratio of 98.34 vol%for residual foams and a moisture content of 56.72 wt%for pressure-filtration cakes.Our findings were demonstrated to be able to provide useful guidance for prediction and optimization of the in situ recycling indicators of EPB shield waste muck in metro tunnel construction sites.展开更多
Progressing beyond the stowage and deployment of reflectors and designing for multiple deployed states result in reflector shape reconfiguration,thus allowing for new functions including radiation pattern reconfigurat...Progressing beyond the stowage and deployment of reflectors and designing for multiple deployed states result in reflector shape reconfiguration,thus allowing for new functions including radiation pattern reconfiguration,and is valuable for space applications such as satellite-based radar and communications.This paper introduces a concept for achieving the deployment and shape reconfiguration of a paraboloid reflector using a 7R-8R(revolute joint)truss network.By realizing reconfigurability mechanically,complex electronic systems such as phased arrays can be avoided,and adopting a single-degree-of-freedom(DOF)design further reduces the number of required actuators.The proposed reflector is axisymmetric and can be doubly curved.It comprises a flexible mesh surface supported by a rigid truss network constructed from 7R and 8R linkages.Approximation of multiple target surfaces is achieved by synthesizing the truss network dimensions using a multiobjective optimization approach.The non-dominated sorting genetic algorithm is used in conjunction with analytical dimension parameterization and forward kinematics computation to determine the optimal dimensions for the truss network.In the resulting designs,the reflector follows a single-DOF trajectory,on which it unfolds from a compact stowed bundle toward a deployed state approximating a doubly curved target surface,then onwards to additional deployed states approximating different target surfaces.Design studies are conducted to demonstrate the reflector’s ability to approximate different target surfaces and continuously transform between such surfaces.This study proposes a new method for reconfiguring reflector shape mechanically,thus uniquely reconfiguring the shape of a doubly curved surface and achieving both deployment and shape reconfiguration under a unified single-DOF motion.展开更多
Path planning is a fundamental component in robotics and game artificial intelligence that considerably influences the motion efficiency of robots and unmanned aerial vehicles,as well as the realism and immersion of v...Path planning is a fundamental component in robotics and game artificial intelligence that considerably influences the motion efficiency of robots and unmanned aerial vehicles,as well as the realism and immersion of virtual environments.However,traditional algorithms are often limited to single-objective optimization and lack real-time adaptability to dynamic environments.This study addresses these limitations through a proposed realtime dynamic multiobjective(RDMO)path-planning algorithm based on an enhanced A^(*) framework.The proposed algorithm employs a queue-based structure and composite multiheuristic functions to dynamically manage game tasks and compute optimal paths under changing-map-connectivity conditions in real time.Simulation experiments are conducted using real-world road network data and benchmarked against mainstream hybrid approaches based on genetic algorithms(GAs)and simulated annealing(SA).The results show that the computational speed of the RDMO algorithm is 88 and 73 times faster than that of the GA-and SA-based solutions,respectively,while the total planned path length is reduced by 58%and 33%,respectively.In addition,the RDMO algorithm also shows excellent responsiveness to dynamic changes in map connectivity and can achieve real-time replanning with a minimal computational overhead.The research results prove that the RDMO algorithm provides a robust and efficient solution for multiobjective path planning in games and robotics applications and has a great application potential in improving system performance and user experience in related fields in the future.展开更多
To investigate the influence of different longitudinal constraint systems on the longitudinal displacement at the girder ends of a three-tower suspension bridge,this study takes the Cangrong Xunjiang Bridge as an engi...To investigate the influence of different longitudinal constraint systems on the longitudinal displacement at the girder ends of a three-tower suspension bridge,this study takes the Cangrong Xunjiang Bridge as an engineering case for finite element analysis.This bridge employs an unprecedented tower-girder constraintmethod,with all vertical supports placed at the transition piers at both ends.This paper aims to study the characteristics of longitudinal displacement control at the girder ends under this novel structure,relying on finite element(FE)analysis.Initially,based on the Weigh In Motion(WIM)data,a random vehicle load model is generated and applied to the finite elementmodel.Several longitudinal constraint systems are proposed,and their effects on the structural response of the bridge are compared.The most reasonable system,balancing girder-end displacement and transitional pier stress,is selected.Subsequently,the study examines the impact of different viscous damper parameters on key structural response indicators,including cumulative longitudinal displacement at the girder ends,maximum longitudinal displacement at the girder ends,cumulative longitudinal displacement at the pier tops,maximum longitudinal displacement at the pier tops,longitudinal acceleration at the pier tops,and maximum bending moment at the pier bottoms.Finally,the coefficient of variation(CV)-TOPSIS method is used to optimize the viscous damper parameters for multiple objectives.The results show that adding viscous dampers at the side towers,in addition to the existing longitudinal limit bearings at the central tower,can most effectively reduce the response of structural indicators.The changes in these indicators are not entirely consistent with variations in damping coefficient and velocity exponent.The damper parameters significantly influence cumulative longitudinal displacement at the girder ends,cumulative longitudinal displacement at the pier tops,and maximum bending moments at the pier bottoms.The optimal damper parameters are found to be a damping coefficient of 5000 kN/(m/s)0.2 and a velocity exponent of 0.2.展开更多
Conventional pit excavation engineering methods often struggle to manage the complex deformation patterns associated with asymmetric excavations,resulting in significant safety risks and increased project costs.These ...Conventional pit excavation engineering methods often struggle to manage the complex deformation patterns associated with asymmetric excavations,resulting in significant safety risks and increased project costs.These challenges highlight the need for more precise and efficient design methodologies to ensure structural stability and economic feasibility.This research proposes an innovative automatic optimization inverse design method(AOIDM)that integrates an enhanced genetic algorithm(EGA)with a multiobjective optimization model.By combining advanced computational techniques with engineering principles,this approach improves search efficiency by 30%and enhances deformation control accuracy by 25%.Additionally,the approach exhibits potential for reducing carbon emissions to align with sustainable engineering goals.The effectiveness of this approach was validated through comprehensive data analysis and practical case studies,demonstrating its ability to optimize retaining structure designs under complex asymmetric loading conditions.This research establishes a new standard for precision and efficiency in automated excavation design,with accompanying improvements in safety and cost-effectiveness.Furthermore,it lays the foundation for future geotechnical engineering advancements,offering a robust solution to one of the most challenging aspects of modern excavation projects.展开更多
To address the challenges of supply-demand imbal-ance in rail transit and the complex passenger flow interactions among multiple hub stations under high-passenger-volume scenarios,this study proposes an optimized rail...To address the challenges of supply-demand imbal-ance in rail transit and the complex passenger flow interactions among multiple hub stations under high-passenger-volume scenarios,this study proposes an optimized rail transit scheduling method based on a flexible train formation strat-egy(FTFS).By constructing interaction parameters that characterize the coupling effects of high passenger flow across multiple hubs,a multiobjective optimization model is developed to minimize passenger waiting time at hub sta-tions and operational costs.An improved nondominated sorting genetic algorithm incorporating chaotic mapping and adaptive evolutionary parameters is designed for efficient so-lution optimization.This method overcomes the limitations of fixed train formations by supporting diversified modular unit detachment and reconnection,enabling dynamic capac-ity adjustment and efficient rolling stock circulation.A case study on Nanjing Metro Line 1 demonstrates that the FTFS reduces the average waiting time at hub stations by 47.2%,alleviates train congestion by approximately 18.6%,and re-duces the operational costs under low-demand scenarios by 44.8%.Pareto frontier analysis further reveals the trade-off mechanism between transport capacity elasticity and opera-tional costs.These findings validate the effectiveness of the flexible train formation model in mitigating platform conges-tion and enhancing passenger flow evacuation efficiency at transport hubs,providing multiobjective decision-making support for managing extreme passenger flow during holi-days and peak events.展开更多
To optimize the operating efficiency and extend the lifespan of the multistack fuel cell hybrid system(MFCHS),this paper proposes a two-layer multiobjective optimal energy management strategy that considers the degrad...To optimize the operating efficiency and extend the lifespan of the multistack fuel cell hybrid system(MFCHS),this paper proposes a two-layer multiobjective optimal energy management strategy that considers the degradation of the fuel cell and the battery.Regarding the issues that power fluctuations damage the fuel cells'lifespan and high-current charging and discharging lead to battery capacity decay,the first layer of the strategy adopts locally weighted scatterplot smoothing(LOWESS)to smooth the output power of the fuel cells and prevent the battery from operating under high-current conditions.The second layer considers the uneven degree of degradation among the fuel cells and employs the dandelion optimizer(DO)algorithm to solve the objective function with an aging adaptive factor,optimizing the efficiency and lifespan.Meanwhile,the DO algorithm is enhanced by tent chaotic mapping and differential variation to improve the convergence speed and accuracy.Compared with the equivalent hydrogen consumption minimization strategy(ECMS)and the equal distribution strategy,the proposed strategy improves the average operating efficiency of the fuel cells,effectively reduces the degradation of the fuel cells and the capacity degradation of the battery,and maintains the performance consistency among the fuel cells.展开更多
This paper deals with extensions of higher-order optimality conditions for scalar optimization to multiobjective optimization.A type of directional derivatives for a multiobjective function is proposed,and with this n...This paper deals with extensions of higher-order optimality conditions for scalar optimization to multiobjective optimization.A type of directional derivatives for a multiobjective function is proposed,and with this notion characterizations of strict local minima of order k for a multiobjective optimization problem with a nonempty set constraint are established,generalizing the corresponding scalar case obtained by Studniarski[3].Also necessary not sufficient and sufficient not necessary optimality conditions for this minima are derived based on our directional derivatives,which are generalizations of some existing scalar results and equivalent to some existing multiobjective ones.Many examples are given to illustrate them there.展开更多
实数编码的多目标进化算法常使用模拟二进制交叉(simulated binary crossover,称SBX)算子.通过对SBX以及进化策略中变异算子进行对比分析,并引入进化策略中的离散重组算子,提出了一种正态分布交叉(normal distribution crossover,称NDX...实数编码的多目标进化算法常使用模拟二进制交叉(simulated binary crossover,称SBX)算子.通过对SBX以及进化策略中变异算子进行对比分析,并引入进化策略中的离散重组算子,提出了一种正态分布交叉(normal distribution crossover,称NDX)算子.首先在一维搜索空间实例中对NDX与SBX算子进行比较和分析,然后将NDX算子应用于Deb等人提出的稳态多目标进化算法ε-MOEA(ε-dominance based multiobjective evolutionary algorithm)中.采用NDX算子的ε-MOEA(记为ε-MOEA/NDX)算法在多目标优化标准测试集ZDT和DTLZ的10个函数上进行了实验比较.实验结果和分析表明,采用NDX的ε-MOEA所求得的Pareto最优解集质量明显优于经典算法ε-MOEA/SBX和NSGA-Ⅱ.展开更多
In order to meet the requirements of combustion optimization for saving energy and reducing pollutant emission simultaneously,an immune cell subsets based multiobjective optimization algorithm（ICSMOA）is proposed.In ...In order to meet the requirements of combustion optimization for saving energy and reducing pollutant emission simultaneously,an immune cell subsets based multiobjective optimization algorithm（ICSMOA）is proposed.In the ICSMOA,the subset division operator and the immunological tolerance operation are defined.Preference can be easily addressed by using the subset division operator,and the distribution of the solutions can be guaranteed by the immunological tolerance operation.Using the ICSMOA,a group of Pareto optimal solutions can be obtained.However,by the traditional weighting method（WM）,only one solution can be obtained and it cannot be judged as Pareto optimal or not.In contrast to the solutions obtained by the repeatedly performed WM,the simulation results show that most solutions obtained by the ICSMOA are better than the solutions obtained by the WM.In addition,the Pareto front obtained by the ICSMOA is not as uniform as most classical multiobjective optimization algorithms.More optimal solutions which meet the preference set by the decision-maker can be obtained and they are very useful for industrial application.展开更多
Regularization inversion uses constraints and a regularization factor to solve ill- posed inversion problems in geophysics. The choice of the regularization factor and of the initial model is critical in regularizatio...Regularization inversion uses constraints and a regularization factor to solve ill- posed inversion problems in geophysics. The choice of the regularization factor and of the initial model is critical in regularization inversion. To deal with these problems, we propose a multiobjective particle swarm inversion （MOPSOI） algorithm to simultaneously minimize the data misfit and model constraints, and obtain a multiobjective inversion solution set without the gradient information of the objective function and the regularization factor. We then choose the optimum solution from the solution set based on the trade-off between data misfit and constraints that substitute for the regularization factor. The inversion of synthetic two-dimensional magnetic data suggests that the MOPSOI algorithm can obtain as many feasible solutions as possible; thus, deeper insights of the inversion process can be gained and more reasonable solutions can be obtained by balancing the data misfit and constraints. The proposed MOPSOI algorithm can deal with the problems of choosing the right regularization factor and the initial model.展开更多
Hanson and Mond have grven sets of necessary and sufficient conditions for optimality in constrained optimization by introducing classes of generalized functions, called type Ⅰ functions. Recently, Bector definded un...Hanson and Mond have grven sets of necessary and sufficient conditions for optimality in constrained optimization by introducing classes of generalized functions, called type Ⅰ functions. Recently, Bector definded univex functions, a new class of functions that unifies several concepts of generalized convexity. In this paper, additional conditions are attached to the Kuhn Tucker conditions giving a set of conditions which are both necessary and sufficient for optimality in constrained optimization, under appropriate constraint qualifications.展开更多
Two uncoupleable distributions, assigning missions to robots and allocating robots to home stations, accompany the use ofmobile service robots in hospitals.In the given problem, two workload-related objectives and fiv...Two uncoupleable distributions, assigning missions to robots and allocating robots to home stations, accompany the use ofmobile service robots in hospitals.In the given problem, two workload-related objectives and five groups of constraints areproposed.A bio-mimicked Binary Bees Algorithm (BBA) is introduced to solve this multiobjective multiconstraint combinatorialoptimisation problem, in which constraint handling technique (Multiobjective Transformation, MOT), multiobjectiveevaluation method (nondominance selection), global search strategy (stochastic search in the variable space), local searchstrategy (Hamming neighbourhood exploitation), and post-processing means (feasibility selection) are the main issues.TheBBA is then demonstrated with a case study, presenting the execution process of the algorithm, and also explaining the change ofelite number in evolutionary process.Its optimisation result provides a group of feasible nondominated two-level distributionschemes.展开更多
A systematic and effective optimization is proposed for the design of a three-dimensional （3-D） vehicle suspension model with eight degrees of freedom （DOF）, including vertical seat motion, vehicle suspension, pit...A systematic and effective optimization is proposed for the design of a three-dimensional （3-D） vehicle suspension model with eight degrees of freedom （DOF）, including vertical seat motion, vehicle suspension, pitching and rolling motions, and vertical wheel motions using the evolutionary game theory. A new design of the passive suspension is aided by game theory to attain the best compromise between ride quality and suspension deflections. Extensive simulations are performed on three type road surface models A, B, C pavement grades based on the guidelines provided by ISO-2631 with the Matlab/Simulink environment. The preliminary results show that, when the passive suspension is optimized via the proposed approach, a substantial improvement in the vertical ride quality is obtained while keeping the suspension deflections within their allowable clearance when the vehicle moves at a constant velocity v=20 m/s, and the comfort performance of a suspension seat can be enhanced by 20%-30%.展开更多
In order to incorporate the decision maker＇s preference into multiobjective optimization a preference-based multiobjective artificial bee colony algorithm PMABCA is proposed.In the proposed algorithm a novel referenc...In order to incorporate the decision maker＇s preference into multiobjective optimization a preference-based multiobjective artificial bee colony algorithm PMABCA is proposed.In the proposed algorithm a novel reference point based preference expression method is addressed.The fitness assignment function is defined based on the nondominated rank and the newly defined preference distance.An archive set is introduced for saving the nondominated solutions and an improved crowding-distance operator is addressed to remove the extra solutions in the archive.The experimental results of two benchmark test functions show that a preferred set of solutions and some other non-preference solutions are achieved simultaneously.The simulation results of the proportional-integral-derivative PID parameter optimization for superheated steam temperature verify that the PMABCA is efficient in aiding to making a reasonable decision.展开更多
The optional types of power source and actuator in the aircraft are more and more diverse due to fast development in more electric technology, which makes the combinations of different power sources and actuators beco...The optional types of power source and actuator in the aircraft are more and more diverse due to fast development in more electric technology, which makes the combinations of different power sources and actuators become extremely complex in the architecture optimization process of airborne actuation system. The traditional ＂trial and error＂ method cannot satisfy the design demands. In this paper, firstly, the composition of more electric aircraft （MEA） flight control actuation system （FCAS） is introduced, and the possible architecture quantity is calculated. Secondly, the evaluation criteria of FCAS architecture with respect to safe reliability, weight and efficiency are proposed, and the evaluation criteria values are calculated in the case that each control surface adopts the same actuator configuration. Finally, the optimization results of MEA FCAS architecture are obtained by applying genetic algorithm （GA）. Compared to the traditional actuation system architecture, which only adopts servo valve controlled hydraulic actuators, the weight of the optimized more electric actuation system architecture can be reduced by 6%, and the efficiency can be improved by 30% based on the safe reliability requirements.展开更多
This paper presents a path planning approach for rotary unmanned aerial vehicles(R-UAVs)in a known static rough terrain environment.This approach aims to find collision-free and feasible paths with minimum altitude,le...This paper presents a path planning approach for rotary unmanned aerial vehicles(R-UAVs)in a known static rough terrain environment.This approach aims to find collision-free and feasible paths with minimum altitude,length and angle variable rate.First,a three-dimensional(3D)modeling method is proposed to reduce the computation burden of the dynamic models of R-UAVs.Considering the length,height and tuning angle of a path,the path planning of R-UAVs is described as a tri-objective optimization problem.Then,an improved multi-objective particle swarm optimization algorithm is developed.To render the algorithm more effective in dealing with this problem,a vibration function is introduced into the collided solutions to improve the algorithm efficiency.Meanwhile,the selection of the global best position is taken into account by the reference point method.Finally,the experimental environment is built with the help of the Google map and the 3D terrain generator World Machine.Experimental results under two different rough terrains from Guilin and Lanzhou of China demonstrate the capabilities of the proposed algorithm in finding Pareto optimal paths.展开更多
In order to obtain accurate probability integration method(PIM) parameters for surface movement of multi-panel mining, a genetic algorithm(GA) was used to optimize the parameters. As the measured surface movement is a...In order to obtain accurate probability integration method(PIM) parameters for surface movement of multi-panel mining, a genetic algorithm(GA) was used to optimize the parameters. As the measured surface movement is affected by more than one mining panel, traditional PIM parameter inversion model is difficult to ensure the reliability of the results due to the complexity of rock movement. With crossover,mutation and selection operators, GA can perform a global optimization search and has high computation efficiency. Compared with the pattern search algorithm, the fitness function can avoid falling into local minima traps. GA reduces the risk of local minima traps which improves the accuracy and reliability with the mutation mechanism. Application at Xuehu colliery shows that GA can be used to inverse the PIM parameters for multi-panel surface movement observation, and reliable results can be obtained. The research provides a new way for back-analysis of PIM parameters for mining subsidence under complex conditions.展开更多
For multi-objective optimization problems,particle swarm optimization(PSO)algorithm generally needs a large number of fitness evaluations to obtain the Pareto optimal solutions.However,it will become substantially tim...For multi-objective optimization problems,particle swarm optimization(PSO)algorithm generally needs a large number of fitness evaluations to obtain the Pareto optimal solutions.However,it will become substantially time-consuming when handling computationally expensive fitness functions.In order to save the computational cost,a surrogate-assisted PSO with Pareto active learning is proposed.In real physical space(the objective functions are computationally expensive),PSO is used as an optimizer,and its optimization results are used to construct the surrogate models.In virtual space,objective functions are replaced by the cheaper surrogate models,PSO is viewed as a sampler to produce the candidate solutions.To enhance the quality of candidate solutions,a hybrid mutation sampling method based on the simulated evolution is proposed,which combines the advantage of fast convergence of PSO and implements mutation to increase diversity.Furthermore,ε-Pareto active learning(ε-PAL)method is employed to pre-select candidate solutions to guide PSO in the real physical space.However,little work has considered the method of determining parameterε.Therefore,a greedy search method is presented to determine the value ofεwhere the number of active sampling is employed as the evaluation criteria of classification cost.Experimental studies involving application on a number of benchmark test problems and parameter determination for multi-input multi-output least squares support vector machines(MLSSVM)are given,in which the results demonstrate promising performance of the proposed algorithm compared with other representative multi-objective particle swarm optimization(MOPSO)algorithms.展开更多
基金Supported by Direktorat Riset dan Pengembangan(Directorate of Research and Development)Universitas Indonesia(NKB-690/UN2.RST/HKP.05.00/2022).
文摘The need to transport goods across countries and islands has resulted in a high demand for commercial vessels.Owing to such trends,shipyards must efficiently produce ships to reduce production costs.Layout and material flow are among the crucial aspects determining the efficiency of the production at a shipyard.This paper presents the initial design optimization of a shipyard layout using Nondominated Sorting Algorithm-Ⅱ(NSGA-Ⅱ)to find the optimal configuration of workstations in a shipyard layout.The proposed method focuses on simultaneously minimizing two material handling costs,namely work-based material handling and duration-based material handling.NSGA-Ⅱ determines the order of workstations in the shipyard layout.The semiflexible bay structure is then used in the workstation placement process from the sequence formed in NSGA-Ⅱ into a complete design.Considering that this study is a case of multiobjective optimization,the performance for both objectives at each iteration is presented in a 3D graph.Results indicate that after 500 iterations,the optimal configuration yields a work-based MHC of 163670.0 WBM-units and a duration-based MHC of 34750 DBM-units.Starting from a random solution,the efficiency of NSGA-Ⅱ demonstrates significant improvements,achieving a 50.19%reduction in work-based MHC and a 48.58%reduction in duration-based MHC.
基金supported by the National Youth Top-notch Talent Support Program of China(Grant No.00389335)the National Natural Science Foundation of China(Grant No.52378392)the“Foal Eagle Program”Youth Top-notch Talent Project of Fujian Province(Grant No.00387088).
文摘In situ recycling is one of the most effective methods to dispose of earth pressure balance(EPB)shield waste muck with residual foaming agents with high moisture content.In this context,response surface methodology(RSM)was employed to quantify the effects of independent variables,including flocculant dosage,defoamer dosage,and muck drying mass(MDM)and their interactions on defoaming-flocculation-dewatering indices.The polymeric aluminum chloride(PACL)and hydroxy silicone oil-glycerol polypropylene ether(H-G)were selected as the flocculant and defoamer.The contents of surfactants and foam stabilizers in residual foaming agents were determined using the proposed empirical equation.The defoaming ratio,antifoaming ratio,turbidity,moisture content,filtration loss ratio,and fall cone penetration depth were considered as dependent variables.The accuracy of developed RSM models was verified by the analysis results of variance,residuals,and paired t-test.Combined with the desirability approach,an optimal mixing ratio of 0.078 wt%PACL,0.016 wt%H-G,and 27.882 wt%MDM was recommended,leading to a defoaming ratio of 98.34 vol%for residual foams and a moisture content of 56.72 wt%for pressure-filtration cakes.Our findings were demonstrated to be able to provide useful guidance for prediction and optimization of the in situ recycling indicators of EPB shield waste muck in metro tunnel construction sites.
基金Supported by National Natural Science Foundation of China(Grant Nos.52320105005,52035008)the New Cornerstone Science Foundation through the Xplorer Prize(Grant No.XPLORER-2020-1035).
文摘Progressing beyond the stowage and deployment of reflectors and designing for multiple deployed states result in reflector shape reconfiguration,thus allowing for new functions including radiation pattern reconfiguration,and is valuable for space applications such as satellite-based radar and communications.This paper introduces a concept for achieving the deployment and shape reconfiguration of a paraboloid reflector using a 7R-8R(revolute joint)truss network.By realizing reconfigurability mechanically,complex electronic systems such as phased arrays can be avoided,and adopting a single-degree-of-freedom(DOF)design further reduces the number of required actuators.The proposed reflector is axisymmetric and can be doubly curved.It comprises a flexible mesh surface supported by a rigid truss network constructed from 7R and 8R linkages.Approximation of multiple target surfaces is achieved by synthesizing the truss network dimensions using a multiobjective optimization approach.The non-dominated sorting genetic algorithm is used in conjunction with analytical dimension parameterization and forward kinematics computation to determine the optimal dimensions for the truss network.In the resulting designs,the reflector follows a single-DOF trajectory,on which it unfolds from a compact stowed bundle toward a deployed state approximating a doubly curved target surface,then onwards to additional deployed states approximating different target surfaces.Design studies are conducted to demonstrate the reflector’s ability to approximate different target surfaces and continuously transform between such surfaces.This study proposes a new method for reconfiguring reflector shape mechanically,thus uniquely reconfiguring the shape of a doubly curved surface and achieving both deployment and shape reconfiguration under a unified single-DOF motion.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(NRF-2023R1A2C1005950).
文摘Path planning is a fundamental component in robotics and game artificial intelligence that considerably influences the motion efficiency of robots and unmanned aerial vehicles,as well as the realism and immersion of virtual environments.However,traditional algorithms are often limited to single-objective optimization and lack real-time adaptability to dynamic environments.This study addresses these limitations through a proposed realtime dynamic multiobjective(RDMO)path-planning algorithm based on an enhanced A^(*) framework.The proposed algorithm employs a queue-based structure and composite multiheuristic functions to dynamically manage game tasks and compute optimal paths under changing-map-connectivity conditions in real time.Simulation experiments are conducted using real-world road network data and benchmarked against mainstream hybrid approaches based on genetic algorithms(GAs)and simulated annealing(SA).The results show that the computational speed of the RDMO algorithm is 88 and 73 times faster than that of the GA-and SA-based solutions,respectively,while the total planned path length is reduced by 58%and 33%,respectively.In addition,the RDMO algorithm also shows excellent responsiveness to dynamic changes in map connectivity and can achieve real-time replanning with a minimal computational overhead.The research results prove that the RDMO algorithm provides a robust and efficient solution for multiobjective path planning in games and robotics applications and has a great application potential in improving system performance and user experience in related fields in the future.
基金supported by the National Key Research and Development Program of China(No.2022YFB3706704)the Academician Special Science Research Project of CCCC(No.YSZX-03-2022-01-B).
文摘To investigate the influence of different longitudinal constraint systems on the longitudinal displacement at the girder ends of a three-tower suspension bridge,this study takes the Cangrong Xunjiang Bridge as an engineering case for finite element analysis.This bridge employs an unprecedented tower-girder constraintmethod,with all vertical supports placed at the transition piers at both ends.This paper aims to study the characteristics of longitudinal displacement control at the girder ends under this novel structure,relying on finite element(FE)analysis.Initially,based on the Weigh In Motion(WIM)data,a random vehicle load model is generated and applied to the finite elementmodel.Several longitudinal constraint systems are proposed,and their effects on the structural response of the bridge are compared.The most reasonable system,balancing girder-end displacement and transitional pier stress,is selected.Subsequently,the study examines the impact of different viscous damper parameters on key structural response indicators,including cumulative longitudinal displacement at the girder ends,maximum longitudinal displacement at the girder ends,cumulative longitudinal displacement at the pier tops,maximum longitudinal displacement at the pier tops,longitudinal acceleration at the pier tops,and maximum bending moment at the pier bottoms.Finally,the coefficient of variation(CV)-TOPSIS method is used to optimize the viscous damper parameters for multiple objectives.The results show that adding viscous dampers at the side towers,in addition to the existing longitudinal limit bearings at the central tower,can most effectively reduce the response of structural indicators.The changes in these indicators are not entirely consistent with variations in damping coefficient and velocity exponent.The damper parameters significantly influence cumulative longitudinal displacement at the girder ends,cumulative longitudinal displacement at the pier tops,and maximum bending moments at the pier bottoms.The optimal damper parameters are found to be a damping coefficient of 5000 kN/(m/s)0.2 and a velocity exponent of 0.2.
基金supported by the National Key R&D Program of China(Grant No.2023YFC3009400)the National Natural Science Foundation of China(Grant Nos.52238009 and 52208344).
文摘Conventional pit excavation engineering methods often struggle to manage the complex deformation patterns associated with asymmetric excavations,resulting in significant safety risks and increased project costs.These challenges highlight the need for more precise and efficient design methodologies to ensure structural stability and economic feasibility.This research proposes an innovative automatic optimization inverse design method(AOIDM)that integrates an enhanced genetic algorithm(EGA)with a multiobjective optimization model.By combining advanced computational techniques with engineering principles,this approach improves search efficiency by 30%and enhances deformation control accuracy by 25%.Additionally,the approach exhibits potential for reducing carbon emissions to align with sustainable engineering goals.The effectiveness of this approach was validated through comprehensive data analysis and practical case studies,demonstrating its ability to optimize retaining structure designs under complex asymmetric loading conditions.This research establishes a new standard for precision and efficiency in automated excavation design,with accompanying improvements in safety and cost-effectiveness.Furthermore,it lays the foundation for future geotechnical engineering advancements,offering a robust solution to one of the most challenging aspects of modern excavation projects.
基金Key Project of the National Natural Science Foundation of China (No. 52432011)the National Natural Science Foundation of China (No. 524B2153)。
文摘To address the challenges of supply-demand imbal-ance in rail transit and the complex passenger flow interactions among multiple hub stations under high-passenger-volume scenarios,this study proposes an optimized rail transit scheduling method based on a flexible train formation strat-egy(FTFS).By constructing interaction parameters that characterize the coupling effects of high passenger flow across multiple hubs,a multiobjective optimization model is developed to minimize passenger waiting time at hub sta-tions and operational costs.An improved nondominated sorting genetic algorithm incorporating chaotic mapping and adaptive evolutionary parameters is designed for efficient so-lution optimization.This method overcomes the limitations of fixed train formations by supporting diversified modular unit detachment and reconnection,enabling dynamic capac-ity adjustment and efficient rolling stock circulation.A case study on Nanjing Metro Line 1 demonstrates that the FTFS reduces the average waiting time at hub stations by 47.2%,alleviates train congestion by approximately 18.6%,and re-duces the operational costs under low-demand scenarios by 44.8%.Pareto frontier analysis further reveals the trade-off mechanism between transport capacity elasticity and opera-tional costs.These findings validate the effectiveness of the flexible train formation model in mitigating platform conges-tion and enhancing passenger flow evacuation efficiency at transport hubs,providing multiobjective decision-making support for managing extreme passenger flow during holi-days and peak events.
基金supported by the Postgraduate Research and Practice Innovation Program of Jiangsu Province(Grant No.SJCX24_0161)the National Natural Science Foundation of China(Grant Nos.61374153 and 61403199).
文摘To optimize the operating efficiency and extend the lifespan of the multistack fuel cell hybrid system(MFCHS),this paper proposes a two-layer multiobjective optimal energy management strategy that considers the degradation of the fuel cell and the battery.Regarding the issues that power fluctuations damage the fuel cells'lifespan and high-current charging and discharging lead to battery capacity decay,the first layer of the strategy adopts locally weighted scatterplot smoothing(LOWESS)to smooth the output power of the fuel cells and prevent the battery from operating under high-current conditions.The second layer considers the uneven degree of degradation among the fuel cells and employs the dandelion optimizer(DO)algorithm to solve the objective function with an aging adaptive factor,optimizing the efficiency and lifespan.Meanwhile,the DO algorithm is enhanced by tent chaotic mapping and differential variation to improve the convergence speed and accuracy.Compared with the equivalent hydrogen consumption minimization strategy(ECMS)and the equal distribution strategy,the proposed strategy improves the average operating efficiency of the fuel cells,effectively reduces the degradation of the fuel cells and the capacity degradation of the battery,and maintains the performance consistency among the fuel cells.
文摘This paper deals with extensions of higher-order optimality conditions for scalar optimization to multiobjective optimization.A type of directional derivatives for a multiobjective function is proposed,and with this notion characterizations of strict local minima of order k for a multiobjective optimization problem with a nonempty set constraint are established,generalizing the corresponding scalar case obtained by Studniarski[3].Also necessary not sufficient and sufficient not necessary optimality conditions for this minima are derived based on our directional derivatives,which are generalizations of some existing scalar results and equivalent to some existing multiobjective ones.Many examples are given to illustrate them there.
文摘实数编码的多目标进化算法常使用模拟二进制交叉(simulated binary crossover,称SBX)算子.通过对SBX以及进化策略中变异算子进行对比分析,并引入进化策略中的离散重组算子,提出了一种正态分布交叉(normal distribution crossover,称NDX)算子.首先在一维搜索空间实例中对NDX与SBX算子进行比较和分析,然后将NDX算子应用于Deb等人提出的稳态多目标进化算法ε-MOEA(ε-dominance based multiobjective evolutionary algorithm)中.采用NDX算子的ε-MOEA(记为ε-MOEA/NDX)算法在多目标优化标准测试集ZDT和DTLZ的10个函数上进行了实验比较.实验结果和分析表明,采用NDX的ε-MOEA所求得的Pareto最优解集质量明显优于经典算法ε-MOEA/SBX和NSGA-Ⅱ.
基金The National Natural Science Foundation of China(No.51036002,51076027)the Key Project of Ministry of Education of China(No.108060)
文摘In order to meet the requirements of combustion optimization for saving energy and reducing pollutant emission simultaneously,an immune cell subsets based multiobjective optimization algorithm（ICSMOA）is proposed.In the ICSMOA,the subset division operator and the immunological tolerance operation are defined.Preference can be easily addressed by using the subset division operator,and the distribution of the solutions can be guaranteed by the immunological tolerance operation.Using the ICSMOA,a group of Pareto optimal solutions can be obtained.However,by the traditional weighting method（WM）,only one solution can be obtained and it cannot be judged as Pareto optimal or not.In contrast to the solutions obtained by the repeatedly performed WM,the simulation results show that most solutions obtained by the ICSMOA are better than the solutions obtained by the WM.In addition,the Pareto front obtained by the ICSMOA is not as uniform as most classical multiobjective optimization algorithms.More optimal solutions which meet the preference set by the decision-maker can be obtained and they are very useful for industrial application.
基金supported by the Natural Science Foundation of China(No.61273179)Department of Education,Science and Technology Research Project of Hubei Province of China(No.D20131206,No.20141304)
文摘Regularization inversion uses constraints and a regularization factor to solve ill- posed inversion problems in geophysics. The choice of the regularization factor and of the initial model is critical in regularization inversion. To deal with these problems, we propose a multiobjective particle swarm inversion （MOPSOI） algorithm to simultaneously minimize the data misfit and model constraints, and obtain a multiobjective inversion solution set without the gradient information of the objective function and the regularization factor. We then choose the optimum solution from the solution set based on the trade-off between data misfit and constraints that substitute for the regularization factor. The inversion of synthetic two-dimensional magnetic data suggests that the MOPSOI algorithm can obtain as many feasible solutions as possible; thus, deeper insights of the inversion process can be gained and more reasonable solutions can be obtained by balancing the data misfit and constraints. The proposed MOPSOI algorithm can deal with the problems of choosing the right regularization factor and the initial model.
文摘Hanson and Mond have grven sets of necessary and sufficient conditions for optimality in constrained optimization by introducing classes of generalized functions, called type Ⅰ functions. Recently, Bector definded univex functions, a new class of functions that unifies several concepts of generalized convexity. In this paper, additional conditions are attached to the Kuhn Tucker conditions giving a set of conditions which are both necessary and sufficient for optimality in constrained optimization, under appropriate constraint qualifications.
文摘Two uncoupleable distributions, assigning missions to robots and allocating robots to home stations, accompany the use ofmobile service robots in hospitals.In the given problem, two workload-related objectives and five groups of constraints areproposed.A bio-mimicked Binary Bees Algorithm (BBA) is introduced to solve this multiobjective multiconstraint combinatorialoptimisation problem, in which constraint handling technique (Multiobjective Transformation, MOT), multiobjectiveevaluation method (nondominance selection), global search strategy (stochastic search in the variable space), local searchstrategy (Hamming neighbourhood exploitation), and post-processing means (feasibility selection) are the main issues.TheBBA is then demonstrated with a case study, presenting the execution process of the algorithm, and also explaining the change ofelite number in evolutionary process.Its optimisation result provides a group of feasible nondominated two-level distributionschemes.
基金Supported by Program for New Century Excellent Talents in University (070003)the Natural Science Foundation of Anhui Province (070414154)~~
文摘A systematic and effective optimization is proposed for the design of a three-dimensional （3-D） vehicle suspension model with eight degrees of freedom （DOF）, including vertical seat motion, vehicle suspension, pitching and rolling motions, and vertical wheel motions using the evolutionary game theory. A new design of the passive suspension is aided by game theory to attain the best compromise between ride quality and suspension deflections. Extensive simulations are performed on three type road surface models A, B, C pavement grades based on the guidelines provided by ISO-2631 with the Matlab/Simulink environment. The preliminary results show that, when the passive suspension is optimized via the proposed approach, a substantial improvement in the vertical ride quality is obtained while keeping the suspension deflections within their allowable clearance when the vehicle moves at a constant velocity v=20 m/s, and the comfort performance of a suspension seat can be enhanced by 20%-30%.
基金The National Natural Science Foundation of China(No.51306082,51476027)
文摘In order to incorporate the decision maker＇s preference into multiobjective optimization a preference-based multiobjective artificial bee colony algorithm PMABCA is proposed.In the proposed algorithm a novel reference point based preference expression method is addressed.The fitness assignment function is defined based on the nondominated rank and the newly defined preference distance.An archive set is introduced for saving the nondominated solutions and an improved crowding-distance operator is addressed to remove the extra solutions in the archive.The experimental results of two benchmark test functions show that a preferred set of solutions and some other non-preference solutions are achieved simultaneously.The simulation results of the proportional-integral-derivative PID parameter optimization for superheated steam temperature verify that the PMABCA is efficient in aiding to making a reasonable decision.
基金National Natural Science Foundation of China （50675009） International Science ＆ Technology Cooperation Program of China （2010DFA72540）
文摘The optional types of power source and actuator in the aircraft are more and more diverse due to fast development in more electric technology, which makes the combinations of different power sources and actuators become extremely complex in the architecture optimization process of airborne actuation system. The traditional ＂trial and error＂ method cannot satisfy the design demands. In this paper, firstly, the composition of more electric aircraft （MEA） flight control actuation system （FCAS） is introduced, and the possible architecture quantity is calculated. Secondly, the evaluation criteria of FCAS architecture with respect to safe reliability, weight and efficiency are proposed, and the evaluation criteria values are calculated in the case that each control surface adopts the same actuator configuration. Finally, the optimization results of MEA FCAS architecture are obtained by applying genetic algorithm （GA）. Compared to the traditional actuation system architecture, which only adopts servo valve controlled hydraulic actuators, the weight of the optimized more electric actuation system architecture can be reduced by 6%, and the efficiency can be improved by 30% based on the safe reliability requirements.
基金supported by the National Natural Science Foundation of China(6167321461673217+2 种基金61673219)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(18KJB120011)the Postgraduate Research and Practice Innovation Program of Jiangsu Province(KYCX19_0299)
文摘This paper presents a path planning approach for rotary unmanned aerial vehicles(R-UAVs)in a known static rough terrain environment.This approach aims to find collision-free and feasible paths with minimum altitude,length and angle variable rate.First,a three-dimensional(3D)modeling method is proposed to reduce the computation burden of the dynamic models of R-UAVs.Considering the length,height and tuning angle of a path,the path planning of R-UAVs is described as a tri-objective optimization problem.Then,an improved multi-objective particle swarm optimization algorithm is developed.To render the algorithm more effective in dealing with this problem,a vibration function is introduced into the collided solutions to improve the algorithm efficiency.Meanwhile,the selection of the global best position is taken into account by the reference point method.Finally,the experimental environment is built with the help of the Google map and the 3D terrain generator World Machine.Experimental results under two different rough terrains from Guilin and Lanzhou of China demonstrate the capabilities of the proposed algorithm in finding Pareto optimal paths.
基金provided by the National Natural Science Foundation of China(No.51404272)the Hunan Province Key Laboratory of Coal Resources Clean-Utilization and Mine Environment Protection(No.E21224)
文摘In order to obtain accurate probability integration method(PIM) parameters for surface movement of multi-panel mining, a genetic algorithm(GA) was used to optimize the parameters. As the measured surface movement is affected by more than one mining panel, traditional PIM parameter inversion model is difficult to ensure the reliability of the results due to the complexity of rock movement. With crossover,mutation and selection operators, GA can perform a global optimization search and has high computation efficiency. Compared with the pattern search algorithm, the fitness function can avoid falling into local minima traps. GA reduces the risk of local minima traps which improves the accuracy and reliability with the mutation mechanism. Application at Xuehu colliery shows that GA can be used to inverse the PIM parameters for multi-panel surface movement observation, and reliable results can be obtained. The research provides a new way for back-analysis of PIM parameters for mining subsidence under complex conditions.
基金supported by the National Natural Sciences Foundation of China(61603069,61533005,61522304,U1560102)the National Key Research and Development Program of China(2017YFA0700300)
文摘For multi-objective optimization problems,particle swarm optimization(PSO)algorithm generally needs a large number of fitness evaluations to obtain the Pareto optimal solutions.However,it will become substantially time-consuming when handling computationally expensive fitness functions.In order to save the computational cost,a surrogate-assisted PSO with Pareto active learning is proposed.In real physical space(the objective functions are computationally expensive),PSO is used as an optimizer,and its optimization results are used to construct the surrogate models.In virtual space,objective functions are replaced by the cheaper surrogate models,PSO is viewed as a sampler to produce the candidate solutions.To enhance the quality of candidate solutions,a hybrid mutation sampling method based on the simulated evolution is proposed,which combines the advantage of fast convergence of PSO and implements mutation to increase diversity.Furthermore,ε-Pareto active learning(ε-PAL)method is employed to pre-select candidate solutions to guide PSO in the real physical space.However,little work has considered the method of determining parameterε.Therefore,a greedy search method is presented to determine the value ofεwhere the number of active sampling is employed as the evaluation criteria of classification cost.Experimental studies involving application on a number of benchmark test problems and parameter determination for multi-input multi-output least squares support vector machines(MLSSVM)are given,in which the results demonstrate promising performance of the proposed algorithm compared with other representative multi-objective particle swarm optimization(MOPSO)algorithms.
