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MULTIOBJECT OPTIMIZATION OF A CENTRIFUGAL IMPELLER USING EVOLUTIONARY ALGORITHMS 认领 引用 被引量：3
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作者 LiJun LiuLijun FengZhenping 《Chinese Journal of Mechanical Engineering》 EI CAS 2004年第3期389-393,共5页
Application of the multiobjective evolutionary algorithms to the aerodynamicoptimization design of a centrifugal impeller is presented. The aerodynamic performance of acentrifugal impeller is evaluated by using the th... Application of the multiobjective evolutionary algorithms to the aerodynamicoptimization design of a centrifugal impeller is presented. The aerodynamic performance of acentrifugal impeller is evaluated by using the three-dimensional Navier-Stokes solutions. Thetypical centrifugal impeller is redesigned for maximization of the pressure rise and blade load andminimization of the rotational total pressure loss at the given flow conditions. The Bezier curvesare used to parameterize the three-dimensional impeller blade shape. The present method obtains manyreasonable Pareto optimal designs that outperform the original centrifugal impeller. Detailedobservation of the certain Pareto optimal design demonstrates the feasibility of the presentmultiobjective optimization method tool for turbomachinery design. 展开更多
关键词 Centrifugal impeller Navier-Stokes solver Evolutionary algorithms Multiobjective optimization Design
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Automated inverse design of asymmetric excavation retaining structures using multiobjective optimization 认领 引用 被引量：1
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作者 Qiwei Wan Changjie Xu +2 位作者 Xiangyu Wang Haibin Ding Xiaozhen Fan 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第11期7351-7366,共16页
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
关键词 Multiobjective optimization Enhanced genetic algorithm(EGA) Inverse design Deformation control Economic optimization
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Knowledge Classification-Assisted Evolutionary Multitasking for Two-Task Multiobjective Optimization Problems 认领 引用
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作者 Xiaoling Wang Qi Kang +3 位作者 MengChu Zhou Qi Deng Zheng Fan Haoyue Liu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第6期1176-1193,共18页
To realize Industry 5.0,manufacturers face various optimization problems that seldom appear in isolation.Evolutionary MultiTasking(EMT)is an effective method to solve multiple related problems by extracting and utiliz... To realize Industry 5.0,manufacturers face various optimization problems that seldom appear in isolation.Evolutionary MultiTasking(EMT)is an effective method to solve multiple related problems by extracting and utilizing common knowledge.Knowledge transfer is the key to the effectiveness of EMT.Existing EMT methods mainly focus on designing effective intertask learning methods and ignore the fact that provided knowledge's appropriateness also has a significant effect on EMT's performance.There is plentiful knowledge in assistant tasks,and knowledge transfer may not work well and even lead to a negative effect if useless knowledge is selected to guide target tasks.EMT is thus confronted with a challenge to find appropriate knowledge.This work proposes an efficient knowledge classification-assisted EMT framework to identify and select valuable knowledge from assistant tasks.During the evolution process,better-performing candidates are supposed to have advantages in exploitation.Therefore,assistant individuals that are similar to better-performing target individuals are used to provide positive knowledge.Specifically,the target sub-population is divided into different levels and then a classifier is trained to divide assistant sub-population.Considering that target and assistant sub-populations have different characteristics,we use domain adaptation to reduce their distribution discrepancies.In this way,the trained classifier can classify assistant individuals more accurately,and truly useful knowledge can be selected for target tasks.The superior performance of our proposed framework over state-of-the-art algorithms is verified via a series of benchmark problems. 展开更多
关键词 Artificial intelligence evolutionary multitasking intelligent optimization inter-task learning knowledge classification knowledge transfer machine learning multiobjective optimization
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Fuzzy Constraint Dominance Strategy for Constrainted Multiobjective Optimization Problems With Multiple Constraints 认领 引用
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作者 Weixiong Huang Rui Wang +2 位作者 Tao Zhang Sheng Qi Ling Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第12期2455-2472,共18页
Solving constrained multiobjective optimization problems(CMOPs) is a highly challenging work. Numerous complex nonlinear constraints significantly add to the complexity of CMOPs, resulting in an exceptionally intricat... Solving constrained multiobjective optimization problems(CMOPs) is a highly challenging work. Numerous complex nonlinear constraints significantly add to the complexity of CMOPs, resulting in an exceptionally intricate feasible region.Makes it difficult for the algorithm to search for the complete constraint PF. In addition, under the influence of multiple complex nonlinear constraints, the conventional calculation method of overall constraint violation is inefficient for assessing the quality of infeasible solutions, potentially misguiding the evolutionary direction of the population. In response to these challenges, this paper proposes the fuzzy constraint dominance strategy(FCDS).This novel approach facilitates nuanced comparisons of solutions to strike a better balance between objectives and constraints. The fuzzy constraint violation introduced in FCDS mitigates the misleading impact of complex nonlinear constraints. Moreover,FCDS divides the solution process of complex CMOP into multiple stages from easy to difficult, and uses adaptive methods to increase the difficulty level of the problem. Systematic experiments on four test suites and three real-world applications have conclusively demonstrated the superior competitiveness of FCDS against leading algorithms. 展开更多
关键词 Constrained multiobjective optimization constrainthandling technologies evolutionary algorithm(EA) fuzzy constraint dominance
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Novel Asymmetric IPM Rotor Design Using Cascaded Multiphysics Topology Optimization 认领 引用
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作者 Mohamed Reda Mahmoud Mohamed N.Ibrahim Peter Sergeant 《CES Transactions on Electrical Machines and Systems》 EI CSCD 2026年第2期153-165,共13页
Sustainable motor design is crucial for improving energy efficiency and reducing material consumption.This paper introduces a novel asymmetric interior permanent magnet(IPM)rotor design using topology optimization(TO)... Sustainable motor design is crucial for improving energy efficiency and reducing material consumption.This paper introduces a novel asymmetric interior permanent magnet(IPM)rotor design using topology optimization(TO).The study employs a two-stage TO approach to design IPM rotor.In the first stage,a multi-objective electromagnetic TO is conducted to enhance the average torque and reduce the mass of the magnetically active rotor zone.In the second stage,structural TO is performed to minimize the mass of the magnetically inactive rotor zone while maintaining mechanical integrity,ensuring the rotor withstands operational mechanical stresses.The asymmetric topology-optimized IPM(ATO-IPM)machine is analyzed and benchmarked against the conventional IPM design and the symmetric topology-optimized IPM(STO-IPM)design.The results indicate that the asymmetric flux barriers enhance the torque performance.The ATO-IPM design offers significantly more efficient utilization of permanent magnets(PMs)and improved torque density by approximately 13.3%compared to the conventional IPM motor.Moreover,ATO-IPM design supports the advancement of sustainability by improving efficiency by 2.3%compared to conventional design.Furthermore,ATO-IPM designs save about 46.7%of the amount of silicon steel of the conventional topology. 展开更多
关键词 Asymmetric design Finite element analysis(FEA) Interior permanent magnet(IPM)machine design Multiobjective optimization Solid isotropic material with penalization(SIMP) Topology optimization
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Higher-order optimality conditions for multiobjective optimization through a new type of directional derivatives 认领 引用
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作者 HUANG Zheng-gang 《Applied Mathematics(A Journal of Chinese Universities)》 SCIE CSCD 2025年第3期543-557,共15页
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
关键词 strict local minima of order k multiobjective optimization higher-order optimality conditions higher-order directional derivatives
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Multiobjective Optimization of Simulated Moving Bed by Tissue P System 认领 引用 被引量：8
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作者 黄亮 孙磊 +1 位作者 王宁 金晓明 《Chinese Journal of Chemical Engineering》 SCIE EI CAS 2007年第5期683-690,共8页
The binaphthol enantiomers separation process using simulation moving bed technology is simulated with the true moving bed approach （TMB）. In order to systematically optimize the process with multiple productive obj... The binaphthol enantiomers separation process using simulation moving bed technology is simulated with the true moving bed approach （TMB）. In order to systematically optimize the process with multiple productive objectives, this article develops a variant of tissue P system （TPS）. Inspired by general tissue P systems, the special TPS has a tissue-like structure with several membranes. The key rules of each membrane are the communication rule and mutation rule. These characteristics contribute to the diversity of the population, the conquest of the multimodal of objective function, and the convergence of algorithm. The results of comparison with a popular algorithm——the non-dominated sorting genetic algorithm 2（NSGA-2） illustrate that the new algorithm has satisfactory performance. Using the algorithm, this study maximizes synchronously several conflicting objectives, purities of different products, and productivity. 展开更多
关键词 simulated moving bed tissue P systems multiobjective optimization Pareto optimality evolutionary algorithm binaphthol enantiomers separation process
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Multiobjective Optimization of the Industrial Naphtha Catalytic Re-forming Process 认领 引用 被引量：9
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作者 侯卫锋 苏宏业 +1 位作者 牟盛静 褚健 《Chinese Journal of Chemical Engineering》 SCIE EI CAS 2007年第1期75-80,共6页
In this article, a multiobjective optimization strategy for an industrial naphtha continuous catalytic reform-ing process that aims to obtain aromatic products is proposed. The process model is based on a 20-lumped ki... In this article, a multiobjective optimization strategy for an industrial naphtha continuous catalytic reform-ing process that aims to obtain aromatic products is proposed. The process model is based on a 20-lumped kinetics re-action network and has been proved to be quite effective in terms of industrial application. The primary objectives in-clude maximization of yield of the aromatics and minimization of the yield of heavy aromatics. Four reactor inlet tem-peratures, reaction pressure, and hydrogen-to-oil molar ratio are selected as the decision variables. A genetic algorithm, which is proposed by the authors and named as the neighborhood and archived genetic algorithm (NAGA), is applied to solve this multiobjective optimization problem. The relations between each decision variable and the two objectives are also proposed and used for choosing a suitable solution from the obtained Pareto set. 展开更多
关键词 multiobjective optimization catalytic reforming lumped kinetics model neighborhood and archived genetic algorithm （NAGA）
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A multiobjective evolutionary optimization method based critical rainfall thresholds for debris flows initiation 认领 引用 被引量：4
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作者 YAN Yan ZHANG Yu +4 位作者 HU Wang GUO Xiao-jun MA Chao WANG Zi-ang ZHANG Qun 《Journal of Mountain Science》 SCIE CSCD 2020年第8期1860-1873,共14页
At present,most researches on the critical rainfall threshold of debris flow initiation use a linear model obtained through regression.With relatively weak fault tolerance,this method not only ignores nonlinear effect... At present,most researches on the critical rainfall threshold of debris flow initiation use a linear model obtained through regression.With relatively weak fault tolerance,this method not only ignores nonlinear effects but also is susceptible to singular noise samples,which makes it difficult to characterize the true quantization relationship of the rainfall threshold.Besides,the early warning threshold determined by statistical parameters is susceptible to negative samples(samples where no debris flow has occurred),which leads to uncertainty in the reliability of the early warning results by the regression curve.To overcome the above limitations,this study develops a data-driven multiobjective evolutionary optimization method that combines an artificial neural network(ANN)and a multiobjective evolutionary optimization implemented by particle swarm optimization(PSO).Firstly,the Pareto optimality method is used to represent the nonlinear and conflicting critical thresholds for the rainfall intensity I and the rainfall duration D.An ANN is used to construct a dual-target(dual-task)predictive surrogate model,and then a PSO-based multiobjective evolutionary optimization algorithm is applied to train the ANN and stochastically search the trained ANN for obtaining the Pareto front of the I-D surrogate prediction model,which is intended to overcome the limitations of the existing linear regression-based threshold methods.Finally,a double early warning curve model that can effectively control the false alarm rate and negative alarm rate of hazard warnings are proposed based on the decision space and target space maps.This study provides theoretical guidance for the early warning and forecasting of debris flows and has strong applicability. 展开更多
关键词 Debris flow Critical rainfall thresholds Multiobjective evolutionary optimization Artificial neural network Pareto optimality
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Multiobjective extremal optimization with applications to engineering design 认领 引用 被引量：3
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作者 CHEN Min-rong LU Yong-zai YANG Gen-ke 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS 2007年第12期1905-1911,共7页
In this paper, we extend a novel unconstrained multiobjective optimization algorithm, so-called multiobjective extremal optimization （MOEO）, to solve the constrained multiobjective optimization problems （MOPs）. Th... In this paper, we extend a novel unconstrained multiobjective optimization algorithm, so-called multiobjective extremal optimization （MOEO）, to solve the constrained multiobjective optimization problems （MOPs）. The proposed approach is validated by three constrained benchmark problems and successfully applied to handling three multiobjective engineering design problems reported in literature. Simulation results indicate that the proposed approach is highly competitive with three state-of-the-art multiobjective evolutionary algorithms, i.e., NSGA-11, SPEA2 and PAES. Thus MOEO can be considered a good alternative to solve constrained multiobjective optimization problems. 展开更多
关键词 Multiobjective optimization Extremal optimization （EO） Engineering design
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Hybrid particle swarm optimization for multiobjective resource allocation 认领 引用 被引量：4
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作者 Yi Yang Li Xiaoxing Gu Chunqin 《Journal of Systems Engineering and Electronics》 SCIE EI 2008年第5期959-964,共6页
Resource allocation(RA)is the problem of allocating resources among various artifacts or business units to meet one or more expected goals,such a.s maximizing the profits,minimizing the costs,or achieving the best qua... Resource allocation(RA)is the problem of allocating resources among various artifacts or business units to meet one or more expected goals,such a.s maximizing the profits,minimizing the costs,or achieving the best qualities.A complex multiobjective RA is addressed,and a multiobjective mathematical model is used to find solutions efficiently.Then,all improved particie swarm algorithm(mO_PSO)is proposed combined with a new particle diversity controller policies and dissipation operation.Meanwhile,a modified Pareto methods used in PSO to deal with multiobjectives optimization is presented.The effectiveness of the provided algorithm is validated by its application to some illustrative example dealing with multiobjective RA problems and with the comparative experiment with other algorithm. 展开更多
关键词 resource allocation multiobjective optimization improved particle swarm optimization.
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Robust Multiobjective and Multidisciplinary Design Optimization of Electrical Drive Systems 认领 引用 被引量：4
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作者 Gang Lei Tianshi Wang +1 位作者 Jianguo Zhu Youguang Guo 《CES Transactions on Electrical Machines and Systems》 CSCD 2018年第4期409-416,共8页
Design and optimization of electrical drive systems often involve simultaneous consideration of multiple objectives that usually contradict to each other and multiple disciplines that normally coupled to each other.Th... Design and optimization of electrical drive systems often involve simultaneous consideration of multiple objectives that usually contradict to each other and multiple disciplines that normally coupled to each other.This paper aims to present efficient system-level multiobjective optimization methods for the multidisciplinary design optimization of electrical drive systems.From the perspective of quality control,deterministic and robust approaches will be investigated for the development of the optimization models for the proposed methods.Meanwhile,two approximation methods,Kriging model and Taylor expansion are employed to decrease the computation/simulation cost.To illustrate the advantages of the proposed methods,a drive system with a permanent magnet synchronous motor driven by a field oriented control system is investigated.Deterministic and robust Pareto optimal solutions are presented and compared in terms of several steady-state and dynamic performances(like average torque and speed overshoot)of the drive system.The robust multiobjective optimization method can produce optimal Pareto solutions with high manufacturing quality for the drive system. 展开更多
关键词 Electrical drive systems electrical machines multidisciplinary design optimization multiobjective optimization robust design optimization
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MULTIOBJECTIVE OPTIMIZATION OF EIGHT-DOF VEHICLE SUSPENSION BASED ON GAME THEORY 认领 引用 被引量：2
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作者 宋崇智 赵又群 +1 位作者 谢能刚 王璐 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2010年第2期138-147,共10页
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
关键词 vehicle suspensions multiobjective optimization game theory riding comfort
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Multiobjective Differential Evolution for Higher-Dimensional Multimodal Multiobjective Optimization 认领 引用 被引量：1
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作者 Jing Liang Hongyu Lin +2 位作者 Caitong Yue Ponnuthurai Nagaratnam Suganthan Yaonan Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第6期1458-1475,共18页
In multimodal multiobjective optimization problems(MMOPs),there are several Pareto optimal solutions corre-sponding to the identical objective vector.This paper proposes a new differential evolution algorithm to solve... In multimodal multiobjective optimization problems(MMOPs),there are several Pareto optimal solutions corre-sponding to the identical objective vector.This paper proposes a new differential evolution algorithm to solve MMOPs with higher-dimensional decision variables.Due to the increase in the dimensions of decision variables in real-world MMOPs,it is diffi-cult for current multimodal multiobjective optimization evolu-tionary algorithms(MMOEAs)to find multiple Pareto optimal solutions.The proposed algorithm adopts a dual-population framework and an improved environmental selection method.It utilizes a convergence archive to help the first population improve the quality of solutions.The improved environmental selection method enables the other population to search the remaining decision space and reserve more Pareto optimal solutions through the information of the first population.The combination of these two strategies helps to effectively balance and enhance conver-gence and diversity performance.In addition,to study the per-formance of the proposed algorithm,a novel set of multimodal multiobjective optimization test functions with extensible decision variables is designed.The proposed MMOEA is certified to be effective through comparison with six state-of-the-art MMOEAs on the test functions. 展开更多
关键词 Benchmark functions diversity measure evolution-ary algorithms multimodal multiobjective optimization.
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Analysis and Research on Mechanical Stress and Multiobjective Optimization of Synchronous Reluctance Motor 认领 引用 被引量：1
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作者 Han Zhou Xiuhe Wang +1 位作者 Lixin Xiong Xin Zhang 《CES Transactions on Electrical Machines and Systems》 EI CSCD 2024年第3期274-283,共10页
The mechanical strength of the synchronous reluctance motor(SynRM)has always been a great challenge.This paper presents an analysis method for assessing stress equivalence and magnetic bridge stress interaction,along ... The mechanical strength of the synchronous reluctance motor(SynRM)has always been a great challenge.This paper presents an analysis method for assessing stress equivalence and magnetic bridge stress interaction,along with a multiobjective optimization approach.Considering the complex flux barrier structure and inevitable stress concentration at the bridge,the finite element model suitable for SynRM is established.Initially,a neural network structure with two inputs,one output,and three layers is established.Continuous functions are constructed to enhance accuracy.Additionally,the equivalent stress can be converted into a contour distribution of a three-dimensional stress graph.The contour line distribution illustrates the matching scheme for magnetic bridge lengths under equivalent stress.Moreover,the paper explores the analysis of magnetic bridge interaction stress.The optimization levels corresponding to the length of each magnetic bridge are defined,and each level is analyzed by the finite element method.The Taguchi method is used to determine the specific gravity of the stress source on each magnetic bridge.Based on this,a multiobjective optimization employing the Multiobjective Particle Swarm Optimization(MOPSO)technique is introduced.By taking the rotor magnetic bridge as the design parameter,ten optimization objectives including air-gap flux density,sinusoidal property,average torque,torque ripple,and mechanical stress are optimized.The relationship between the optimization objectives and the design parameters can be obtained based on the response surface method(RSM)to avoid too many experimental samples.The optimized model is compared with the initial model,and the optimized effect is verified.Finally,the temperature distribution of under rated working conditions is analyzed,providing support for addressing thermal stress as mentioned earlier. 展开更多
关键词 Multiobjective optimization Neural network Stress equivalence Synchronous reluctance motor Taguchi method
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Multiobjective Optimization of Hull Form Based on Global Optimization Algorithm 认领 引用 被引量：1
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作者 LIU Jie ZHANG Baoji 《Journal of Shanghai Jiaotong university(Science)》 EI 2022年第3期346-355,共10页
Rankine source method,optimization technology,parametric modeling technology,and improved multiobjective optimization algorithm were combined to investigate the multiobjective optimization design of hull form.A multio... Rankine source method,optimization technology,parametric modeling technology,and improved multiobjective optimization algorithm were combined to investigate the multiobjective optimization design of hull form.A multiobjective and multilevel optimization design framework was constructed for the comprehensive navigation performance of ships.CAESES software was utilized as the optimization platform,and nondominated sorting genetic algorithm II(NSGA-II)was used to conduct multiobjective optimization research on the resistance and sea-keeping performance of the ITTC Ship A-2 fishing vessel.Optimization objectives of this study are heave/pitch response amplitude and wave-making resistance.Taking the displacement and the length between perpendiculars as constraints,we optimized the profile of the hull.Analytic hierarchy process(AHP)and technique for order preference by similarity to ideal solution(TOPSIS)were used to sort and select Pareto solutions and determine weight coefficient of each navigation performance objective in the general objective.Finally,the hydrodynamic performance before and after the parametric deformation of the hull was compared.The results show that both the wave-making resistance and heave/pitch amplitude of the optimized hull form are reduced,and the satisfactory optimal hull form is obtained.The results of this study have a certain reference value for the initial stage of multiobjective optimization design of hull form. 展开更多
关键词 multiobjective optimization Rankine source method global optimization algorithm nondominated sorting genetic algorithm II(NSGA-II)
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UAV Task Allocation for Hierarchical Multiobjective Optimization in Complex Conditions Using Modified NSGA-III with Segmented Encoding 认领 引用 被引量：1
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作者 JIN Yudong FENG Jiabo ZHANG Weijun 《Journal of Shanghai Jiaotong university(Science)》 EI 2021年第4期431-445,共15页
With the recent boom in unmanned aerial vehicle (UAV) technology, many UAV applications involving complex and risky tasks in military and civilian fields have emerged, such as military strikes and disaster monitoring.... With the recent boom in unmanned aerial vehicle (UAV) technology, many UAV applications involving complex and risky tasks in military and civilian fields have emerged, such as military strikes and disaster monitoring. Task allocation for UAVs is the process of planning the division of work among UAVs, controlled from ground stations by human operators. This study formulates the UAV task-allocation problem as an extended traveling salesman problem and presents a novel UAV task-allocation model for complex air concentration monitoring tasks. Then, an optimized non-dominated sorting genetic algorithm III (NSGA-III) based on a twin-exclusion mechanism, hierarchical objective-domination operator, and segmented gene encoding (i.e., NSGA-III-TEHOD) is developed to solve complex task-allocation problems involving multiple UAVs, hierarchical objectives, obstacles, and ambient wind. The algorithm is tested in several simulations, and the results demonstrate that the new algorithm outperforms NSGA-III, non-dominated sorting genetic algorithm II (NSGA-II), and genetic algorithm (GA) in terms of efficiency of global convergence and early maturation prevention and is available for the hierarchical objective-optimization problems. 展开更多
关键词 unmanned aerial vehicle(UAV) task allocation non-dominated sorting genetic algorithm(NSGA) multiobjective optimization
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Reliability based multiobjective optimization for design of structures subject to random vibrations 认领 引用 被引量：1
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作者 Giuseppe Carlo MARANO 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS 2008年第1期15-25,共11页
Based on a multiobjective approach whose objective function (OF) vector collects stochastic reliability performance and structural cost indices, a structural optimization criterion for mechanical systems subject to ra... Based on a multiobjective approach whose objective function (OF) vector collects stochastic reliability performance and structural cost indices, a structural optimization criterion for mechanical systems subject to random vibrations is presented for supporting engineer’s design. This criterion differs from the most commonly used conventional optimum design criterion for random vibrating structure, which is based on minimizing displacement or acceleration variance of main structure responses, without considering explicitly required performances against failure. The proposed criterion can properly take into account the design-reliability required performances, and it becomes a more efficient support for structural engineering decision making. The multiobjective optimum (MOO) design of a tuned mass damper (TMD) has been developed in a typical seismic design problem, to control structural vibration induced on a multi-storey building structure excited by nonstationary base acceleration random process. A numerical example for a three-storey building is developed and a sensitivity analysis is carried out. The results are shown in a useful manner for TMD design decision support. 展开更多
关键词 Structural optimization Multiobjective optimization （MOO） Random vibration Tuned mass damper （TMD）
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Design-space adaptation method for multiobjective and multidisciplinary optimization 认领 引用
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作者 Jongho JUNG Kwanjung YEE Shinkyu JEONG 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2024年第8期166-189,共24页
This paper developed a new method that adaptively adjusts a design space by considering the actual solution distribution of a problem to overcome the conventional design-space adaptation method that assumes the soluti... This paper developed a new method that adaptively adjusts a design space by considering the actual solution distribution of a problem to overcome the conventional design-space adaptation method that assumes the solutions distribution to be a normal distribution because the distributions of solutions are rarely normal distributions for real-world problems.The developed method was applied to nineteen multiobjective test functions that are widely used to evaluate the characteristics and performance of optimization approaches.The results showed that this method adapted the design space to an appropriate design space where the solution existence probability was high.The optimization performance achieved using the developed method was higher than that of the conventional methods.Furthermore,the developed method was applied to the conceptual design of an unmanned spacecraft to confirm its validity in real-world design and multidisciplinaryoptimization problems.The results showed that the Pareto solutions of the developed method were superior to those of conventional methods.Additionally,the optimization efficiency with the developed method was improved by more than 1.4 times over that of the conventional methods.In this regard,the developed method has the potential to be applied to complicated real-world optimization problems to achieve better performance and efficiency. 展开更多
关键词 Multiobjective optimization Multiobjective genetic algorithm Design-space adaptation Multidisciplinary optimization Hypersonic vehicle
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A Decision Variables Classification-Based Evolutionary Algorithm for Constrained Multi-Objective Optimization Problems 认领 引用
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作者 Xuanxuan Ban Jing Liang +4 位作者 Kangjia Qiao Kunjie Yu Yaonan Wang Jinzhu Peng Boyang Qu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第9期1830-1849,共20页
Solving constrained multi-objective optimization problems(CMOPs)is a challenging task due to the presence of multiple conflicting objectives and intricate constraints.In order to better address CMOPs and achieve a bal... Solving constrained multi-objective optimization problems(CMOPs)is a challenging task due to the presence of multiple conflicting objectives and intricate constraints.In order to better address CMOPs and achieve a balance between objectives and constraints,existing constrained multi-objective evolutionary algorithms(CMOEAs)predominantly focus on devising various strategies by leveraging the relationships between objectives and constraints,and the designed strategies usually are effective for the problems with simple constraints.However,these methods most ignore the relationship between decision variables and constraints.In fact,the essence of optimization is to find appropriate decision variables to meet various complex constraints.Therefore,it is hoped that the problem can be analyzed from the perspective of decision variables,so as to obtain more excellent results.Based on the above motivation,this paper proposes a decision variables classification approach,according to the relationship between decision variables and constraints,variables are divided into constraint-related(CR)variables and constraintindependent(CI)variables.Consequently,by optimizing these two types of variables independently,the population can sustain a favorable balance between feasibility and diversity.Furthermore,specific offspring generation strategies are proposed for the two categories of decision variables in order to achieve rapid convergence while maintaining population diversity.Experimental results on 31 test problems as well as 20 real-world problems demonstrate that the proposed algorithm is competitive compared to some state-of-the-art constrained multi-objective optimization algorithms. 展开更多
关键词 Constraint-independent(CI) constrained multiobjective optimization constraint-related(CR) decision variables
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