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An Approximation Theorem and Generic Uniqueness of Weakly Pareto-Nash Equilibrium for Multiobjective Population Games 认领 引用
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作者 Hua-Xin Chen Wen-Sheng Jia 《Journal of the Operations Research Society of China》 EI CSCD 2026年第2期591-602,共12页
We mainly study an approximation theorem and generic uniqueness of weakly Pareto-Nash equilibrium(PNE)for multiobjective population games.First,we define the concept of approximate weakly PNE of multiobjective populat... We mainly study an approximation theorem and generic uniqueness of weakly Pareto-Nash equilibrium(PNE)for multiobjective population games.First,we define the concept of approximate weakly PNE of multiobjective population games.Then,an approximation theorem is proved under very mild conditions.Furthermore,we prove the generic uniqueness of weakly PNE for multiobjective population games by establishing a function space.Finally,we obtain the generic uniqueness of weakly PNE for multiobjective population games in the case of function perturbation.The above results are new and improve the related results of the recent literature. 展开更多
关键词 Multiobjective population game Approximation theorem Weakly Pareto-Nash equilibrium
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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Variable Reconstruction for Evolutionary Expensive Large-Scale Multiobjective Optimization and Its Application on Aerodynamic Design 认领 引用
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作者 Jianqing Lin Cheng He +1 位作者 Ye Tian Linqiang Pan 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第4期719-733,共15页
Expensive multiobjective optimization problems(EMOPs)are complex optimization problems exacted from realworld applications,where each objective function evaluation(FE)involves expensive computations or physical experi... Expensive multiobjective optimization problems(EMOPs)are complex optimization problems exacted from realworld applications,where each objective function evaluation(FE)involves expensive computations or physical experiments.Many surrogate-assisted evolutionary algorithms(SAEAs)have been designed to solve EMOPs.Nevertheless,EMOPs with large-scale decision variables remain challenging for existing SAEAs,leading to difficulties in maintaining convergence and diversity.To address this deficiency,we proposed a variable reconstructionbased SAEA(VREA)to balance convergence enhancement and diversity maintenance.Generally,a cluster-based variable reconstruction strategy reconstructs the original large-scale decision variables into low-dimensional weight variables.Thus,the population can be rapidly pushed towards the Pareto set(PS)by optimizing low-dimensional weight variables with the assistance of surrogate models.Population diversity is improved due to the cluster-based variable reconstruction strategy.An adaptive search step size strategy is proposed to balance exploration and exploitation further.Experimental comparisons with four state-of-the-art SAEAs are conducted on benchmark EMOPs with up to 1000 decision variables and an aerodynamic design task.Experimental results demonstrate that VREA obtains well-converged and diverse solutions with limited real FEs. 展开更多
关键词 Aerodynamic design large-scale optimization multiobjective evolutionary algorithm surrogate model variable reconstruction
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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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A Two-Layer Multiobjective Optimal Energy Management Strategy Considering Fuel Cell/Battery Lifetime 认领 引用
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作者 Zhaoyang Shen Zhidong Qi +2 位作者 Jie Zhou Junsong Xu Liang Shan 《Carbon and Hydrogen》 SCIE CAS CSCD 2025年第1期80-96,共17页
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
关键词 dandelion optimizer multiobjective optimization multistack fuel cell hybrid system
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Real-Time Dynamic Multiobjective Path Planning:A Case Study 认领 引用
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作者 Hongle Li SeongKi Kim 《Computers, Materials & Continua》 SCIE EI 2025年第12期5571-5594,共24页
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
关键词 Multiobjective optimization path planning real-time system dynamic environments A*algorithm game artificial intelligence
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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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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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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：8
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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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A Novel Multiobjective Fireworks Algorithm and Its Applications to Imbalanced Distance Minimization Problems 认领 引用 被引量：5
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作者 Shoufei Han Kun Zhu +4 位作者 MengChu Zhou Xiaojing Liu Haoyue Liu Yusuf Al-Turki Abdullah Abusorrah 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2022年第8期1476-1489,共14页
Recently,multimodal multiobjective optimization problems(MMOPs)have received increasing attention.Their goal is to find a Pareto front and as many equivalent Pareto optimal solutions as possible.Although some evolutio... Recently,multimodal multiobjective optimization problems(MMOPs)have received increasing attention.Their goal is to find a Pareto front and as many equivalent Pareto optimal solutions as possible.Although some evolutionary algorithms for them have been proposed,they mainly focus on the convergence rate in the decision space while ignoring solutions diversity.In this paper,we propose a new multiobjective fireworks algorithm for them,which is able to balance exploitation and exploration in the decision space.We first extend a latest single-objective fireworks algorithm to handle MMOPs.Then we make improvements by incorporating an adaptive strategy and special archive guidance into it,where special archives are established for each firework,and two strategies(i.e.,explosion and random strategies)are adaptively selected to update the positions of sparks generated by fireworks with the guidance of special archives.Finally,we compare the proposed algorithm with eight state-of-the-art multimodal multiobjective algorithms on all 22 MMOPs from CEC2019 and several imbalanced distance minimization problems.Experimental results show that the proposed algorithm is superior to compared algorithms in solving them.Also,its runtime is less than its peers'. 展开更多
关键词 Adaptive strategy fireworks algorithm multimodal multiobjective optimization problems(MMOP)
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Multiobjective particle swarm inversion algorithm for two-dimensional magnetic data 认领 引用 被引量：12
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作者 熊杰 张涛 《Applied Geophysics》 SCIE CSCD 2015年第2期127-136,273,共10页
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
关键词 multiobjective inversion particle swarm optimization regularization factor global search magnetic data
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New immune multiobjective optimization algorithm and its application in boiler combustion optimization 认领 引用 被引量：5
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作者 周霞 沈炯 +1 位作者 沈剑贤 李益国 《Journal of Southeast University(English Edition)》 EI CAS 2010年第4期563-568,共6页
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
关键词 combustion optimization multiobjective optimizat-ion immune cell subsets
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Generalized constrained multiobjective games in locally FC-uniform spaces 认领 引用 被引量：4
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作者 丁协平 黎进三 姚任之 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI 2008年第3期301-309,共9页
A new class of generalized constrained multiobjective games is introduced and studied in locally FC-uniform spaces without convexity structure where the number of players may be finite or infinite and all payoff funct... A new class of generalized constrained multiobjective games is introduced and studied in locally FC-uniform spaces without convexity structure where the number of players may be finite or infinite and all payoff functions get their values in an infinite-dimensional space. By using a Himmelberg type fixed point theorem in locally FC-uniform spaces due to author, some existence theorems of weak Paxeto equilibria for the generalized constrained multiobjective games are established in locally FC-uniform spaces. These theorems improve, unify and generalize the corresponding results in recent literatures. 展开更多
关键词 locally FC-uniform space fixed point generalized constrained multiobjective game weak Pareto equilibria
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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 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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