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Cooperative Metaheuristics with Dynamic Dimension Reduction for High-Dimensional Optimization Problems 认领 引用
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作者 Junxiang Li Zhipeng Dong +2 位作者 Ben Han Jianqiao Chen Xinxin Zhang 《Computers, Materials & Continua》 SCIE EI 2026年第1期1484-1502,共19页
Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when ta... Owing to their global search capabilities and gradient-free operation,metaheuristic algorithms are widely applied to a wide range of optimization problems.However,their computational demands become prohibitive when tackling high-dimensional optimization challenges.To effectively address these challenges,this study introduces cooperative metaheuristics integrating dynamic dimension reduction(DR).Building upon particle swarm optimization(PSO)and differential evolution(DE),the proposed cooperative methods C-PSO and C-DE are developed.In the proposed methods,the modified principal components analysis(PCA)is utilized to reduce the dimension of design variables,thereby decreasing computational costs.The dynamic DR strategy implements periodic execution of modified PCA after a fixed number of iterations,resulting in the important dimensions being dynamically identified.Compared with the static one,the dynamic DR strategy can achieve precise identification of important dimensions,thereby enabling accelerated convergence toward optimal solutions.Furthermore,the influence of cumulative contribution rate thresholds on optimization problems with different dimensions is investigated.Metaheuristic algorithms(PSO,DE)and cooperative metaheuristics(C-PSO,C-DE)are examined by 15 benchmark functions and two engineering design problems(speed reducer and composite pressure vessel).Comparative results demonstrate that the cooperative methods achieve significantly superior performance compared to standard methods in both solution accuracy and computational efficiency.Compared to standard metaheuristic algorithms,cooperative metaheuristics achieve a reduction in computational cost of at least 40%.The cooperative metaheuristics can be effectively used to tackle both high-dimensional unconstrained and constrained optimization problems. 展开更多
关键词 Dimension reduction modified principal components analysis high-dimensional optimization problems cooperative metaheuristics metaheuristic algorithms
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Solving high-dimensional global optimization problems via solution space restructuring with neural network 认领 引用
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作者 N.VO T.LE-DUC +3 位作者 H.TANG H.NGUYEN-XUAN S.H.LEE J.H.LEE 《Applied Mathematics and Mechanics(English Edition)》 SCIE EI CSCD 2026年第6期1383-1400,共18页
It is well-known that appropriately determining the solution space and initializations is crucial for obtaining high-quality optimal solutions for optimization problems,considering both gradient-based and gradient-fre... It is well-known that appropriately determining the solution space and initializations is crucial for obtaining high-quality optimal solutions for optimization problems,considering both gradient-based and gradient-free techniques.However,the general framework for adaptively dealing with a particular optimization problem is commonly overlooked and thus still hidden in the literature.To overcome this limitation,a new approach assisted by the neural network(NN)is proposed for solving high-dimensional optimization issues.By restructuring the search space to optimize the objective function via a nonlinear mapping constructed by an autoencoder(AE),the surrogate solution space is constructed by a network training process and dynamically oriented to the optimal solution of the optimization issue.To enhance the optimization efficiency and address non-smooth problems,the classical metaheuristic grey wolf optimizer(GWO)and the adaptive moment estimation(Adam)are sequentially employed to complement the disadvantages of the constituted models.The effectiveness of the proposed approach is validated by solving a set of mathematical functions with 1000-dimensional and three large-scale truss design optimization problems.Several numerical experiments show that the solution space is reduced in terms of both size and complexity based on the restructuring procedure,in which the global optimal solution is still conserved,leading to better optimization efficiency when solving optimization problems with complex search domains with large dimensions.In addition,the hybrid optimizer has also been proven to be more effective when combined with the restructuring technique owing to the use of the Adam algorithm in the second phase. 展开更多
关键词 high-dimensional optimization solution space restructuring adaptive moment estimation(Adam) grey wolf optimizer(GWO) neural network(NN)
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Adaptive Meta-Loss Networks:Learning Task-Agnostic Loss Functions via Evolutionary Optimization 认领 引用
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作者 Mirna Yunita Xiabi Liu +1 位作者 Zhaoyang Hai Rachmat Muwardi 《Computers, Materials & Continua》 SCIE EI 2026年第5期1931-1949,共19页
Designing appropriate loss functions is critical to the success of supervised learning models.However,most conventional losses are fixed and manually designed,making them suboptimal for diverse and dynamic learning sc... Designing appropriate loss functions is critical to the success of supervised learning models.However,most conventional losses are fixed and manually designed,making them suboptimal for diverse and dynamic learning scenarios.In this work,we propose an Adaptive Meta-Loss Network(Adaptive-MLN)that learns to generate taskagnostic loss functions tailored to evolving classification problems.Unlike traditional methods that rely on static objectives,Adaptive-MLN treats the loss function itself as a trainable component,parameterized by a shallow neural network.To enable flexible,gradient-free optimization,we introduce a hybrid evolutionary approach that combines GeneticAlgorithms(GA)for global exploration and Evolution Strategies(ES)for local refinement.This co-evolutionary process dynamically adjusts the loss landscape,improvingmodel generalization without relying on analytic gradients or handcrafted heuristics.Experimental evaluations on synthetic tasks and the CIFAR-10 andMNIST datasets demonstrate that our approach consistently outperforms standard losses such as Cross-Entropy and Mean Squared Error in terms of accuracy,convergence,and adaptability. 展开更多
关键词 Meta-learning adaptive loss function task-agnostic optimization evolutionary strategy genetic algorithm classification
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A Robust Damage Identification Method Based on Modified Holistic Swarm Optimization Algorithm and Hybrid Objective Function 认领 引用
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作者 Xiansong Xie Xiaoqian Qian 《Structural Durability & Health Monitoring》 EI 2026年第2期235-259,共25页
Correlation function of acceleration responses-based damage identificationmethods has been developed and employed,while they still face the difficulty in identifying local orminor structural damages.To deal with this ... Correlation function of acceleration responses-based damage identificationmethods has been developed and employed,while they still face the difficulty in identifying local orminor structural damages.To deal with this issue,a robust structural damage identification method is developed,integrating a modified holistic swarm optimization(MHSO)algorithm with a hybrid objective function.The MHSO is developed by combining Hammersley sequencebased population initialization,chaotic search around the worst solution,and Hooke-Jeeves pattern search around the best solution,thereby improving both global exploration and local exploitation capabilities.A hybrid objective function is constructed by merging acceleration correlation function-based and strain correlation function-based objective functions,effectively leveraging the complementary sensitivities of global and local responses.To further suppress spurious solutions and promote sparsity in parameter estimation,an additional L0.5 regularization term is introduced.The effectiveness of the proposed method is validated through numerical simulations on a simply supported beam and a steel girder benchmark structure.Comparative studies with sequential quadratic programming,genetic algorithm,andHSO demonstrate that theMHSOachieves superior accuracy and convergence efficiency,even with limited sensors and 20%noise-contaminated measurements.Results highlight that the hybrid objective function significantly enhances the detection of both major and minor damages,while the inclusion of sparse regularization improves robustness against noise and model uncertainties.The findings indicate that the proposed framework provides a reliable and computationally efficient solution for simultaneous localization and quantification of structural damages,offering promising applicability to real-world structural health monitoring scenarios. 展开更多
关键词 Damage identification holistic swarm optimization algorithm combined correlation function hybrid objective function sparse regularization grid structure
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On Smoothing l1Exact Penalty Function for Nonlinear Constrained Optimization Problems 认领 引用
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作者 Yu-Fei Ren You-Lin Shang 《Journal of the Operations Research Society of China》 EI CSCD 2026年第2期700-718,共19页
The penalty function method is a significant method for solving nonlinear constrained optimization problems(COP).In this paper,a new quadratic continuous differentiable smooth penalty function is proposed for the l_(1... The penalty function method is a significant method for solving nonlinear constrained optimization problems(COP).In this paper,a new quadratic continuous differentiable smooth penalty function is proposed for the l1exact penalty function.The error estimations between the objective function values of the smooth penalty problem,the penalty problem and the original problem are also studied.Furthermore,based on the smoothed penalty function,an algorithm for solving COP is proposed,and the convergence of the algorithm is proved.Finally,several numerical examples are given to illustrate the effectiveness of the proposed algorithm. 展开更多
关键词 l1exact penalty function Constrained optimization problem Smoothing method Approximate optimal solution
A Comprehensive Review of Parallel Optimization Algorithms for High-Dimensional and Incomplete Matrix Factorization 认领 引用
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作者 Qicong Hu Hao Wu Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第12期2399-2426,共28页
High-dimensional and incomplete(HDI) matrices are commonly encountered in various big data-related applications for illustrating the complex interactions among numerous entities, like the user-item interactions in a c... High-dimensional and incomplete(HDI) matrices are commonly encountered in various big data-related applications for illustrating the complex interactions among numerous entities, like the user-item interactions in a commercial recommender system or the user-user interactions in a social network services system. The factorization of such an HDI matrix can embed the involved entities into the low-dimensional feature space for acquiring their principal representation, which is a vital task in various application scenes and is often established through the Latent Factor Analysis(LFA). Nevertheless, an HDI matrix can be huge when the corresponding application explodes to involve millions of users, items, or other interactive nodes. In this case, a parallel optimization algorithm is desired for raising the scalability and time efficiency of an LFA model. This paper provides a comprehensive review of the existing parallel optimization algorithms for the LFA model. Specifically, it performs: 1) discussion and summary of these algorithms based on computing architecture and mode, 2) empirical studies of representative models, and3) summary of the current challenges and future directions in this domain. This survey aims to offer an exhaustive review of Parallel Optimization Algorithms for High-Dimensional and Incomplete Matrix Factorization, thereby fostering further research in this field. 展开更多
关键词 Data-related application high-dimensional and incomplete(HDI)data high-performance computing latent feature analysis(LFA) matrix factorization parallel optimization algorithm
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Disturbance observer-based constrained adaptive fuzzy control for manipulator:a cooperative optimization approach of gain 认领 引用
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作者 Qing Yang Haisheng Yu +2 位作者 Xiangxiang Meng Wenqian Yu Qingkun Guo 《Control Theory and Technology》 EI CSCD 2026年第3期416-432,共17页
This paper investigates input–output constraints adaptive fuzzy control strategy with cooperative optimization approach of the gain and time-varying nonlinear disturbance observer for manipulator systems.First,the st... This paper investigates input–output constraints adaptive fuzzy control strategy with cooperative optimization approach of the gain and time-varying nonlinear disturbance observer for manipulator systems.First,the static control gain strategies cannot simultaneously optimize system performances during both dynamic and steady-state stages.To address this problem,a novel cooperative optimization approach of the gain(COG)based on tracking error is proposed to replace the traditional static gain strategies.Second,a time-varying nonlinear disturbance observer(NDO)is proposed to accurately estimate variable disturbances and mitigate harmful observation peak at the initial stage of manipulator tracking.Furthermore,an auxiliary system and an asymmetric time-varying barrier Lyapunov function are used to ensure that the inputs and outputs of the system remain within predefined constraints.Notably,the traditional backstepping control relies on precise model information.To minimize the impact of model uncertainties on tracking performance,an adaptive fuzzy control is employed to design the controller,eliminating the need for precise model information.Finally,the effectiveness of the proposed input–output constraints adaptive fuzzy control strategy with COG and time-varying NDO is verified and analyzed through comparative experiments on a two-joint manipulator platform. 展开更多
关键词 Cooperative optimization Adaptive fuzzy control Barrier Lyapunov function Disturbance observer Constrained manipulator
Tuning nonlinear model predictive control via Bayesian optimization:a comparative performance analysis 认领 引用
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作者 Maria Alice de F.Marques Juarez dos Santos Azevedo +1 位作者 Julio Elias Normey-Rico Marcus V.Americano da Costa 《Control Theory and Technology》 EI CSCD 2026年第3期374-387,共14页
This paper presents a new approach to tuning the cost function weights of a nonlinear model predictive controller(NMPC)using offline Bayesian optimization(BO).We propose a recursive weight selection method that integr... This paper presents a new approach to tuning the cost function weights of a nonlinear model predictive controller(NMPC)using offline Bayesian optimization(BO).We propose a recursive weight selection method that integrates BO directly into the NMPC simulation loop and targets an economic cost function.This approach identifies weights that optimize an economic cost function,ensuring that controller performance is aligned with the operational economics of the process.A case study involving an interconnected tank system with nonlinear level and temperature dynamics illustrates the method.Two operating conditions are tested:an undisturbed scenario and a disturbed one,incorporating sensor noise and model–plant mismatch.In both scenarios,the BO-tuned NMPC outperforms Traditional and Satisficing-based weight strategies,yielding smoother control actions.In the disturbed case,cost reductions of up to 4.5%are achieved.The results confirm that offline BO-based tuning offers a viable and robust alternative to manual or online adaptive strategies,especially in systems where online retraining is impractical.Sensitivity tests further highlight the importance of informed search interval selection to ensure convergence and performance. 展开更多
关键词 Nonlinear predictive control Bayesian optimization Tuning parameters Cost function
Generalized Functional Linear Models:Efficient Modeling for High-dimensional Correlated Mixture Exposures 认领 引用
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作者 Bingsong Zhang Haibin Yu +11 位作者 Xin Peng Haiyi Yan Siran Li Shutong Luo Renhuizi Wei Zhujiang Zhou Yalin Kuang Yihuan Zheng Chulan Ou Linhua Liu Yuehua Hu Jindong Ni 《Biomedical and Environmental Sciences》 SCIE CAS CSCD 2025年第8期961-976,共16页
Objective Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health.Analysis of these mixture exposures presents several key challenges for environmental epidemio... Objective Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health.Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment,including high dimensionality,correlated exposure,and subtle individual effects.Methods We proposed a novel statistical approach,the generalized functional linear model(GFLM),to analyze the health effects of exposure mixtures.GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation.The robustness and efficiency was evaluated under various scenarios through extensive simulation studies.Results We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey(NHANES).In the first application,we examined the effects of 37 nutrients on BMI(2011–2016 cycles).The GFLM identified a significant mixture effect,with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI,respectively.For the second application,we investigated the association between four pre-and perfluoroalkyl substances(PFAS)and gout risk(2007–2018 cycles).Unlike traditional methods,the GFLM indicated no significant association,demonstrating its robustness to multicollinearity.Conclusion GFLM framework is a powerful tool for mixture exposure analysis,offering improved handling of correlated exposures and interpretable results.It demonstrates robust performance across various scenarios and real-world applications,advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology. 展开更多
关键词 Mixture exposure modeling Functional data analysis High-dimensional data Correlated exposures Environmental epidemiology
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Two-scale topology optimization of structural fundamental eigenfrequency using a data-driven microstructure model based on M-VCUT level set 认领 引用
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作者 Zibo Wang Minjie Shao Qi Xia 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第2期437-447,共11页
For the optimization of fundamental eigenfrequency in vibrating structures,it has been proven that multi-scale structures have advantages over single scale structures.This study introduces a two-scale topology optimiz... For the optimization of fundamental eigenfrequency in vibrating structures,it has been proven that multi-scale structures have advantages over single scale structures.This study introduces a two-scale topology optimization method using a data-driven microstructure model based on a multiple variable cutting(M-VCUT)level set approach.This method aims to maximize the fundamental eigenfrequency of two-scale structures.The method consists of two parts:offline database construction and online topology optimization.In the process of offline database construction,many microstructures are obtained by varying the value of geometric parameters according to the M-VCUT level set approach;then,a mapping relationship between the geometric parameters and the homogenized mechanical properties of microstructures is established by compactly supported radial basis function interpolation,which gives the data-driven microstructure model.In the process of online optimization,the homogenized mechanical properties corresponding to arbitrary design variables are obtained by using the data-driven microstructure model,whose computational costs are much less than those of the homogenization.Topology optimization is carried out with this data-driven model to enhance computational efficiency.In order to adapt the method of moving asymptotes(MMA),the eigenfrequency maximization problem is converted to its reciprocal minimization problem for sensitivity calculation.The method’s effectiveness is proved through several numerical examples. 展开更多
关键词 Two-scale structure Data-driven M-VCUT level set Eigenfrequency optimization Radial basis function MMA
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Optimization of integrated resource utilization in railway hubs with mixed high-speed and conventional operations at shared stations 认领 引用
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作者 Sitong Xiang Feng Lin +2 位作者 Zhigang Le Datong Song Ershuai Nie 《Railway Sciences》 2026年第4期489-504,共16页
Purpose-With the continuous expansion of railway hubs,increasing functional complexity and growing capacity constraints,the coordinated and efficient utilization of transportation resources-such as stations,lines and ... Purpose-With the continuous expansion of railway hubs,increasing functional complexity and growing capacity constraints,the coordinated and efficient utilization of transportation resources-such as stations,lines and maintenance facilities-has become a critical issue for improving hub operational efficiency.This study focuses on the division of functions within railway hubs that incorporate shared stations operating under mixed high-speed and conventional train services.Design/methodology/approach-An optimization model for hub functional allocation is developed to achieve efficient resource utilization in hubs containing mixed-operation stations.A node-arc network representation combined with an improved multi-commodity flow model is employed,taking train dwell and operation time within the hub as the optimization objective.A case study is conducted to derive optimized solutions,followed by both qualitative and quantitative analyses.Findings-The results indicate that optimizing train operation routes and station assignments within the hub can effectively reduce the total occupation time of train flows and significantly improve resource utilization efficiency.Originality/value-The proposed model demonstrates both scientific rigor and practical effectiveness.In realworld operations,it can provide operators with preliminary and proactive functional allocation schemes,help identify key constraints limiting hub capacity utilization and offer decision support for transport plan adjustments or infrastructure and facility upgrades. 展开更多
关键词 Mixed high-speed and conventional operations Railway hub Hub functional allocation Route optimization ILOG CPLEX
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An Ant Colony Optimization Based Dimension Reduction Method for High-Dimensional Datasets 认领 引用 被引量：4
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作者 Ying Li Gang Wang +2 位作者 Huiling Chen Lian Shi Lei Qin 《Journal of Bionic Engineering》 SCIE EI CSCD 2013年第2期231-241,共11页
In this paper, a bionic optimization algorithm based dimension reduction method named Ant Colony Optimization -Selection （ACO-S） is proposed for high-dimensional datasets. Because microarray datasets comprise tens o... In this paper, a bionic optimization algorithm based dimension reduction method named Ant Colony Optimization -Selection （ACO-S） is proposed for high-dimensional datasets. Because microarray datasets comprise tens of thousands of features （genes）, they are usually used to test the dimension reduction techniques. ACO-S consists of two stages in which two well-known ACO algorithms, namely ant system and ant colony system, are utilized to seek for genes, respectively. In the first stage, a modified ant system is used to filter the nonsignificant genes from high-dimensional space, and a number of promising genes are reserved in the next step. In the second stage, an improved ant colony system is applied to gene selection. In order to enhance the search ability of ACOs, we propose a method for calculating priori available heuristic information and design a fuzzy logic controller to dynamically adjust the number of ants in ant colony system. Furthermore, we devise another fuzzy logic controller to tune the parameter （q0） in ant colony system. We evaluate the performance of ACO-S on five microarray datasets, which have dimensions varying from 7129 to 12000. We also compare the performance of ACO-S with the results obtained from four existing well-known bionic optimization algorithms. The comparison results show that ACO-S has a notable ability to＂ generate a gene subset with the smallest size and salient features while yielding high classification accuracy. The comparative results generated by ACO-S adopting different classifiers are also given. The proposed method is shown to be a promising and effective tool for mining high-dimension data and mobile robot navigation. 展开更多
关键词 gene selection feature selection ant colony optimization high-dimensional data
Approximate solutions and nonlinear scalarizations for set optimization with a co-radiant set 认领 引用
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作者 ZHANG Qi XU Yi-hong 《Applied Mathematics(A Journal of Chinese Universities)》 SCIE CSCD 2026年第2期367-385,共19页
In normed linear spaces,by applying the Minkowski difference,the concepts of efficient solutions,weakly efficient solutions,proper efficient solutions and Henig efficient solutions are introduced for set optimization ... In normed linear spaces,by applying the Minkowski difference,the concepts of efficient solutions,weakly efficient solutions,proper efficient solutions and Henig efficient solutions are introduced for set optimization with respect to co-radiant sets,and the relationships among them is discussed.Optimality conditions for minimal solutions and strictly minimal solutions of scalar set optimization are established by using the generalized oriented distance functions,respectively.The relationship between the solutions of set optimization problem and the solutions of scalar problem is studied.Several examples are given to explain our result.Properties of Gerstewitz’s functions with respect to co-radiant sets are discussed,which are used to establish sufficient conditions for weakly efficient solutions of set optimization. 展开更多
关键词 set optimization nonlinear function approximate solution co-radiant set
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Reliability Topology Optimization Based on Kriging-Assisted Level Set Function and Novel Dynamic Hybrid Particle Swarm Optimization Algorithm 认领 引用 被引量：1
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作者 Hang Zhou Xiaojun Ding +1 位作者 Song Chen Qijun Zhang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第8期1907-1933,共27页
Structural Reliability-Based Topology Optimization(RBTO),as an efficient design methodology,serves as a crucial means to ensure the development ofmodern engineering structures towards high performance,long service lif... Structural Reliability-Based Topology Optimization(RBTO),as an efficient design methodology,serves as a crucial means to ensure the development ofmodern engineering structures towards high performance,long service life,and high reliability.However,in practical design processes,topology optimization must not only account for the static performance of structures but also consider the impacts of various responses and uncertainties under complex dynamic conditions,which traditional methods often struggle accommodate.Therefore,this study proposes an RBTO framework based on a Kriging-assisted level set function and a novel Dynamic Hybrid Particle Swarm Optimization(DHPSO)algorithm.By leveraging the Kriging model as a surrogate,the high cost associated with repeatedly running finite element analysis processes is reduced,addressing the issue of minimizing structural compliance.Meanwhile,the DHPSO algorithm enables a better balance between the population’s developmental and exploratory capabilities,significantly accelerating convergence speed and enhancing global convergence performance.Finally,the proposed method is validated through three different structural examples,demonstrating its superior performance.Observed that the computational that,compared to the traditional Solid Isotropic Material with Penalization(SIMP)method,the proposed approach reduces the upper bound of structural compliance by approximately 30%.Additionally,the optimized results exhibit clear material interfaces without grayscale elements,and the stress concentration factor is reduced by approximately 42%.Consequently,the computational results fromdifferent examples verify the effectiveness and superiority of this study across various fields,achieving the goal of providing more precise optimization results within a shorter timeframe. 展开更多
关键词 Reliability topology optimization kriging model level set function dynamic hybrid particle swarm optimization engineering structure
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Optimal personality functioning:An existential Franklian psychobiography of Noel Chabani Manganyi(1940–2024) 认领 引用
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作者 Lulu Mtimkulu Paul J.P Fouché 《Journal of Psychology in Africa》 2026年第3期361-370,共10页
This psychobiography aimed to uncover the characteristics of optimal personality functioning(OPF)across the lifespan of Chabani Manganyi(1940–2024),the first Black South African clinical psychologist.The methodology ... This psychobiography aimed to uncover the characteristics of optimal personality functioning(OPF)across the lifespan of Chabani Manganyi(1940–2024),the first Black South African clinical psychologist.The methodology used in this study encompassed an existential Franklian scholarly psychobiography.Sources of data on Manganyi included only publicly available primary and secondary data.Primary sources included Manganyi’s own writings,such as his autobiography,as well as his academic publications,including the biographies he wrote on creative individuals such as Gordimer,Sekoto and Mphahlele.Secondary sources included scholarly publications by academics and colleagues who knew him,as well as tributes,historical accounts,and archival records related to South African psychology scholarship during apartheid and the country’s transition to democracy.The study’s data sources were captured using online research platforms and search engines that included EBSCOhost,ResearchGate,Google Scholar,the University of the Free State’s Kovsie Catalogue and ProQuest.Alexander’s(1988,1990)biographical approach,which lists nine indicators of thematic salience(i.e.,uniqueness,negation,emphasis,primacy,frequency,error or distortion,isolation,incompletion,and omission)were utilized for the identification,extraction and compilation of salient data for analysis,alongside Frankl’s proposed nine characteristics of optimal personality functioning.Findings revealed that Manganyi personified characteristics of self-determining action,which were consistently evident in his pursuit of education,his intellectual independence,and his scholarly innovations in the field of South African psychology.Manganyi also personified a sense of self-transcendence,primarily expressed through his scholarship,mentorship,and social advocacy.He consistently positioned his dedication to work in his search for broader societal comprehension and transformation,beyond his personal advancement.Manganyi also exhibited qualities of future-directedness,work as vocation,and the search for meaning in resilient living,serving as a role model in managing challenging life and historical circumstances in transformative ways.The findings align with Frankl’s existential characteristics of optimal personality functioning as applied within this scholarly psychobiographical approach,highlighting its cross-cultural transportability in studying historical figures. 展开更多
关键词 Psychobiography Chabani Manganyi Viktor Frankl existentialism optimal personality functioning
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Predefined-Time Distributed Optimization for Resource Allocation Problems With Time-Varying Objective Function and Constraints 认领 引用
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作者 Haotian Wu Yang Liu +1 位作者 Mahmoud Abdel-Aty Weihua Gui 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第11期2353-2355,共3页
Dear Editor,This letter addresses distributed optimization for resource allocation problems with time-varying objective functions and time-varying constraints.Inspired by the distributed average tracking(DAT)approach,... Dear Editor,This letter addresses distributed optimization for resource allocation problems with time-varying objective functions and time-varying constraints.Inspired by the distributed average tracking(DAT)approach,a distributed control protocol is proposed for optimal resource allocation.The convergence to a time-varying optimal solution within a predefined time is proved.Two numerical examples are given to illustrate the effectiveness of the proposed approach. 展开更多
关键词 resource allocation distributed optimization time varying objective function optimal resource allocationthe distributed control protocol time varying constraints predefined time convergence
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SW-DDFT: Parallel Optimization of the Dynamical Density Functional Theory Algorithm Based on Sunway Bluelight II Supercomputer 认领 引用
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作者 Xiaoguang Lv Tao Liu +5 位作者 Han Qin Ying Guo Jingshan Pan Dawei Zhao Xiaoming Wu Meihong Yang 《Computers, Materials & Continua》 SCIE EI 2025年第7期1417-1436,共20页
The Dynamical Density Functional Theory(DDFT)algorithm,derived by associating classical Density Functional Theory(DFT)with the fundamental Smoluchowski dynamical equation,describes the evolution of inhomo-geneous flui... The Dynamical Density Functional Theory(DDFT)algorithm,derived by associating classical Density Functional Theory(DFT)with the fundamental Smoluchowski dynamical equation,describes the evolution of inhomo-geneous fluid density distributions over time.It plays a significant role in studying the evolution of density distributions over time in inhomogeneous systems.The Sunway Bluelight II supercomputer,as a new generation of China’s developed supercomputer,possesses powerful computational capabilities.Porting and optimizing industrial software on this platform holds significant importance.For the optimization of the DDFT algorithm,based on the Sunway Bluelight II supercomputer and the unique hardware architecture of the SW39000 processor,this work proposes three acceleration strategies to enhance computational efficiency and performance,including direct parallel optimization,local-memory constrained optimization for CPEs,and multi-core groups collaboration and communication optimization.This method combines the characteristics of the program’s algorithm with the unique hardware architecture of the Sunway Bluelight II supercomputer,optimizing the storage and transmission structures to achieve a closer integration of software and hardware.For the first time,this paper presents Sunway-Dynamical Density Functional Theory(SW-DDFT).Experimental results show that SW-DDFT achieves a speedup of 6.67 times within a single-core group compared to the original DDFT implementation,with six core groups(a total of 384 CPEs),the maximum speedup can reach 28.64 times,and parallel efficiency can reach 71%,demonstrating excellent acceleration performance. 展开更多
关键词 Sunway supercomputer high-performance computing dynamical density functional theory parallel optimization
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Biological activities and potential functional optimization strategies of corosolic acid:a review 认领 引用
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作者 Baozhu Shi Haixin Sun +4 位作者 Zhuang Sun Qiaojun Jia Hao Zhang Yong Mao Zisheng Luo 《Food Innovation and Advances》 2025年第2期228-237,共10页
Corosolic acid,a naturally occurring pentacyclic triterpenic acid,is widely recognized for its broad spectrum of biological activities,particularly its antidiabetic properties,making it a popular ingredient in dietary... Corosolic acid,a naturally occurring pentacyclic triterpenic acid,is widely recognized for its broad spectrum of biological activities,particularly its antidiabetic properties,making it a popular ingredient in dietary supplements for regulating blood sugar levels.Beyond its anti-diabetic effects,recent studies have revealed its therapeutic potential in areas such as anti-cancer,anti-inflammatory,and antibacterial activities.However,its clinical application is hindered by poor water solubility and low bioavailability due to its molecular structure.This review systematically examines the pharmacological activities of corosolic acid,emphasizing its mechanisms of action in disease intervention.Emerging strategies to overcome its inherent limitations,including chemical modifications,microbial transformations,and advanced delivery systems,are also discussed.Notably,some chemical derivatives exhibitα-glucosidase inhibition with IC50 values half that of corosolic acid.Microbial transformations have been shown to enhance its bioavailability while reducing cancer cell toxicity.Additionally,corosolic acid-based delivery systems have demonstrated significant improvements in solubility,stability,and biological activity.By consolidating current insights into its functional properties and biological activity enhancement methods,this review aims to emphasize the practical application values in food and medicine and the future development of corosolic acid as a versatile bioactive compound. 展开更多
关键词 pentacyclic triterpenic acidis regulating blood sugar biological activities corosolic acid low bioavail dietary supplements functional optimization strategies corosolic acida
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An Efficient Reliability-Based Optimization Method Utilizing High-Dimensional Model Representation and Weight-Point Estimation Method 认领 引用 被引量：2
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作者 Xiaoyi Wang Xinyue Chang +2 位作者 Wenxuan Wang Zijie Qiao Feng Zhang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第5期1775-1796,共22页
The objective of reliability-based design optimization(RBDO)is to minimize the optimization objective while satisfying the corresponding reliability requirements.However,the nested loop characteristic reduces the effi... The objective of reliability-based design optimization(RBDO)is to minimize the optimization objective while satisfying the corresponding reliability requirements.However,the nested loop characteristic reduces the efficiency of RBDO algorithm,which hinders their application to high-dimensional engineering problems.To address these issues,this paper proposes an efficient decoupled RBDO method combining high dimensional model representation(HDMR)and the weight-point estimation method(WPEM).First,we decouple the RBDO model using HDMR and WPEM.Second,Lagrange interpolation is used to approximate a univariate function.Finally,based on the results of the first two steps,the original nested loop reliability optimization model is completely transformed into a deterministic design optimization model that can be solved by a series of mature constrained optimization methods without any additional calculations.Two numerical examples of a planar 10-bar structure and an aviation hydraulic piping system with 28 design variables are analyzed to illustrate the performance and practicability of the proposed method. 展开更多
关键词 Reliability-based design optimization high-dimensional model decomposition point estimation method Lagrange interpolation aviation hydraulic piping system
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Strength,Self-flowing,and Multi-objective Optimization of Cemented Paste Backfill Materials Base on RSM-DF 认领 引用 被引量：1
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作者 LIU Chunkang WANG Hongjiang +2 位作者 WANG Hui SUN Jiaqi BAI Longjian 《Journal of Wuhan University of Technology(Materials Science)》 SCIE EI CAS CSCD 2025年第2期449-461,共13页
The multi-objective optimization of backfill effect based on response surface methodology and desirability function(RSM-DF)was conducted.Firstly,the test results show that the uniaxial compressive strength(UCS)increas... The multi-objective optimization of backfill effect based on response surface methodology and desirability function(RSM-DF)was conducted.Firstly,the test results show that the uniaxial compressive strength(UCS)increases with cement sand ratio(CSR),slurry concentration(SC),and curing age(CA),while flow resistance(FR)increases with SC and backfill flow rate(BFR),and decreases with CSR.Then the regression models of UCS and FR as response values were established through RSM.Multi-factor interaction found that CSR-CA impacted UCS most,while SC-BFR impacted FR most.By introducing the desirability function,the optimal backfill parameters were obtained based on RSM-DF(CSR is 1:6.25,SC is 69%,CA is 11.5 d,and BFR is 90 m3/h),showing close results of Design Expert and high reliability for optimization.For a copper mine in China,RSM-DF optimization will reduce cement consumption by 4758 t per year,increase tailings consumption by about 6700 t,and reduce CO2emission by about 4758 t.Thus,RSM-DF provides a new approach for backfill parameters optimization,which has important theoretical and practical values. 展开更多
关键词 cemented paste backfill response surface methodology desirability function multi-objective optimization
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