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Two-Phase Genetic Algorithm Applied in the Optimization of Multi-Modal Function 认领 引用 被引量：5
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作者 Huang Yu-zhen, Kang Li-shan,Zhou Ai-minState Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, Hubei,China 《Wuhan University Journal of Natural Sciences》 EI CAS 2003年第S1期259-264,共6页
This paper presents a two-phase genetic algorithm (TPGA) based on the multi- parent genetic algorithm (MPGA). Through analysis we find MPGA will lead the population' s evol vement to diversity or convergence accor... This paper presents a two-phase genetic algorithm (TPGA) based on the multi- parent genetic algorithm (MPGA). Through analysis we find MPGA will lead the population' s evol vement to diversity or convergence according to the population size and the crossover size, so we make it run in different forms during the global and local optimization phases and then forms TPGA. The experiment results show that TPGA is very efficient for the optimization of low-dimension multi-modal functions, usually we can obtain all the global optimal solutions. 展开更多
关键词 optimization of multi-modal function genetic algorithm global optimization local optimization
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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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Elitism-based immune genetic algorithm and its application to optimization of complex multi-modal functions 认领 引用 被引量：6
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作者 谭冠政 周代明 +1 位作者 江斌 DIOUBATE Mamady I 《Journal of Central South University of Technology》 2008年第6期845-852,共8页
A novel immune genetic algorithm with the elitist selection and elitist crossover was proposed, which is called the immune genetic algorithm with the elitism （IGAE）. In IGAE, the new methods for computing antibody s... A novel immune genetic algorithm with the elitist selection and elitist crossover was proposed, which is called the immune genetic algorithm with the elitism （IGAE）. In IGAE, the new methods for computing antibody similarity, expected reproduction probability, and clonal selection probability were given. IGAE has three features. The first is that the similarities of two antibodies in structure and quality are all defined in the form of percentage, which helps to describe the similarity of two antibodies more accurately and to reduce the computational burden effectively. The second is that with the elitist selection and elitist crossover strategy IGAE is able to find the globally optimal solution of a given problem. The third is that the formula of expected reproduction probability of antibody can be adjusted through a parameter r, which helps to balance the population diversity and the convergence speed of IGAE so that IGAE can find the globally optimal solution of a given problem more rapidly. Two different complex multi-modal functions were selected to test the validity of IGAE. The experimental results show that IGAE can find the globally maximum/minimum values of the two functions rapidly. The experimental results also confirm that IGAE is of better performance in convergence speed, solution variation behavior, and computational efficiency compared with the canonical genetic algorithm with the elitism and the immune genetic algorithm with the information entropy and elitism. 展开更多
关键词 immune genetic algorithm multi-modal function optimization evolutionary computation elitist selection elitist crossover
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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
Adaptive Reinforcement Learning with Multi-Modal Perception for Autonomous Formation Control and Exploration in Large-Scale Multi-UAV Swarms 认领 引用
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作者 Ziyuan Ma Huajun Gong Xinhua Wang 《Journal of Beijing Institute of Technology》 EI CAS 2026年第1期63-83,共21页
To address the challenge of achieving decentralized,scalable,and adaptive control for large-scale multiple unmanned aerial vehicle(multi-UAV)swarms in dynamic urban environments with obstacles and wind perturbations,w... To address the challenge of achieving decentralized,scalable,and adaptive control for large-scale multiple unmanned aerial vehicle(multi-UAV)swarms in dynamic urban environments with obstacles and wind perturbations,we proposed a hybrid framework integrating adaptive reinforcement learning(RL),multi-modal perception fusion,and enhanced pigeon flock optimization(PFO)with curiosity-driven exploration to enable robust autonomous and formation control.The framework leverages meta-learning to optimize RL policies for real-time adaptation,fuses sensor data for precise state estimation,and enhances PFO with learned leader-follower dynamics and exploration rewards to maintain cohesive formations and explore uncertain areas.For swarms of 10–30 UAVs,it achieves 34%faster convergence,61%reduced stability root mean square error(RMSE),88%fewer collisions and 85.6%–92.3%success rates in target detection and encirclement,outperforming standard multi-agent RL,pure PFO,and single-modality RL.Three-dimensional trajectory visualizations confirm cohesive formations,collision-free maneuvers,and efficient exploration in urban search-and-rescue scenarios.Innovations include meta-RL for rapid adaptation,multi-modal fusion for robust perception,and curiosity-driven PFO for scalable,decentralized control,advancing real-world multi-UAV swarm autonomy and coordination. 展开更多
关键词 multiple unmanned aerial vehicle(multi-UAV)swarm autonomous control reinforcement learning(RL) multi-modal perception pigeon flock optimization(PFO)
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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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An Improved Variant of Multi-Population Cooperative Constrained Multi-Objective Optimization(MCCMO)for Multi-Objective Optimization Problem 认领 引用
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作者 Muhammad Waqar Khan Adnan Ahmed Siddiqui Syed Sajjad Hussain Rizvi 《Computers, Materials & Continua》 SCIE EI 2026年第2期1874-1888,共15页
The multi-objective optimization problems,especially in constrained environments such as power distribution planning,demand robust strategies for discovering effective solutions.This work presents the improved variant... The multi-objective optimization problems,especially in constrained environments such as power distribution planning,demand robust strategies for discovering effective solutions.This work presents the improved variant of the Multi-population Cooperative Constrained Multi-Objective Optimization(MCCMO)Algorithm,termed Adaptive Diversity Preservation(ADP).This enhancement is primarily focused on the improvement of constraint handling strategies,local search integration,hybrid selection approaches,and adaptive parameter control.Theimproved variant was experimented on with the RWMOP50 power distribution systemplanning benchmark.As per the findings,the improved variant outperformed the original MCCMO across the eleven performance metrics,particularly in terms of convergence speed,constraint handling efficiency,and solution diversity.The results also establish that MCCMOADP consistently delivers substantial performance gains over the baseline MCCMO,demonstrating its effectiveness across performancemetrics.The new variant also excels atmaintaining the balanced trade-off between exploration and exploitation throughout the search process,making it especially suitable for complex optimization problems in multiconstrained power systems.These enhancements make MCCMO-ADP a valuable and promising candidate for handling problems such as renewable energy scheduling,logistics planning,and power system optimization.Future work will benchmark the MCCMO-ADP against widely recognized algorithms such as NSGA-Ⅱ,NSGA-Ⅲ,and MOEA/D and will also extend its validation to large-scale real-world optimization domains to further consolidate its generalizability. 展开更多
关键词 MCCMO algorithms adaptive diversity preservation RWMOP50 power distribution system multi-modal multi objective optimization evolutionary algorithm multi objective problem
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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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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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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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Dipper Throated Optimization Algorithm for Unconstrained Function and Feature Selection 认领 引用 被引量：9
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作者 Ali E.Takieldeen El-Sayed M.El-kenawy +1 位作者 Mohammed Hadwan Rokaia M.Zaki 《Computers, Materials & Continua》 SCIE EI 2022年第7期1465-1481,共17页
Dipper throated optimization(DTO)algorithm is a novel with a very efficient metaheuristic inspired by the dipper throated bird.DTO has its unique hunting technique by performing rapid bowing movements.To show the effi... Dipper throated optimization(DTO)algorithm is a novel with a very efficient metaheuristic inspired by the dipper throated bird.DTO has its unique hunting technique by performing rapid bowing movements.To show the efficiency of the proposed algorithm,DTO is tested and compared to the algorithms of Particle Swarm Optimization(PSO),Whale Optimization Algorithm(WOA),Grey Wolf Optimizer(GWO),and Genetic Algorithm(GA)based on the seven unimodal benchmark functions.Then,ANOVA and Wilcoxon rank-sum tests are performed to confirm the effectiveness of the DTO compared to other optimization techniques.Additionally,to demonstrate the proposed algorithm’s suitability for solving complex realworld issues,DTO is used to solve the feature selection problem.The strategy of using DTOs as feature selection is evaluated using commonly used data sets from the University of California at Irvine(UCI)repository.The findings indicate that the DTO outperforms all other algorithms in addressing feature selection issues,demonstrating the proposed algorithm’s capabilities to solve complex real-world situations. 展开更多
关键词 Metaheuristic optimization swarmoptimization feature selection function optimization
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Multi-objective optimization of rolling schedule based on cost function for tandem cold mill 认领 引用 被引量：10
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作者 陈树宗 张欣 +3 位作者 彭良贵 张殿华 孙杰 刘印忠 《Journal of Central South University》 SCIE EI CAS 2014年第5期1733-1740,共8页
In terms of tandem cold mill productivity and product quality, a multi-objective optimization model of rolling schedule based on cost fimction was proposed to determine the stand reductions, inter-stand tensions and r... In terms of tandem cold mill productivity and product quality, a multi-objective optimization model of rolling schedule based on cost fimction was proposed to determine the stand reductions, inter-stand tensions and rolling speeds for a specified product. The proposed schedule optimization model consists of several single cost fi.mctions, which take rolling force, motor power, inter-stand tension and stand reduction into consideration. The cost function, which can evaluate how far the rolling parameters are from the ideal values, was minimized using the Nelder-Mead simplex method. The proposed rolling schedule optimization method has been applied successfully to the 5-stand tandem cold mill in Tangsteel, and the results from a case study show that the proposed method is superior to those based on empirical formulae. 展开更多
关键词 tandem cold mill multi-object optimization rolling schedule cost function simplex algorithm
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Well production optimization using streamline features-based objective function and Bayesian adaptive direct search algorithm 认领 引用 被引量：10
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作者 Qi-Hong Feng Shan-Shan Li +2 位作者 Xian-Min Zhang Xiao-Fei Gao Ji-Hui Ni 《Petroleum Science》 SCIE CAS CSCD 2022年第6期2879-2894,共16页
Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.T... Well production optimization is a complex and time-consuming task in the oilfield development.The combination of reservoir numerical simulator with optimization algorithms is usually used to optimize well production.This method spends most of computing time in objective function evaluation by reservoir numerical simulator which limits its optimization efficiency.To improve optimization efficiency,a well production optimization method using streamline features-based objective function and Bayesian adaptive direct search optimization(BADS)algorithm is established.This new objective function,which represents the water flooding potential,is extracted from streamline features.It only needs to call the streamline simulator to run one time step,instead of calling the simulator to calculate the target value at the end of development,which greatly reduces the running time of the simulator.Then the well production optimization model is established and solved by the BADS algorithm.The feasibility of the new objective function and the efficiency of this optimization method are verified by three examples.Results demonstrate that the new objective function is positively correlated with the cumulative oil production.And the BADS algorithm is superior to other common algorithms in convergence speed,solution stability and optimization accuracy.Besides,this method can significantly accelerate the speed of well production optimization process compared with the objective function calculated by other conventional methods.It can provide a more effective basis for determining the optimal well production for actual oilfield development. 展开更多
关键词 Well production Optimization efficiency Streamline simulation Streamline feature Objective function Bayesian adaptive direct search algorithm
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