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Multi-objective optimization of reservoir refill operate considering energy storage and hydrological conditions 认领 引用
1
作者 Lihua Chen Kaipeng Yang +2 位作者 Yunyao Chen Xiaoli Zhang Xuefang Li 《River》 EI CSCD 2026年第2期282-294,共13页
Optimizing reservoir refill operation rules is crucial for enhancing reservoir sustainability and resilience.This study proposes a refill operation model designed to derive optimal refill guide curves by considering f... Optimizing reservoir refill operation rules is crucial for enhancing reservoir sustainability and resilience.This study proposes a refill operation model designed to derive optimal refill guide curves by considering flood risk prevention,maximum storage levels,and the combined benefits of hydropower generation and energy storage.The proposed model was applied to the Longtan Reservoir in China.The results indicate that employing various optimal refill guide curves,tailored to wet,normal,and dry hydrological conditions,yields better outcomes than using a single refill guide curve.In wet years,the maximum annual combined benefits of hydropower generation and energy storage increased by 2.88%.In normal and dry years,the average annual water level at the end of the water storage period was significantly raised by 0.71 and 1.9 m,respectively.Correspondingly,the reservoir storage volume increased by 2.53×108m3and 6.19×108m3,respectively.These findings demonstrate that the proposed approach can enhance the benefits of refill operations at the Longtan Reservoir under varying hydrological conditions. 展开更多
关键词 energy storage hydrological conditions multi‐objective optimization NSGA‐II reservoir refill guide curves
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An efficient multi‑objective optimization framework based on data‑driven identification and adaptive directed correction for complex distillation processes 认领 引用
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作者 Fucheng Xu Lu Yang +6 位作者 Zihao Wang Tao Shi Rongsheng Lin Bohong Wang Hengcong Tao Zhiliang Cheng Weifeng Shen 《ENGINEERING Chemical Engineering》 SCIE EI CAS CSCD 2026年第7期25-35,共11页
Complex distillation processes can often be effectively optimized using meta‑heuristic algorithms.However,during optimization procedure,a large number of infeasible solutions are generated,hindering efficient explorat... Complex distillation processes can often be effectively optimized using meta‑heuristic algorithms.However,during optimization procedure,a large number of infeasible solutions are generated,hindering efficient exploration of feasible,high‑performance regions of the search space.In this study,we propose a data‑driven identification and adaptive directed correction strategy for handling infeasible solutions,and on this basis,develop an efficient multi‑objective optimization framework(MO‑DIDC)for complex distillation processes.By identifying infeasible solutions that closely resemble high‑performance ones,the framework leverages them to accelerate convergence to optimal designs.A surrogate model is trained to distinguish high‑and low‑performance solutions and is then used to identify potentially high‑performance candidates within the infeasible set.Through similarity analysis,the most influential variable is selected for correction to generate new promising solutions.This strategy reduces unnecessary exploration of infeasible regions and concentrates computational effort on feasible,high‑quality solutions.Demonstrated on a side‑stream double‑column extractive distillation system and a four‑column extractive distillation system,the proposed optimization framework outperforms a widely used genetic algorithm while substantially improving computational efficiency,achieving optimization time reductions of 35.3%and 20.8%,respectively.Overall,the proposed MO‑DIDC framework provides an effective and computationally efficient tool for the optimization of complex distillation processes. 展开更多
关键词 data‑driven identification adaptive directed correction infeasible solutions handling multi‑objective optimization complex distillation process
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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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Multi-objective ANN-driven genetic algorithm optimization of energy efficiency measures in an NZEB multi-family house building in Greece 认领 引用
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《建筑节能（中英文）》 CAS 2026年第2期62-62,共1页
The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measu... The goal of the present work is to demonstrate the potential of Artificial Neural Network(ANN)-driven Genetic Algorithm(GA)methods for energy efficiency and economic performance optimization of energy efficiency measures in a multi-family house building in Greece.The energy efficiency measures include different heating/cooling systems(such as low-temperature and high-temperature heat pumps,natural gas boilers,split units),building envelope components for floor,walls,roof and windows of variable heat transfer coefficients,the installation of solar thermal collectors and PVs.The calculations of the building loads and investment and operating and maintenance costs of the measures are based on the methodology defined in Directive 2010/31/EU,while economic assumptions are based on EN 15459-1 standard.Typically,multi-objective optimization of energy efficiency measures often requires the simulation of very large numbers of cases involving numerous possible combinations,resulting in intense computational load.The results of the study indicate that ANN-driven GA methods can be used as an alternative,valuable tool for reliably predicting the optimal measures which minimize primary energy consumption and life cycle cost of the building with greatly reduced computational requirements.Through GA methods,the computational time needed for obtaining the optimal solutions is reduced by 96.4%-96.8%. 展开更多
关键词 energy efficiency measures gas boilerssplit units building envelope components energy efficiency economic performance artificial neural network ann driven multi objective optimization economic performance optimization ANN driven GA methods
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Multi-objective optimization of grinding process parameters for improving gear machining precision 认领 引用 被引量：4
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作者 YOU Tong-fei HAN Jiang +4 位作者 TIAN Xiao-qing TANG Jian-ping LU Yi-guo LI Guang-hui XIA Lian 《Journal of Central South University》 SCIE EI CAS CSCD 2025年第2期538-551,共14页
The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can caus... The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods. 展开更多
关键词 worm wheel gear grinding machine gear machining precision machining process parameters multi objective optimization
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Two Performance Indicators Assisted Infill Strategy for Expensive Many⁃Objective Optimization 认领 引用
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作者 Yi Zhao Jianchao Zeng Ying Tan 《Journal of Harbin Institute of Technology(New Series)》 CAS 2025年第5期24-40,共17页
In recent years,surrogate models derived from genuine data samples have proven to be efficient in addressing optimization challenges that are costly or time⁃intensive.However,the individuals in the population become i... In recent years,surrogate models derived from genuine data samples have proven to be efficient in addressing optimization challenges that are costly or time⁃intensive.However,the individuals in the population become indistinguishable as the curse of dimensionality increases in the objective space and the accumulation of surrogate approximated errors.Therefore,in this paper,each objective function is modeled using a radial basis function approach,and the optimal solution set of the surrogate model is located by the multi⁃objective evolutionary algorithm of strengthened dominance relation.The original objective function values of the true evaluations are converted to two indicator values,and then the surrogate models are set up for the two performance indicators.Finally,an adaptive infill sampling strategy that relies on approximate performance indicators is proposed to assist in selecting individuals for real evaluations from the potential optimal solution set.The algorithm is contrasted against several advanced surrogate⁃assisted evolutionary algorithms on two suites of test cases,and the experimental findings prove that the approach is competitive in solving expensive many⁃objective optimization problems. 展开更多
关键词 expensive multi⁃objective optimization problems infill sample strategy evolutionary optimization algorithm
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Modeling and multi-objective optimization of a gasoline engine using neural networks and evolutionary algorithms 认领 引用 被引量：9
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作者 JoséD. MARTíNEZ-MORALES Elvia R. PALACIOS-HERNáNDEZ Gerardo A. VELáZQUEZ-CARRILLO 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2013年第9期657-670,共14页
In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （S... In this paper, a multi-objective particle swarm optimization （MOPSO） algorithm and a nondominated sorting genetic algorithm II （NSGA-II） are used to optimize the operating parameters of a 1.6 L, spark ignition （SI） gasoline engine. The aim of this optimization is to reduce engine emissions in terms of carbon monoxide （CO）, hydrocarbons （HC）, and nitrogen oxides （NOx）, which are the causes of diverse environmental problems such as air pollution and global warming. Stationary engine tests were performed for data generation, covering 60 operating conditions. Artificial neural networks （ANNs） were used to predict exhaust emissions, whose inputs were from six engine operating parameters, and the outputs were three resulting exhaust emissions. The outputs of ANNs were used to evaluate objective functions within the optimization algorithms： NSGA-II and MOPSO. Then a decision-making process was conducted, using a fuzzy method to select a Pareto solution with which the best emission reductions can be achieved. The NSGA-II algorithm achieved reductions of at least 9.84%, 82.44%, and 13.78% for CO, HC, and NOx, respectively. With a MOPSO algorithm the reached reductions were at least 13.68%, 83.80%, and 7.67% for CO, HC, and NOx, respectively. 展开更多
关键词 Engine calibration Multi-objective optimization Neural networks Multiple objective particle swarm optimization（MOPSO） Nondominated sorting genetic algorithm II （NSGA-II）
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Multi Objective Decision Making Method Based on Evaluation Criterion for Problems with Linear Value Function 认领 引用
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作者 蒋尚华 韩勇 徐南荣 《Journal of Southeast University(English Edition)》 CAS 1998年第1期114-121,共8页
A class of interactive multi objective decision making method by means of evaluation criterion is proposed for problems with linear value function,in which case,the decision maker(DM) usually has only unwhole informat... A class of interactive multi objective decision making method by means of evaluation criterion is proposed for problems with linear value function,in which case,the decision maker(DM) usually has only unwhole information of weights for objectives. The concept of fault measure of the evaluation criterion is proposed to measure the deviation of the evaluation criterion from the DMs preference structure.The approach to obtain an upper boundary of fault measure of an evaluation criterion,and the approach to modify the evaluation criterion to be one with smaller fault measure,and the approach to obtain a pre optimized objective set by evaluation criterion with certain fault measure are also proposed. 展开更多
关键词 multi objective decision making evaluation criterion fault measure of evaluation criterion pre optimized objective set
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Multiple objectives application approach to waste minimization 认领 引用
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作者 张清宇 《Journal of Zhejiang University Science》 2002年第4期405-411,共7页
Besides economics and controllability, waste minimization has now become an objective in designing chemical processes, and usually leads to high costs of investment and operation. An attempt was made to minimize waste... Besides economics and controllability, waste minimization has now become an objective in designing chemical processes, and usually leads to high costs of investment and operation. An attempt was made to minimize waste discharged from chemical reaction processes during the design and modification process while the operation conditions were also optimized to meet the requirements of technology and economics. Multiobjectives decision nonlinear programming (NLP) was employed to optimize the operation conditions of a chemical reaction process and reduce waste. A modeling language package-SPEEDUP was used to simulate the process. This paper presents a case study of the benzene production process. The flowsheet factors affecting the economics and waste generation were examined. Constraints were imposed to reduce the number of objectives and carry out optimal calculations easily. After comparisons of all possible solutions, best-compromise approach was applied to meet technological requirements and minimize waste. 展开更多
关键词 Waste minimization Multiple objectives optimization Chemical reaction process.
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Multi-objective route planning approach for timely searching tasks of a supervised robot 认领 引用
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作者 刘鹏 熊光明 +2 位作者 李勇 姜岩 龚建伟 《Journal of Beijing Institute of Technology》 EI CAS 2014年第4期481-489,共9页
To performance efficient searching for an operator-supervised mobile robot, a multiple objectives route planning approach is proposed considering timeliness and path cost. An improved fitness function for route planni... To performance efficient searching for an operator-supervised mobile robot, a multiple objectives route planning approach is proposed considering timeliness and path cost. An improved fitness function for route planning is proposed based on the multi-objective genetic algorithm （MOGA） for multiple objectives traveling salesman problem （MOTSP）. Then, the path between two route nodes is generated based on the heuristic path planning method A ＊. A simplified timeliness function for route nodes is proposed to represent the timeliness of each node. Based on the proposed timeliness function, experiments are conducted using the proposed two-stage planning method. The experimental results show that the proposed MOGA with improved fitness function can perform the searching function well when the timeliness of the searching task needs to be taken into consideration. 展开更多
关键词 multiple objective optimization multi-objective genetic algorithm supervised robots route planning timeliness
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Multi-Objective Optimization with Artificial Neural Network Based Robust Paddy Yield Prediction Model 认领 引用
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作者 S.Muthukumaran P.Geetha E.Ramaraj 《Intelligent Automation & Soft Computing》 SCIE 2023年第1期215-230,共16页
Agriculture plays a vital role in the food production process that occupies nearly one-third of the total surface of the earth.Rice is propagated from the seeds of paddy and it is a stable food almost used byfifty per... Agriculture plays a vital role in the food production process that occupies nearly one-third of the total surface of the earth.Rice is propagated from the seeds of paddy and it is a stable food almost used byfifty percent of the total world population.The extensive growth of the human population alarms us to ensure food security and the country should take proper food steps to improve the yield of food grains.This paper concentrates on improving the yield of paddy by predicting the factors that influence the growth of paddy with the help of Evolutionary Computation Techniques.Most of the researchers used to relay on historical records of meteorological parameters to predict the yield of paddy.There is a lack in analyzing the day to day impact of meteorological parameters such as direction of wind,relative humidity,Instant Wind Speed in paddy cultivation.The real time meteorological data collected and analysis the impact of weather parameters from the day of paddy sowing to till the last day of paddy harvesting with regular time series.A Robust Optimized Artificial Neural Network(ROANN)Algorithm with Genetic Algorithm(GA)and Multi Objective Particle Swarm Optimization Algorithm(MOPSO)proposed to predict the factors that to be concentrated by farmers to improve the paddy yield in cultivation.A real time paddy data collected from farmers of Tamilnadu and the meteorological parameters were matched with the cropping pattern of the farmers to construct the database.The input parameters were optimized either by using GA or MOPSO optimization algorithms to reconstruct the database.Reconstructed database optimized by using Artificial Neural Network Back Propagation Algorithm.The reason for improving the growth of paddy was identified using the output of the Neural Network.Performance metrics such as Accuracy,Error Rate etc were used to measure the performance of the proposed algorithm.Comparative analysis made between ANN with GA and ANN with MOPSO to identify the recommendations for improving the paddy yield. 展开更多
关键词 ANN back propagation algorithm genetic algorithm multi objective particle swarm optimization algorithm
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Selection Method of Multi-Objective Problems Using Genetic Algorithm in Motion Plan of AUV 认领 引用 被引量：4
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作者 ZHANG Ming-jun , ZHENG Jin-xing , ZHANG Jing College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001 ,China College of Computer and Information Science, Harbin Engineering University, Harbin 150001 , China 《Journal of Marine Science and Application》 2002年第1期81-86,共6页
To research the effect of the selection method of multi — objects genetic algorithm problem on optimizing result, this method is analyzed theoretically and discussed by using an autonomous underwater vehicle (AUV) as... To research the effect of the selection method of multi — objects genetic algorithm problem on optimizing result, this method is analyzed theoretically and discussed by using an autonomous underwater vehicle (AUV) as an object. A changing weight value method is put forward and a selection formula is modified. Some experiments were implemented on an AUV, TwinBurger. The results shows that this method is effective and feasible. 展开更多
关键词 AUV multi — objective optimization genetic algorithm selection method
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Multi-objective Firefly Algorithm for Test Data Generation with Surrogate Model 认领 引用
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作者 Wenning Zhang Qinglei Zhou +1 位作者 Chongyang Jiao Ting Xu 《国际计算机前沿大会会议论文集》 EI 2021年第1期283-299,共17页
To solve the emerging complex optimization problems, multi objectiveoptimization algorithms are needed. By introducing the surrogate model forapproximate fitness calculation, the multi objective firefly algorithm with... To solve the emerging complex optimization problems, multi objectiveoptimization algorithms are needed. By introducing the surrogate model forapproximate fitness calculation, the multi objective firefly algorithm with surrogatemodel (MOFA-SM) is proposed in this paper. Firstly, the population wasinitialized according to the chaotic mapping. Secondly, the external archive wasconstructed based on the preference sorting, with the lightweight clustering pruningstrategy. In the process of evolution, the elite solutions selected from archivewere used to guide the movement to search optimal solutions. Simulation resultsshow that the proposed algorithm can achieve better performance in terms ofconvergence iteration and stability. 展开更多
关键词 Firefly algorithm Multi objective optimization Surrogate model Test data generation
MODS: A Novel Metaheuristic of Deterministic Swapping for the Multi-Objective Optimization of Combinatorials Problems 认领 引用
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作者 Elias David Nifio Ruiz Carlos Julio Ardila Hemandez +2 位作者 Daladier Jabba Molinares Agustin Barrios Sarmiento Yezid Donoso Meisel 《Computer Technology and Application》 2011年第4期280-292,共13页
This paper states a new metaheuristic based on Deterministic Finite Automata （DFA） for the multi - objective optimization of combinatorial problems. First, a new DFA named Multi - Objective Deterministic Finite Auto... This paper states a new metaheuristic based on Deterministic Finite Automata （DFA） for the multi - objective optimization of combinatorial problems. First, a new DFA named Multi - Objective Deterministic Finite Automata （MDFA） is defined. MDFA allows the representation of the feasible solutions space of combinatorial problems. Second, it is defined and implemented a metaheuritic based on MDFA theory. It is named Metaheuristic of Deterministic Swapping （MODS）. MODS is a local search strategy that works using a MDFA. Due to this, MODS never take into account unfeasible solutions. Hence, it is not necessary to verify the problem constraints for a new solution found. Lastly, MODS is tested using well know instances of the Bi-Objective Traveling Salesman Problem （TSP） from TSPLIB. Its results were compared with eight Ant Colony inspired algorithms and two Genetic algorithms taken from the specialized literature. The comparison was made using metrics such as Spacing, Generational Distance, Inverse Generational Distance and No-Dominated Generation Vectors. In every case, the MODS results on the metrics were always better and in some of those cases, the superiority was 100%. 展开更多
关键词 Metaheuristic deterministic finite automata combinatorial problem multi - objective optimization metrics.
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Inverse kinematics and redundancy resolution in manipulators:Methods,optimization objectives,and future trends 认领 引用
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作者 Hongjun Xing Ruixiang Huang +5 位作者 Yuqi Yang Jinbao Chen Weihua Li Chen Yao Chuang Shi Liang Ding 《Biomimetic Intelligence & Robotics》 EI CSCD 2026年第2期22-48,共27页
Redundant manipulators possess additional degrees of freedom that enable superior dexterity,adaptability,and fault tolerance in complex environments.However,this redundancy also introduces challenges in inverse kinema... Redundant manipulators possess additional degrees of freedom that enable superior dexterity,adaptability,and fault tolerance in complex environments.However,this redundancy also introduces challenges in inverse kinematics(IK)and redundancy resolution,as multiple feasible joint configurations may exist for a given end-effector task.This review systematically summarizes the state of the art in IK methods and optimization objectives for redundant manipulators.It first classifies IK approaches into analytical,numerical,optimization-based,and artificial intelligence-driven categories,highlighting their mathematical foundations,computational efficiency,and real-time feasibility.Next,various optimization objectives are analyzed from three perspectives:manipulability and dexterity indices,joint-level criteria such as torque or energy minimization,and task-level performance metrics including accuracy,smoothness,and collision avoidance.The integration of IK and redundancy resolution within hierarchical control,task-priority,and multi-objective frameworks is discussed,along with representative applications in industrial,medical,service,and space robotics.Finally,emerging research directions are identified,including hybrid learning-optimization paradigms,system-level fusion,collaborative redundancy resolution in multi-agent systems,and digital twin-enabled evaluation for trustworthy deployment.This survey provides both theoretical and practical insights for developing adaptive,explainable,and deployable redundancy-resolution systems for next-generation intelligent robots. 展开更多
关键词 Redundant manipulators Inverse kinematics Redundancy resolution Optimization objectives
Reinforcement Learning in Materials Science:Recent Advances,Methodologies and Applications 认领 引用 被引量：1
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作者 Jiaye Li Xinyuan Zhang +7 位作者 Chunlei Shang Xing Ran Zhe Wang Chengjiang Tang Xiaohang Zhang Mingshuo Nie Wei Xu Xin Lu 《Acta Metallurgica Sinica(English Letters)》 SCIE EI CAS CSCD 2025年第12期2077-2101,共25页
In the era of big data,reinforcement learning(RL)has emerged as a powerful data-driven optimization approach in materials science,enabling unprecedented advances in material design and performance improvement.Unlike t... In the era of big data,reinforcement learning(RL)has emerged as a powerful data-driven optimization approach in materials science,enabling unprecedented advances in material design and performance improvement.Unlike traditional trial-and-error and physics-based approaches,RL agents autonomously identify optimal strategies across high-dimensional and dynamic design spaces by iterative interactions with complex environments.This capability makes RL especially effective for target optimization and sequential decision-making in challenging materials science problems.In this review,we present a comprehensive overview of fundamental RL algorithms,including Q-learning,deep Q-networks(DQN),actor-critic methods,and deep deterministic policy gradient(DDPG).Then,the core mechanisms,advantages,limitations,and representative applications of RL in materials discovery,property optimization,process control,and manufacturing are discussed systematically.Lastly,key future research directions and opportunities are outlined.The perspectives presented herein aim to foster interdisciplinary collaboration and drive innovation at the frontier of AI‑driven materials science. 展开更多
关键词 Reinforcement learning Data-driven Objective optimization Material design Material application
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Machine Learning‐Driven Predictive Modeling and Multi‐Objective Exploration of Oxaliplatin‐Loaded Nanocarriers for Enhanced Loading and Encapsulation Efficiency 认领 引用
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作者 Abbas Rahdar Maryam Shirzad +1 位作者 Sonia Fathi‐Karkan M.Ali Aboudzadeh 《Materials Genome Engineering Advances》 CAS CSCD 2026年第2期75-85,共11页
Oxaliplatin(OXA),a critical third‐generation platinum chemotherapeutic,is significantly limited by suboptimal loading capacity and encapsulation efficiency in nanoparticle‐based delivery systems.To address this,we d... Oxaliplatin(OXA),a critical third‐generation platinum chemotherapeutic,is significantly limited by suboptimal loading capacity and encapsulation efficiency in nanoparticle‐based delivery systems.To address this,we developed an integrated machine learning(ML)and multi‐objective optimization(MOO)framework for the simultaneous prediction and exploration of loading efficiency(LE)and encapsulation efficiency(EE).Ensemble learning models,trained on a curated dataset of 70 experimentally characterized nanocarrier formulations,demonstrated robust predictive performance under stringent leave‐one‐paper‐out(LOPO)cross‐validation(R2=0.87 for LE,R2=0.84 for EE).The multi‐objective exploration identified a Pareto‐optimal design space,with predicted performance reaching up to 45.3%LE and 87.2%EE,and pinpointed a balanced knee‐point formulation at 40.2%LE and 83.7%EE.Interpretable ML analysis revealed surface area‐to‐volume ratio,coordination site availability,and zeta potential as the primary physicochemical drivers of OXA loading and retention.Consequently,an optimized nanocarrier profile,characterized by a particle size of 90-110 nm,a negative surface charge,and a carboxylate‐rich composition,was derived.This study establishes a predictive,data‐driven computational framework that bridges the gap between single‐objective prediction and the holistic design of high‐performance nanocarriers,providing a rational blueprint for accelerating the development of more effective OXA‐based nanotherapies for colorectal cancer. 展开更多
关键词 encapsulation efficiency|loading efficiency|machine‐learning|multi‐objective optimization|oxaliplatin
Improving deconvolution reliability in well test analysis with an automated objective function 认领 引用
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作者 Mina S.Khalaf 《Deep Resources Engineering》 2026年第1期53-74,共22页
Deconvolution is a mathematical technique that eliminates the effects of production rate variations in pressure transient data,enabling the recovery of a smoother pressure signal extended across both drawdown and buil... Deconvolution is a mathematical technique that eliminates the effects of production rate variations in pressure transient data,enabling the recovery of a smoother pressure signal extended across both drawdown and buildup periods.This approach has distinct advantages over conventional analysis methods,including enhanced radius of investigation,improved derivative quality,and simplified use of existing interpretation techniques.Since the introduction of von Schroeter deconvolution method,as the first stable algorithm,in the early 2000s,several researchers have advanced the methodology by refining algorithms and improving the objective functions that govern their performance.Despite these developments,the definition of a robust and reliable objective function remains a challenge in achieving accurate deconvolution results.This study introduces a new objective function(NOF)for well test deconvolution that simplifies weighting schemes,eliminates subjective parameter tuning,and improves robustness under noisy conditions.Unlike earlier methods,the NOF assigns automated weights to pressure and rate measurements based on gauge accuracy,ensuring objective normalization of errors and removing the need for the unresolved flow rate regularization parameter.This streamlined approach not only reduces complexity but also enhances accuracy.The proposed noise-oriented NOF consistently outperformed traditional formulations across all simulated and field cases.In four synthetic examples with varying reservoir conditions and noise levels,the NOF successfully recovered smooth pressure responses and accurate flow rates,while the original von Schroeter and Levitan objective functions produced distorted or shifted signals and failed to adjust rates.Quantitative comparisons showed that this study produces a marked reduction in average deconvolution errors.This study enhances the stability and accuracy of deconvolution and offers a practical and superior tool for reservoir characterization,making it easier for engineers to extract meaningful reservoir properties,extend the radius of investigation,and reduce interpretation errors in real field applications. 展开更多
关键词 Pressure transient analysis Deconvolution Objective function optimization Reservoir characterization Measurements uncertainty
Noise Reduction of an Axial Piston Pump by Valve Plate Optimization 认领 引用 被引量：31
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作者 Shao-Gan Ye Jun-Hui Zhang Bing Xu 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2018年第3期85-100,共16页
Current researches mainly focus on the investigations of the valve plate utilizing pressure relief grooves. However,air?release and cavitation can occur near the grooves. The valve plate utilizing damping holes show e... Current researches mainly focus on the investigations of the valve plate utilizing pressure relief grooves. However,air?release and cavitation can occur near the grooves. The valve plate utilizing damping holes show excellent perfor?mance in avoiding air?release and cavitation. This study aims to reduce the noise emitted from an axial piston pump using a novel valve plate utilizing damping holes. A dynamic pump model is developed,in which the fluid properties are carefully modeled to capture the phenomena of air release and cavitation. The causes of di erent noise sources are investigated using the model. A comprehensive parametric analysis is conducted to enhance the understanding of the e ects of the valve plate parameters on the noise sources. A multi?objective genetic algorithm optimization method is proposed to optimize the parameters of valve plate. The amplitudes of the swash plate moment and flow rates in the inlet and outlet ports are defined as the objective functions. The pressure overshoot and undershoot in the piston chamber are limited by properly constraining the highest and lowest pressure values. A comparison of the various noise sources between the original and optimized designs over a wide range of pressure levels shows that the noise sources are reduced at high pressures. The results of the sound pressure level measurements show that the optimized valve plate reduces the noise level by 1.6 d B(A) at the rated working condition. The proposed method is e ective in reducing the noise of axial piston pumps and contributes to the development of quieter axial piston machines. 展开更多
关键词 Axial piston pump Noise reduction Fluid?borne noise Structure?borne noise Parametric analysis Multi?objective optimization
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Influence of yaw damper layouts on locomotive lateral dynamics performance:Pareto optimization and parameter analysis 认领 引用 被引量：7
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作者 Guang LI Yuan YAO +2 位作者 Longjiang SHEN Xiaoxing DENG Wensheng ZHONG 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS CSCD 2023年第5期450-464,共15页
High-speed locomotives are prone to carbody or bogie hunting when the wheel-rail contact conicity is excessively low or high.This can cause negative impacts on vehicle dynamics performance.This study presents four typ... High-speed locomotives are prone to carbody or bogie hunting when the wheel-rail contact conicity is excessively low or high.This can cause negative impacts on vehicle dynamics performance.This study presents four types of typical yaw damper layouts for a high-speed locomotive(Bo-Bo)and compares,by using the multi-objective optimization method,the influences of those layouts on the lateral dynamics performance of the locomotive;the linear stability indexes under lowconicity and high-conicity conditions are selected as optimization objectives.Furthermore,the radial basis function-based highdimensional model representation(RBF-HDMR)method is used to conduct a global sensitivity analysis(GSA)between key suspension parameters and the lateral dynamics performance of the locomotive,including the lateral ride comfort on straight tracks under the low-conicity condition,and also the operational safety on curved tracks.It is concluded that the layout of yaw dampers has a considerable impact on low-conicity stability and lateral ride comfort but has little influence on curving performance.There is also an important finding that only when the locomotive adopts the layout with opening outward,the difference in lateral ride comfort between the front and rear ends of the carbody can be eliminated by adjusting the lateral installation angle of the yaw dampers.Finally,force analysis and modal analysis methods are adopted to explain the influence mechanism of yaw damper layouts on the lateral stability and differences in lateral ride comfort between the front and rear ends of the carbody. 展开更多
关键词 High-speed locomotive Yaw damper layout Lateral stability Lateral ride comfort Multi objective optimization Global sensitivity analysis(GSA)
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