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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
1
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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Multi-objective optimization of reservoir refill operate considering energy storage and hydrological conditions 认领 引用
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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 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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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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Multi-material topology optimization for dual-alloy turbine disk considering accurate stress prediction and control 认领 引用 被引量：1
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作者 Cheng Yan Haowei Guo +3 位作者 Ce Liu Dong Mi He Liu Yancheng You 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2026年第3期215-227,共13页
The dual alloy turbine disk can fully leverage the advantages of dissimilar materials and has broad application prospects.Multi-material topology optimization(MMTO)provides possibilities for its innovative design.Howe... The dual alloy turbine disk can fully leverage the advantages of dissimilar materials and has broad application prospects.Multi-material topology optimization(MMTO)provides possibilities for its innovative design.However,the coupling between multi-material design variables and centrifugal loads poses challenges,in-cluding insufficient stress prediction accuracy,ineffective stress control,and difficulties in optimization con-vergence.Therefore,a new MMTO method that considers accurate stress prediction and control under cen-trifugal loads is proposed herein.The core ideas are as follows.(1)Introducing the multi-material predicted density to reduce the number of gray elements,proposing a multi-material transition factor for flexible se-lection of stiffness matrix interpolation,and thus developing an innovative accurate multi-material stress prediction method.(2)By modifying the normalized global stress method and utilizing a fixed step size to update the multi-material global stress relaxation coefficient,the maximum stress of the design domain is effectively controlled.(3)Deriving the stress sensitivity of axisymmetric problems under the complex coupling of cen-trifugal loads and multi-material design variables to improve the optimization convergence of stresses.Then MMTO for a turbine disk is conducted using two popular alloys(GH4169 and K418B).The results show that the proposed MMTO method effectively controls maximum stress,fully utilizes the advantages of both alloys and achieves a novel dual-alloy turbine disk structure. 展开更多
关键词 Dual—alloy turbine disk Multi—material topology optimization Stress prediction Stress control Centrifugal load
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LLM-Enhanced Multi-Agent Transfer Reinforcement Learning for Sensing,Communication,Computing,and Control Co-Optimization in Cyber-Physical Systems 认领 引用
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作者 Junyuan Zhang Chi Xu Haibin Yu 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第5期1251-1253,共3页
Dear Editor,With the rapid development of new-generation information and communication technology,the fusion of sensing,communication,computing,and control(S3C)is becoming increasingly significant for cyber-physical s... Dear Editor,With the rapid development of new-generation information and communication technology,the fusion of sensing,communication,computing,and control(S3C)is becoming increasingly significant for cyber-physical systems(CPS).However,due to the non-convexity,the curse of dimensionality,and the partial observability faced by CPS,traditional convex optimization algorithms are challenging to deal with S3C co-optimization. 展开更多
关键词 computing convex optimization algorithms LLM enhanced reinforcement communication sensing learning multi agent
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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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Salient Object Detection Based on Multi-Strategy Feature Optimization 认领 引用
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作者 Libo Han Sha Tao +3 位作者 Wen Xia Weixin Sun Li Yan Wanlin Gao 《Computers, Materials & Continua》 SCIE EI 2025年第2期2431-2449,共19页
At present, salient object detection (SOD) has achieved considerable progress. However, the methods that perform well still face the issue of inadequate detection accuracy. For example, sometimes there are problems of... At present, salient object detection (SOD) has achieved considerable progress. However, the methods that perform well still face the issue of inadequate detection accuracy. For example, sometimes there are problems of missed and false detections. Effectively optimizing features to capture key information and better integrating different levels of features to enhance their complementarity are two significant challenges in the domain of SOD. In response to these challenges, this study proposes a novel SOD method based on multi-strategy feature optimization. We propose the multi-size feature extraction module (MSFEM), which uses the attention mechanism, the multi-level feature fusion, and the residual block to obtain finer features. This module provides robust support for the subsequent accurate detection of the salient object. In addition, we use two rounds of feature fusion and the feedback mechanism to optimize the features obtained by the MSFEM to improve detection accuracy. The first round of feature fusion is applied to integrate the features extracted by the MSFEM to obtain more refined features. Subsequently, the feedback mechanism and the second round of feature fusion are applied to refine the features, thereby providing a stronger foundation for accurately detecting salient objects. To improve the fusion effect, we propose the feature enhancement module (FEM) and the feature optimization module (FOM). The FEM integrates the upper and lower features with the optimized features obtained by the FOM to enhance feature complementarity. The FOM uses different receptive fields, the attention mechanism, and the residual block to more effectively capture key information. Experimental results demonstrate that our method outperforms 10 state-of-the-art SOD methods. 展开更多
关键词 Salient object detection multi-strategy feature optimization feedback mechanism
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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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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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Multi-Objective Load Distribution Optimization for Hot Strip Mills 认领 引用 被引量：11
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作者 JIA Shu-jin LI Wei-gang +1 位作者 LIU Xiang-hua DU Bin 《Journal of Iron and Steel Research International》 SCIE CAS CSCD 2013年第2期27-32,61,共6页
Load distribution is a key technology in hot strip rolling process, which directly influences strip product quality. A multi-objective load distribution model, which takes into account the rolling force margin balance... Load distribution is a key technology in hot strip rolling process, which directly influences strip product quality. A multi-objective load distribution model, which takes into account the rolling force margin balance, roll wear ratio and strip shape control, is presented. To avoid the selection of weight coefficients encountered in single objective optimization, a multi-objective differential evolutionary algorithm, called MaximinDE, is proposed to solve this model. The experimental results based on practical production data indicate that MaximinDE can obtain a good pareto-optimal solution set, which consists of a series of alternative solutions to load distribution. Decision-makers can select a trade-off solution from the pareto-optimal solution set based on their experience or the importance of ob- iectives. In comparison with the empirical load distribution solution, the trade-off solution can achieve a better per- formance, which demonstrates the effectiveness of the multi-objective load distribution optimization. Moreover, the conflicting relationship among different objectives can be also found, which is another advantage of multi-objective load distribution optimization. 展开更多
关键词 hot strip mill load distribution multi-objective optimization
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Rotary unmanned aerial vehicles path planning in rough terrain based on multi-objective particle swarm optimization 认领 引用 被引量：29
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作者 XU Zhen ZHANG Enze CHEN Qingwei 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2020年第1期130-141,共12页
This paper presents a path planning approach for rotary unmanned aerial vehicles(R-UAVs)in a known static rough terrain environment.This approach aims to find collision-free and feasible paths with minimum altitude,le... This paper presents a path planning approach for rotary unmanned aerial vehicles(R-UAVs)in a known static rough terrain environment.This approach aims to find collision-free and feasible paths with minimum altitude,length and angle variable rate.First,a three-dimensional(3D)modeling method is proposed to reduce the computation burden of the dynamic models of R-UAVs.Considering the length,height and tuning angle of a path,the path planning of R-UAVs is described as a tri-objective optimization problem.Then,an improved multi-objective particle swarm optimization algorithm is developed.To render the algorithm more effective in dealing with this problem,a vibration function is introduced into the collided solutions to improve the algorithm efficiency.Meanwhile,the selection of the global best position is taken into account by the reference point method.Finally,the experimental environment is built with the help of the Google map and the 3D terrain generator World Machine.Experimental results under two different rough terrains from Guilin and Lanzhou of China demonstrate the capabilities of the proposed algorithm in finding Pareto optimal paths. 展开更多
关键词 unmanned aerial vehicle(UAV) path planning multiobjective optimization particle swarm optimization
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Multi-objective optimization of membrane structures based on Pareto Genetic Algorithm 认领 引用 被引量：9
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作者 伞冰冰 孙晓颖 武岳 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2010年第5期622-630,共9页
A multi-objective optimization method based on Pareto Genetic Algorithm is presented for shape design of membrane structures from a structural view point.Several non-dimensional variables are defined as optimization v... A multi-objective optimization method based on Pareto Genetic Algorithm is presented for shape design of membrane structures from a structural view point.Several non-dimensional variables are defined as optimization variables,which are decision factors of shapes of membrane structures.Three objectives are proposed including maximization of stiffness,maximum uniformity of stress and minimum reaction under external loads.Pareto Multi-objective Genetic Algorithm is introduced to solve the Pareto solutions.Consequently,the dependence of the optimality upon the optimization variables is derived to provide guidelines on how to determine design parameters.Moreover,several examples illustrate the proposed methods and applications.The study shows that the multi-objective optimization method in this paper is feasible and efficient for membrane structures;the research on Pareto solutions can provide explicit and useful guidelines for shape design of membrane structures. 展开更多
关键词 membrane structures multi-objective optimization Pareto solutions multi-objective genetic algorithm
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Aerodynamic multi-objective integrated optimization based on principal component analysis 认领 引用 被引量：17
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作者 Jiangtao HUANG Zhu ZHOU +2 位作者 Zhenghong GAO Miao ZHANG Lei YU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2017年第4期1336-1348,共13页
Based on improved multi-objective particle swarm optimization（MOPSO） algorithm with principal component analysis（PCA） methodology, an efficient high-dimension multiobjective optimization method is proposed, which,... Based on improved multi-objective particle swarm optimization（MOPSO） algorithm with principal component analysis（PCA） methodology, an efficient high-dimension multiobjective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design. The mathematical efficiency,the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil,and the proposed method is integrated into aircraft multi-disciplinary design（AMDEsign） platform, which contains aerodynamics, stealth and structure weight analysis and optimization module.Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft, in which the redundant objectives identified by PCA are transformed to optimization constraints, and several design methods are compared. The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached. The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem. 展开更多
关键词 Aerodynamic optimization Dimensional reduction Improved multi-objective particle swarm optimization（MOPSO） algorithm Multi-objective Principal component analysis
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Multi-objective Optimization Conceptual Design of Product Structure Based on Variable Length Gene Expression 认领 引用 被引量：7
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作者 WEI Xiaopeng ZHAO Tingting +2 位作者 JU Zhenhe ZHANG Shi LI Xiaoxiao 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2011年第1期42-49,共8页
It is a complicated problem for the bottom-to-top adaptive conceptual design of complicated products between structure and function. Reliable theories demand to be found in order to determine whether the structure acc... It is a complicated problem for the bottom-to-top adaptive conceptual design of complicated products between structure and function. Reliable theories demand to be found in order to determine whether the structure accords with the requirement of design. For the requirement generally is dynamic variety as time passes, new requirements will come, and some initial requirements can no longer be used. The number of product requirements, the gene length expressing requirements, the structure of the product, and the correlation matrix are varied with individuation of customer requirements of the product. By researching on the calculation mechanisms of dynamic variety, the approaches of gene expression and variable length gene expression are proposed. According to the diversity of structure selection in conceptual design and mutual relations between structure and function as well as structure and structure, the correlation matrixes between structure and function as well as structure and structure are defined. By the approach of making the sum of the elements of correlation matrix maximum, the mathematical models of multi-object optimization for structure design are provided based on variable requirements. An improved genetic algorithm called segment genetic algorithm is proposed based on optimization preservation simple genetic algorithm. The models of multi-object optimization are calculated by the segment genetic algorithm and hybrid genetic algorithm. An example for the conceptual design of a washing machine is given to show that the proposed method is able to realize the optimization structure design fitting for variable requirements. In addition, the proposed approach can provide good Pareto optimization solutions, and the individuation customer requirements for structures of products are able to be resolved effectively. 展开更多
关键词 gene expression multi-object optimization conceptual design genetic algorithm
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Multidisciplinary Design Optimization of Vehicle Instrument Panel Based on Multi-objective Genetic Algorithm 认领 引用 被引量：16
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作者 WANG Ping WU Guangqiang 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2013年第2期304-312,共9页
Typical multidisciplinary design optimization（MDO） has gradually been proposed to balance performances of lightweight, noise, vibration and harshness（NVH） and safety for instrument panel（IP） structure in the aut... Typical multidisciplinary design optimization（MDO） has gradually been proposed to balance performances of lightweight, noise, vibration and harshness（NVH） and safety for instrument panel（IP） structure in the automotive development. Nevertheless, plastic constitutive relation of Polypropylene（PP） under different strain rates, has not been taken into consideration in current reliability-based and collaborative IP MDO design. In this paper, based on tensile test under different strain rates, the constitutive relation of Polypropylene material is studied. Impact simulation tests for head and knee bolster are carried out to meet the regulation of FMVSS 201 and FMVSS 208, respectively. NVH analysis is performed to obtain mainly the natural frequencies and corresponding mode shapes, while the crashworthiness analysis is employed to examine the crash behavior of IP structure. With the consideration of lightweight, NVH, head and knee bolster impact performance, design of experiment（DOE）, response surface model（RSM）, and collaborative optimization（CO） are applied to realize the determined and reliability-based optimizations, respectively. Furthermore, based on multi-objective genetic algorithm（MOGA）, the optimal Pareto sets are completed to solve the multi-objective optimization（MOO） problem. The proposed research ensures the smoothness of Pareto set, enhances the ability of engineers to make a comprehensive decision about multi-objectives and choose the optimal design, and improves the quality and efficiency of MDO. 展开更多
关键词 instrument panel（IP） NVH safety multidisciplinary design optimization multi-objective optimization
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Multi-objective optimization of oil well drilling using elitist non-dominated sorting genetic algorithm 认领 引用 被引量：12
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作者 Chandan Guria Kiran K Goli Akhilendra K Pathak 《Petroleum Science》 SCIE CAS CSCD 2014年第1期97-110,共14页
A multi-objective optimization of oil well drilling has been carried out using a binary coded elitist non-dominated sorting genetic algorithm.A Louisiana offshore field with abnormal formation pressure is considered f... A multi-objective optimization of oil well drilling has been carried out using a binary coded elitist non-dominated sorting genetic algorithm.A Louisiana offshore field with abnormal formation pressure is considered for optimization.Several multi-objective optimization problems involving twoand three-objective functions were formulated and solved to fix optimal drilling variables.The important objectives are：（i） maximizing drilling depth,（ii） minimizing drilling time and （iii） minimizing drilling cost with fractional drill bit tooth wear as a constraint.Important time dependent decision variables are：（i） equivalent circulation mud density,（ii） drill bit rotation,（iii） weight on bit and （iv） Reynolds number function of circulating mud through drill bit nozzles.A set of non-dominated optimal Pareto frontier is obtained for the two-objective optimization problem whereas a non-dominated optimal Pareto surface is obtained for the three-objective optimization problem.Depending on the trade-offs involved,decision makers may select any point from the optimal Pareto frontier or optimal Pareto surface and hence corresponding values of the decision variables that may be selected for optimal drilling operation.For minimizing drilling time and drilling cost,the optimum values of the decision variables are needed to be kept at the higher values whereas the optimum values of decision variables are at the lower values for the maximization of drilling depth. 展开更多
关键词 Drilling performance rate of penetration abnormal pore pressure genetic algorithm multi-objective optimization
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Parametric Optimization Design of Aircraft Based on Hybrid Parallel Multi-objective Tabu Search Algorithm 认领 引用 被引量：13
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作者 邱志平 张宇星 《Chinese Journal of Aeronautics》 SCIE EI CAS 2010年第4期430-437,共8页
For dealing with the multi-objective optimization problems of parametric design for aircraft, a novel hybrid parallel multi-objective tabu search （HPMOTS） algorithm is used. First, a new multi-objective tabu search ... For dealing with the multi-objective optimization problems of parametric design for aircraft, a novel hybrid parallel multi-objective tabu search （HPMOTS） algorithm is used. First, a new multi-objective tabu search （MOTS） algorithm is proposed. Comparing with the traditional MOTS algorithm, this proposed algorithm adds some new methods such as the combination of MOTS algorithm and ＂Pareto solution＂, the strategy of ＂searching from many directions＂ and the reservation of good solutions. Second, this article also proposes the improved parallel multi-objective tabu search （PMOTS） algorithm. Finally, a new hybrid algorithm--HPMOTS algorithm which combines the PMOTS algorithm with the non-dominated sorting-based multi-objective genetic algorithm （NSGA） is presented. The computing results of these algorithms are compared with each other and it is shown that the optimal result can be obtained by the HPMOTS algorithm and the computing result of the PMOTS algorithm is better than that of MOTS algorithm. 展开更多
关键词 aircraft design conceptual design multi-objective optimization tabu search genetic algorithm Pareto optimal
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