期刊文献+
共找到104,193篇文章
< 1 2 250 >
每页显示 20 50 100
一种改进Oriented RepPoints的遥感图像有向目标检测
1
作者 谢国波 张家源 +1 位作者 林志毅 廖文康 《小型微型计算机系统》 CSCD 北大核心 2024年第11期2725-2731,共7页
为解决Oriented RepPoints算法在遥感图像有向目标检测中因遥感图像背景干扰信息较多和目标尺度大小不一所导致的检测精度不高、易漏检误检等问题,提出一种改进Oriented RepPoints的遥感图像有向目标检测方法MA-RPDet(Mixed Attention R... 为解决Oriented RepPoints算法在遥感图像有向目标检测中因遥感图像背景干扰信息较多和目标尺度大小不一所导致的检测精度不高、易漏检误检等问题,提出一种改进Oriented RepPoints的遥感图像有向目标检测方法MA-RPDet(Mixed Attention RepPoints Detector).首先,采用了PVTv2作为主干网络,该网络利用线性空间缩减自注意力机制提取出更具局部连续性的特征图,并保持与卷积运算类似的线性复杂度.其次,在特征融合阶段设计了串联性混合注意力模块,进一步强化了重要特征,促进了多尺度特征的高效交互.最后,引入平滑GIoU损失函数对模型学习策略进行优化,提高了检测精度.在两个遥感图像目标检测数据集DOTA和HRSC2016上的实验结果表明,所提方法的检测精度mAP分别达到了77.19%和90.3%,均高于其他对比算法,证明了本文方法的有效性. 展开更多
关键词 遥感图像 有向目标检测 oriented RepPoints 注意力机制 损失函数
下载PDF
基于改进Oriented R-CNN的旋转框麦穗检测与计数模型 被引量:1
2
作者 于俊伟 陈威威 +2 位作者 郭园森 母亚双 樊超 《农业工程学报》 EI CAS CSCD 北大核心 2024年第6期248-257,共10页
为对干扰、遮挡等复杂的田野环境中麦穗进行精准定位与计数,该研究提出了一种改进的Oriented R-CNN麦穗旋转框检测与计数方法,首先在主干网络中引入跨阶段局部空间金字塔(spatial pyramid pooling cross stage partial networks,SPPCSPC... 为对干扰、遮挡等复杂的田野环境中麦穗进行精准定位与计数,该研究提出了一种改进的Oriented R-CNN麦穗旋转框检测与计数方法,首先在主干网络中引入跨阶段局部空间金字塔(spatial pyramid pooling cross stage partial networks,SPPCSPC)模块扩大模型感受野,增强网络感知能力;其次,在颈网络中结合路径聚合网络(PANet,path aggregation network)和混合注意力机制(E2CBAM,efficient two convolutional block attention module),丰富特征图包含的特征信息;最后采用柔性非极大值抑制算法(Soft-NMS,soft-non maximum suppression)优化预测框筛选过程。试验结果显示,改进的模型对复杂环境中的麦穗检测效果良好。相较原模型,平均精确度均值mAP提高了2.02个百分点,与主流的旋转目标检测模型Gliding vertex、R3det、Rotated Faster R-CNN、S2anet和Rotated Retinanet相比,mAP分别提高了4.99、2.49、3.94、2.25和4.12个百分点。该研究方法利用旋转框准确定位麦穗位置,使得框内背景区域面积大幅度减少,为实际观察麦穗生长状况和统计数量提供了一种有效的方法。 展开更多
关键词 图像识别 作物 注意力机制 麦穗 oriented R-CNN
下载PDF
Multi-Strategy Assisted Multi-Objective Whale Optimization Algorithm for Feature Selection 被引量:1
3
作者 Deng Yang Chong Zhou +2 位作者 Xuemeng Wei Zhikun Chen Zheng Zhang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第8期1563-1593,共31页
In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature sel... In classification problems,datasets often contain a large amount of features,but not all of them are relevant for accurate classification.In fact,irrelevant features may even hinder classification accuracy.Feature selection aims to alleviate this issue by minimizing the number of features in the subset while simultaneously minimizing the classification error rate.Single-objective optimization approaches employ an evaluation function designed as an aggregate function with a parameter,but the results obtained depend on the value of the parameter.To eliminate this parameter’s influence,the problem can be reformulated as a multi-objective optimization problem.The Whale Optimization Algorithm(WOA)is widely used in optimization problems because of its simplicity and easy implementation.In this paper,we propose a multi-strategy assisted multi-objective WOA(MSMOWOA)to address feature selection.To enhance the algorithm’s search ability,we integrate multiple strategies such as Levy flight,Grey Wolf Optimizer,and adaptive mutation into it.Additionally,we utilize an external repository to store non-dominant solution sets and grid technology is used to maintain diversity.Results on fourteen University of California Irvine(UCI)datasets demonstrate that our proposed method effectively removes redundant features and improves classification performance.The source code can be accessed from the website:https://github.com/zc0315/MSMOWOA. 展开更多
关键词 multi-objective optimization whale optimization algorithm multi-strategy feature selection
下载PDF
Multi-objective optimization and evaluation of supercritical CO_(2) Brayton cycle for nuclear power generation 被引量:1
4
作者 Guo-Peng Yu Yong-Feng Cheng +1 位作者 Na Zhang Ping-Jian Ming 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2024年第2期183-209,共27页
The supercritical CO_(2) Brayton cycle is considered a promising energy conversion system for Generation IV reactors for its simple layout,compact structure,and high cycle efficiency.Mathematical models of four Brayto... The supercritical CO_(2) Brayton cycle is considered a promising energy conversion system for Generation IV reactors for its simple layout,compact structure,and high cycle efficiency.Mathematical models of four Brayton cycle layouts are developed in this study for different reactors to reduce the cost and increase the thermohydraulic performance of nuclear power generation to promote the commercialization of nuclear energy.Parametric analysis,multi-objective optimizations,and four decision-making methods are applied to obtain each Brayton scheme’s optimal thermohydraulic and economic indexes.Results show that for the same design thermal power scale of reactors,the higher the core’s exit temperature,the better the Brayton cycle’s thermo-economic performance.Among the four-cycle layouts,the recompression cycle(RC)has the best overall performance,followed by the simple recuperation cycle(SR)and the intercooling cycle(IC),and the worst is the reheating cycle(RH).However,RH has the lowest total cost of investment(C_(tot))of$1619.85 million,and IC has the lowest levelized cost of energy(LCOE)of 0.012$/(kWh).The nuclear Brayton cycle system’s overall performance has been improved due to optimization.The performance of the molten salt reactor combined with the intercooling cycle(MSR-IC)scheme has the greatest improvement,with the net output power(W_(net)),thermal efficiencyη_(t),and exergy efficiency(η_(e))improved by 8.58%,8.58%,and 11.21%,respectively.The performance of the lead-cooled fast reactor combined with the simple recuperation cycle scheme was optimized to increase C_(tot) by 27.78%.In comparison,the internal rate of return(IRR)increased by only 7.8%,which is not friendly to investors with limited funds.For the nuclear Brayton cycle,the molten salt reactor combined with the recompression cycle scheme should receive priority,and the gas-cooled fast reactor combined with the reheating cycle scheme should be considered carefully. 展开更多
关键词 Supercritical CO_(2)Brayton cycle Nuclear power generation Thermo-economic analysis multi-objective optimization Decision-making methods
下载PDF
A modified back analysis method for deep excavation with multi-objective optimization procedure
5
作者 Chenyang Zhao Le Chen +2 位作者 Pengpeng Ni Wenjun Xia Bin Wang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2024年第4期1373-1387,共15页
Real-time prediction of excavation-induced displacement of retaining pile during the deep excavation process is crucial for construction safety.This paper proposes a modified back analysis method with multi-objective ... Real-time prediction of excavation-induced displacement of retaining pile during the deep excavation process is crucial for construction safety.This paper proposes a modified back analysis method with multi-objective optimization procedure,which enables a real-time prediction of horizontal displacement of retaining pile during construction.As opposed to the traditional stage-by-stage back analysis,time series monitoring data till the current excavation stage are utilized to form a multi-objective function.Then,the multi-objective particle swarm optimization (MOPSO) algorithm is applied for parameter identification.The optimized model parameters are immediately adopted to predict the excavation-induced pile deformation in the continuous construction stages.To achieve efficient parameter optimization and real-time prediction of system behavior,the back propagation neural network (BPNN) is established to substitute the finite element model,which is further implemented together with MOPSO for automatic operation.The proposed approach is applied in the Taihu tunnel excavation project,where the effectiveness of the method is demonstrated via the comparisons with the site monitoring data.The method is reliable with a prediction accuracy of more than 90%.Moreover,different optimization algorithms,including non-dominated sorting genetic algorithm (NSGA-II),Pareto Envelope-based Selection Algorithm II (PESA-II) and MOPSO,are compared,and their influences on the prediction accuracy at different excavation stages are studied.The results show that MOPSO has the best performance for high dimensional optimization task. 展开更多
关键词 multi-objective optimization Back analysis Surrogate model multi-objective particle swarm optimization(MOPSO) Deep excavation
下载PDF
A reduced combustion mechanism of ammonia/diesel optimized with multi-objective genetic algorithm
6
作者 Wanchen Sun Shaodian Lin +4 位作者 Hao Zhang Liang Guo Wenpeng Zeng Genan Zhu Mengqi Jiang 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2024年第4期187-200,共14页
For the deep understanding on combustion of ammonia/diesel,this study develops a reduced mechanism of ammonia/diesel with 227 species and 937 reactions.The sub-mechanism on ammonia/interactions of N-based and C-based ... For the deep understanding on combustion of ammonia/diesel,this study develops a reduced mechanism of ammonia/diesel with 227 species and 937 reactions.The sub-mechanism on ammonia/interactions of N-based and C-based species(N—C)/NOx is optimized using the Non-dominated Sorting Genetic Algorithm II(NSGA-II)with 200 generations.The optimized mechanism(named as 937b)is validated against combustion characteristics of ammonia/methane(which is used to examine the accuracy of N—C interactions)and ammonia/diesel blends.The ignition delay times(IDTs),the laminar flame speeds and most of key intermediate species during the combustion of ammonia/methane blends can be accurately simulated by 937b under a wide range of conditions.As for ammonia/diesel blends with various diesel energy fractions,reasonable predictions on the IDTs under pressures from 1.0 MPa to5.0 MPa as well as the laminar flame speeds are also achieved by 937b.In particular,with regard to the IDT simulations of ammonia/diesel blends,937b makes progress in both aspects of overall accuracy and computational efficiency,compared to a detailed ammonia/diesel mechanism.Further kinetic analysis reveals that the reaction pathway of ammonia during the combustion of ammonia/diesel blend mainly differs in the tendencies of oxygen additions to NH_2 and NH with different equivalence ratios. 展开更多
关键词 AMMONIA DIESEL COMBUSTION Kinetic mechanism multi-objective optimization
下载PDF
Multi-objective global optimization approach predicted quasi-layered ternary TiOS crystals with promising photocatalytic properties
7
作者 向依婕 高思妍 +4 位作者 王春雷 方海平 段香梅 郑益峰 张越宇 《Chinese Physics B》 SCIE EI CAS CSCD 2024年第8期429-435,共7页
Titanium dioxide(TiO_(2))has attracted considerable research attentions for its promising applications in solar cells and photocatalytic devices.However,the intrinsic challenge lies in the relatively low energy conver... Titanium dioxide(TiO_(2))has attracted considerable research attentions for its promising applications in solar cells and photocatalytic devices.However,the intrinsic challenge lies in the relatively low energy conversion efficiency of TiO_(2),primarily attributed to the substantial band gaps(exceeding 3.0 eV)associated with its rutile and anatase phases.Leveraging multi-objective global optimization,we have identified two quasi-layered ternary Ti-O-S crystals,composed of titanium,oxygen,and sulfur.The calculations of formation energy,phonon dispersions,and thermal stability confirm the chemical,dynamical and thermal stability of these newly discovered phases.Employing the state-of-art hybrid density functional approach and many-body perturbation theory(quasiparticle GW approach and Bethe-Salpeter equation),we calculate the optical properties of both the TiOS phases.Significantly,both phases show favorable photocatalytic characteristics,featuring band gaps suitable for visible optical absorption and appropriate band alignments with water for effective charge carrier separation.Therefore,ternary compound TiOS holds the potential for achieving high-efficiency photochemical conversion,showing our multi-objective global optimization provides a new approach for novel environmental and energy materials design with multicomponent compounds. 展开更多
关键词 PHOTOCATALYSIS first principles calculations multi-objective global optimization
下载PDF
DeepSurNet-NSGA II:Deep Surrogate Model-Assisted Multi-Objective Evolutionary Algorithm for Enhancing Leg Linkage in Walking Robots
8
作者 Sayat Ibrayev Batyrkhan Omarov +1 位作者 Arman Ibrayeva Zeinel Momynkulov 《Computers, Materials & Continua》 SCIE EI 2024年第10期229-249,共21页
This research paper presents a comprehensive investigation into the effectiveness of the DeepSurNet-NSGA II(Deep Surrogate Model-Assisted Non-dominated Sorting Genetic Algorithm II)for solving complex multiobjective o... This research paper presents a comprehensive investigation into the effectiveness of the DeepSurNet-NSGA II(Deep Surrogate Model-Assisted Non-dominated Sorting Genetic Algorithm II)for solving complex multiobjective optimization problems,with a particular focus on robotic leg-linkage design.The study introduces an innovative approach that integrates deep learning-based surrogate models with the robust Non-dominated Sorting Genetic Algorithm II,aiming to enhance the efficiency and precision of the optimization process.Through a series of empirical experiments and algorithmic analyses,the paper demonstrates a high degree of correlation between solutions generated by the DeepSurNet-NSGA II and those obtained from direct experimental methods,underscoring the algorithm’s capability to accurately approximate the Pareto-optimal frontier while significantly reducing computational demands.The methodology encompasses a detailed exploration of the algorithm’s configuration,the experimental setup,and the criteria for performance evaluation,ensuring the reproducibility of results and facilitating future advancements in the field.The findings of this study not only confirm the practical applicability and theoretical soundness of the DeepSurNet-NSGA II in navigating the intricacies of multi-objective optimization but also highlight its potential as a transformative tool in engineering and design optimization.By bridging the gap between complex optimization challenges and achievable solutions,this research contributes valuable insights into the optimization domain,offering a promising direction for future inquiries and technological innovations. 展开更多
关键词 multi-objective optimization genetic algorithm surrogate model deep learning walking robots
下载PDF
Three-dimensionally oriented organization of hexagonal MIL-96 microplates toward superior film microstructure
9
作者 Sixing Chen Xinmiao Jin +3 位作者 Yuyang Wu Taotao Ji Fei Wang Yi Liu 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2024年第8期69-73,共5页
Preferential orientation control of metal—organic framework(MOF)films is advantageous for maximizing pore uniformity and minimizing grain-boundary defects.Nonetheless,the preparation of MOF films with both in-plane a... Preferential orientation control of metal—organic framework(MOF)films is advantageous for maximizing pore uniformity and minimizing grain-boundary defects.Nonetheless,the preparation of MOF films with both in-plane and out-of-plane orientations remains a grand challenge.In this study,we reported the preparation of three-dimensionally oriented MIL-96 layers through combining morphology control of MIL-96 seeds with addition of polyvinylpyrrolidone surfactants and arachidonic acids.The three-dimensionally oriented MIL-96 film was readily obtained through in-plane epitaxial growth.It is anticipated that the aforementioned protocol can be effective for obtaining diverse MOF films with a three-dimensionally oriented organization. 展开更多
关键词 Metaleorganic framework FILM orientation Epitaxial growth Morphological control
下载PDF
Crashworthiness Design and Multi-Objective Optimization of Bionic Thin-Walled Hybrid Tube Structures
10
作者 Pingfan Li Jiumei Xiao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第4期999-1016,共18页
Thin-walled structures are widely used in cars due to their lightweight construction and energy-absorbing properties.However,issues such as high initial stress and lowenergy-absorbing efficiency arise.This study propo... Thin-walled structures are widely used in cars due to their lightweight construction and energy-absorbing properties.However,issues such as high initial stress and lowenergy-absorbing efficiency arise.This study proposes a novel energy-absorbing structure inwhich a straight tube is combinedwith a conical tube and a bamboo-inspired bulkhead structure is introduced.This configuration allows the conical tube to flip outward first and then fold together with the straight tube.This deformation mode absorbs more energy and less peak force than the conical tube sinking and flipping inward.Through finite element numerical simulation,the specific energy absorption capacity of the structure is increased by 26%compared to that of a regular circular cross-section tube.Finally,the impact resistance of the bionic straight tapered tube structure is further improved through multi-objective optimization,promoting the engineering application and lightweight design of hybrid cross-section tubes. 展开更多
关键词 CRASHWORTHINESS tube inversion multi-objective optimization energy absorption
下载PDF
Multi-objective Design of Blending Fuel by Intelligent Optimization Algorithms
11
作者 Ruichen Liu Cong Li +2 位作者 Li Wang Xiangwen Zhang Guozhu Li 《Transactions of Tianjin University》 EI CAS 2024年第3期221-237,共17页
Fuel design is a complex multi-objective optimization problem in which facile and robust methods are urgently demanded.Herein,a complete workflow for designing a fuel blending scheme is presented,which is theoreticall... Fuel design is a complex multi-objective optimization problem in which facile and robust methods are urgently demanded.Herein,a complete workflow for designing a fuel blending scheme is presented,which is theoretically supported,efficient,and reliable.Based on the data distribution of the composition and properties of the blending fuels,a model of polynomial regression with appropriate hypothesis space was established.The parameters of the model were further optimized by different intelligence algorithms to achieve high-precision regression.Then,the design of a blending fuel was described as a multi-objective optimization problem,which was solved using a Nelder–Mead algorithm based on the concept of Pareto domination.Finally,the design of a target fuel was fully validated by experiments.This study provides new avenues for designing various blending fuels to meet the needs of next-generation engines. 展开更多
关键词 multi-objective optimization Machine learning Blending fuel
下载PDF
MOALG: A Metaheuristic Hybrid of Multi-Objective Ant Lion Optimizer and Genetic Algorithm for Solving Design Problems
12
作者 Rashmi Sharma Ashok Pal +4 位作者 Nitin Mittal Lalit Kumar Sreypov Van Yunyoung Nam Mohamed Abouhawwash 《Computers, Materials & Continua》 SCIE EI 2024年第3期3489-3510,共22页
This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm(MOALO)which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm(ALO)and the Genetic ... This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm(MOALO)which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm(ALO)and the Genetic Algorithm(GA).MOALO version has been employed to address those problems containing many objectives and an archive has been employed for retaining the non-dominated solutions.The uniqueness of the hybrid is that the operators like mutation and crossover of GA are employed in the archive to update the solutions and later those solutions go through the process of MOALO.A first-time hybrid of these algorithms is employed to solve multi-objective problems.The hybrid algorithm overcomes the limitation of ALO of getting caught in the local optimum and the requirement of more computational effort to converge GA.To evaluate the hybridized algorithm’s performance,a set of constrained,unconstrained test problems and engineering design problems were employed and compared with five well-known computational algorithms-MOALO,Multi-objective Crystal Structure Algorithm(MOCryStAl),Multi-objective Particle Swarm Optimization(MOPSO),Multi-objective Multiverse Optimization Algorithm(MOMVO),Multi-objective Salp Swarm Algorithm(MSSA).The outcomes of five performance metrics are statistically analyzed and the most efficient Pareto fronts comparison has been obtained.The proposed hybrid surpasses MOALO based on the results of hypervolume(HV),Spread,and Spacing.So primary objective of developing this hybrid approach has been achieved successfully.The proposed approach demonstrates superior performance on the test functions,showcasing robust convergence and comprehensive coverage that surpasses other existing algorithms. 展开更多
关键词 multi-objective optimization genetic algorithm ant lion optimizer METAHEURISTIC
下载PDF
A Prediction-Based Multi-Objective VM Consolidation Approach for Cloud Data Centers
13
作者 Xialin Liu Junsheng Wu +1 位作者 Lijun Chen Jiyuan Hu 《Computers, Materials & Continua》 SCIE EI 2024年第7期1601-1631,共31页
Virtual machine(VM)consolidation aims to run VMs on the least number of physical machines(PMs).The optimal consolidation significantly reduces energy consumption(EC),quality of service(QoS)in applications,and resource... Virtual machine(VM)consolidation aims to run VMs on the least number of physical machines(PMs).The optimal consolidation significantly reduces energy consumption(EC),quality of service(QoS)in applications,and resource utilization.This paper proposes a prediction-basedmulti-objective VMconsolidation approach to search for the best mapping between VMs and PMs with good timeliness and practical value.We use a hybrid model based on Auto-Regressive Integrated Moving Average(ARIMA)and Support Vector Regression(SVR)(HPAS)as a prediction model and consolidate VMs to PMs based on prediction results by HPAS,aiming at minimizing the total EC,performance degradation(PD),migration cost(MC)and resource wastage(RW)simultaneously.Experimental results usingMicrosoft Azure trace show the proposed approach has better prediction accuracy and overcomes the multi-objective consolidation approach without prediction(i.e.,Non-dominated sorting genetic algorithm 2,Nsga2)and the renowned Overload Host Detection(OHD)approaches without prediction,such as Linear Regression(LR),Median Absolute Deviation(MAD)and Inter-Quartile Range(IQR). 展开更多
关键词 VM consolidation PREDICTION multi-objective optimization machine learning
下载PDF
Prediction Model-based Multi-objective Optimization for Mix-ratio Design of Recycled Aggregate Concrete
14
作者 CHEN Tao WU Di YAO Xiaojun 《Journal of Wuhan University of Technology(Materials Science)》 SCIE EI CAS CSCD 2024年第6期1507-1517,共11页
The prediction model for mechanical properties of RAC was established through the Bayesian optimization-based Gaussian process regression(BO-GPR)method,where the input variables in BO-GPR model depend on the mix ratio... The prediction model for mechanical properties of RAC was established through the Bayesian optimization-based Gaussian process regression(BO-GPR)method,where the input variables in BO-GPR model depend on the mix ratio of concrete.Then the compressive strength prediction model,the material cost,and environmental factors were simultaneously considered as objectives,while a multi-objective gray wolf optimization algorithm was developed for finding the optimal mix ratio.A total of 730 RAC datasets were used for training and testing the predication model,while the optimal design method for mix ratio was verified through RAC experiments.The experimental results show that the predicted,testing,and expected compressive strengths are nearly consistent,illustrating the effectiveness of the proposed method. 展开更多
关键词 recycled coarse aggregate mix ratio multi-objective optimization prediction model compressive strength
下载PDF
Multi-Objective Equilibrium Optimizer for Feature Selection in High-Dimensional English Speech Emotion Recognition
15
作者 Liya Yue Pei Hu +1 位作者 Shu-Chuan Chu Jeng-Shyang Pan 《Computers, Materials & Continua》 SCIE EI 2024年第2期1957-1975,共19页
Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by emotions.The number of features acquired with acoustic analysis is ext... Speech emotion recognition(SER)uses acoustic analysis to find features for emotion recognition and examines variations in voice that are caused by emotions.The number of features acquired with acoustic analysis is extremely high,so we introduce a hybrid filter-wrapper feature selection algorithm based on an improved equilibrium optimizer for constructing an emotion recognition system.The proposed algorithm implements multi-objective emotion recognition with the minimum number of selected features and maximum accuracy.First,we use the information gain and Fisher Score to sort the features extracted from signals.Then,we employ a multi-objective ranking method to evaluate these features and assign different importance to them.Features with high rankings have a large probability of being selected.Finally,we propose a repair strategy to address the problem of duplicate solutions in multi-objective feature selection,which can improve the diversity of solutions and avoid falling into local traps.Using random forest and K-nearest neighbor classifiers,four English speech emotion datasets are employed to test the proposed algorithm(MBEO)as well as other multi-objective emotion identification techniques.The results illustrate that it performs well in inverted generational distance,hypervolume,Pareto solutions,and execution time,and MBEO is appropriate for high-dimensional English SER. 展开更多
关键词 Speech emotion recognition filter-wrapper HIGH-DIMENSIONAL feature selection equilibrium optimizer multi-objective
下载PDF
Even Search in a Promising Region for Constrained Multi-Objective Optimization
16
作者 Fei Ming Wenyin Gong Yaochu Jin 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第2期474-486,共13页
In recent years, a large number of approaches to constrained multi-objective optimization problems(CMOPs) have been proposed, focusing on developing tweaked strategies and techniques for handling constraints. However,... In recent years, a large number of approaches to constrained multi-objective optimization problems(CMOPs) have been proposed, focusing on developing tweaked strategies and techniques for handling constraints. However, an overly finetuned strategy or technique might overfit some problem types,resulting in a lack of versatility. In this article, we propose a generic search strategy that performs an even search in a promising region. The promising region, determined by obtained feasible non-dominated solutions, possesses two general properties.First, the constrained Pareto front(CPF) is included in the promising region. Second, as the number of feasible solutions increases or the convergence performance(i.e., approximation to the CPF) of these solutions improves, the promising region shrinks. Then we develop a new strategy named even search,which utilizes the non-dominated solutions to accelerate convergence and escape from local optima, and the feasible solutions under a constraint relaxation condition to exploit and detect feasible regions. Finally, a diversity measure is adopted to make sure that the individuals in the population evenly cover the valuable areas in the promising region. Experimental results on 45 instances from four benchmark test suites and 14 real-world CMOPs have demonstrated that searching evenly in the promising region can achieve competitive performance and excellent versatility compared to 11 most state-of-the-art methods tailored for CMOPs. 展开更多
关键词 Constrained multi-objective optimization even search evolutionary algorithms promising region real-world problems
下载PDF
An Immune-Inspired Approach with Interval Allocation in Solving Multimodal Multi-Objective Optimization Problems with Local Pareto Sets
17
作者 Weiwei Zhang Jiaqiang Li +2 位作者 Chao Wang Meng Li Zhi Rao 《Computers, Materials & Continua》 SCIE EI 2024年第6期4237-4257,共21页
In practical engineering,multi-objective optimization often encounters situations where multiple Pareto sets(PS)in the decision space correspond to the same Pareto front(PF)in the objective space,known as Multi-Modal ... In practical engineering,multi-objective optimization often encounters situations where multiple Pareto sets(PS)in the decision space correspond to the same Pareto front(PF)in the objective space,known as Multi-Modal Multi-Objective Optimization Problems(MMOP).Locating multiple equivalent global PSs poses a significant challenge in real-world applications,especially considering the existence of local PSs.Effectively identifying and locating both global and local PSs is a major challenge.To tackle this issue,we introduce an immune-inspired reproduction strategy designed to produce more offspring in less crowded,promising regions and regulate the number of offspring in areas that have been thoroughly explored.This approach achieves a balanced trade-off between exploration and exploitation.Furthermore,we present an interval allocation strategy that adaptively assigns fitness levels to each antibody.This strategy ensures a broader survival margin for solutions in their initial stages and progressively amplifies the differences in individual fitness values as the population matures,thus fostering better population convergence.Additionally,we incorporate a multi-population mechanism that precisely manages each subpopulation through the interval allocation strategy,ensuring the preservation of both global and local PSs.Experimental results on 21 test problems,encompassing both global and local PSs,are compared with eight state-of-the-art multimodal multi-objective optimization algorithms.The results demonstrate the effectiveness of our proposed algorithm in simultaneously identifying global Pareto sets and locally high-quality PSs. 展开更多
关键词 Multimodal multi-objective optimization problem local PSs immune-inspired reproduction
下载PDF
Multi-Objective Optimization of Aluminum Alloy Electric Bus Frame Connectors for Enhanced Durability
18
作者 Wenjun Zhou Mingzhi Yang +3 位作者 Qian Peng Yong Peng Kui Wang Qiang Xiao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第7期735-755,共21页
The widespread adoption of aluminumalloy electric buses,known for their energy efficiency and eco-friendliness,faces a challenge due to the aluminum frame’s susceptibility to deformation compared to steel.This issue ... The widespread adoption of aluminumalloy electric buses,known for their energy efficiency and eco-friendliness,faces a challenge due to the aluminum frame’s susceptibility to deformation compared to steel.This issue is further exacerbated by the stringent requirements imposed by the flammability and explosiveness of batteries,necessitating robust frame protection.Our study aims to optimize the connectors of aluminum alloy bus frames,emphasizing durability,energy efficiency,and safety.This research delves into Multi-Objective Coordinated Optimization(MCO)techniques for lightweight design in aluminum alloy bus body connectors.Our goal is to enhance lightweighting,reinforce energy absorption,and improve deformation resistance in connector components.Three typical aluminum alloy connectors were selected and a design optimization platform was built for their MCO using a variety of software and methods.Firstly,through three-point bending experiments and finite element analysis on three types of connector components,we identified optimized design parameters based on deformation patterns.Then,employing Optimal Latin hypercube design(OLHD),parametric modeling,and neural network approximation,we developed high-precision approximate models for the design parameters of each connector component,targeting energy absorption,mass,and logarithmic strain.Lastly,utilizing the Archive-based Micro Genetic Algorithm(AMGA),Multi-Objective Particle Swarm Optimization(MOPSO),and Non-dominated SortingGenetic Algorithm(NSGA2),we explored optimized design solutions for these joint components.Subsequently,we simulated joint assembly buckling during bus rollover crash scenarios to verify and analyze the optimized solutions in three-point bending simulations.Each joint component showcased a remarkable 30%–40%mass reduction while boosting energy absorption.Our design optimization method exhibits high efficiency and costeffectiveness.Leveraging contemporary automation technology,the design optimization platform developed in this study is poised to facilitate intelligent optimization of lightweight metal components in future applications. 展开更多
关键词 Aluminum connectors three-point bending simulation parametric design model multi-objective collaborative optimization
下载PDF
Multi-Objective Optimization of VBHF in Deep Drawing Based on the Improved QO-Jaya Algorithm
19
作者 Xiangyu Jiang Zhaoxi Hong +1 位作者 Yixiong Feng Jianrong Tan 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2024年第1期189-202,共14页
Blank holder force(BHF)is a crucial parameter in deep drawing,having close relation with the forming quality of sheet metal.However,there are different BHFs maintaining the best forming effect in different stages of d... Blank holder force(BHF)is a crucial parameter in deep drawing,having close relation with the forming quality of sheet metal.However,there are different BHFs maintaining the best forming effect in different stages of deep drawing.The variable blank holder force(VBHF)varying with the drawing stage can overcome this problem at an extent.The optimization of VBHF is to determine the optimal BHF in every deep drawing stage.In this paper,a new heuristic optimization algorithm named Jaya is introduced to solve the optimization efficiently.An improved“Quasi-oppositional”strategy is added to Jaya algorithm for improving population diversity.Meanwhile,an innovated stop criterion is added for better convergence.Firstly,the quality evaluation criteria for wrinkling and tearing are built.Secondly,the Kriging models are developed to approximate and quantify the relation between VBHF and forming defects under random sampling.Finally,the optimization models are established and solved by the improved QO-Jaya algorithm.A VBHF optimization example of component with complicated shape and thin wall is studied to prove the effectiveness of the improved Jaya algorithm.The optimization results are compared with that obtained by other algorithms based on the TOPSIS method. 展开更多
关键词 Variable blank holder force multi-objective optimization QO-Jaya algorithm Algorithm stop criterion
下载PDF
Constraints Separation Based Evolutionary Multitasking for Constrained Multi-Objective Optimization Problems
20
作者 Kangjia Qiao Jing Liang +4 位作者 Kunjie Yu Xuanxuan Ban Caitong Yue Boyang Qu Ponnuthurai Nagaratnam Suganthan 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第8期1819-1835,共17页
Constrained multi-objective optimization problems(CMOPs)generally contain multiple constraints,which not only form multiple discrete feasible regions but also reduce the size of optimal feasible regions,thus they prop... Constrained multi-objective optimization problems(CMOPs)generally contain multiple constraints,which not only form multiple discrete feasible regions but also reduce the size of optimal feasible regions,thus they propose serious challenges for solvers.Among all constraints,some constraints are highly correlated with optimal feasible regions;thus they can provide effective help to find feasible Pareto front.However,most of the existing constrained multi-objective evolutionary algorithms tackle constraints by regarding all constraints as a whole or directly ignoring all constraints,and do not consider judging the relations among constraints and do not utilize the information from promising single constraints.Therefore,this paper attempts to identify promising single constraints and utilize them to help solve CMOPs.To be specific,a CMOP is transformed into a multitasking optimization problem,where multiple auxiliary tasks are created to search for the Pareto fronts that only consider a single constraint respectively.Besides,an auxiliary task priority method is designed to identify and retain some high-related auxiliary tasks according to the information of relative positions and dominance relationships.Moreover,an improved tentative method is designed to find and transfer useful knowledge among tasks.Experimental results on three benchmark test suites and 11 realworld problems with different numbers of constraints show better or competitive performance of the proposed method when compared with eight state-of-the-art peer methods. 展开更多
关键词 Constrained multi-objective optimization(CMOPs) evolutionary multitasking knowledge transfer single constraint.
下载PDF
上一页 1 2 250 下一页 到第
使用帮助 返回顶部