期刊文献+
共找到223,941篇文章
< 1 2 250 >
每页显示 20 50 100
光滑双向渐进结构优化法拓扑优化连续体结构频率和动刚度 预览
1
作者 滕晓艳 毛炳坤 江旭东 《农业工程学报》 EI CAS CSCD 北大核心 2019年第7期55-61,共7页
针对双向渐进结构优化(bi-directional evolutionary structural optimization,BESO)方法的单元过删除问题,提出了光滑双向渐进结构优化(smooth bi-directional evolutionary structural optimization,SBESO)方法,通过引入权重函数更新... 针对双向渐进结构优化(bi-directional evolutionary structural optimization,BESO)方法的单元过删除问题,提出了光滑双向渐进结构优化(smooth bi-directional evolutionary structural optimization,SBESO)方法,通过引入权重函数更新单元的质量与刚度矩阵,控制单元删除率以使低效单元逐渐从设计域中删除。以连续体结构固有频率最大化为目标,提出了一种基于SBESO的频率优化方法,对比分析了常函数、线性函数和正弦函数等不同权重函数对连续体结构优化的影响。将等效静载荷(equivalent static loads,ESL)方法与SBESO方法相融合,提出了动载荷作用下连续体结构的动刚度优化方法。数值算例表明,SBESO方法通过调节单元删除率和权重函数,控制低效单元在结构设计域中逐渐被删除,有效抑制了单元的过删除问题。采用线性和正弦权重函数,更有利于获得连续体结构的频率最优拓扑解。随单元删除率的减少,动刚度最优拓扑解的结构边界逐渐光滑,而且逼近于同一构形。由此,所提出的SBESO方法完善了BESO方法的优化准则,对于解决连续体结构动力学优化设计问题具有较为重要的理论意义。 展开更多
关键词 优化 模型 光滑双向渐进结构优化 频率优化 等效静载荷 动刚度优化
在线阅读 下载PDF
DESCENT DIRECTION STOCHASTIC APPROXIMATION ALGORITHM WITH ADAPTIVE STEP SIZES
2
作者 Zorana Luzanin Irena Stojkovska Milena Kresoja 《计算数学:英文版》 SCIE CSCD 2019年第1期76-94,共19页
A stochastic approximation (SA)algorithm with new adaptive step sizes for solving unconstrained minimization problems in noisy environment is proposed.New adaptive step size scheme uses ordered statistics of fixed num... A stochastic approximation (SA)algorithm with new adaptive step sizes for solving unconstrained minimization problems in noisy environment is proposed.New adaptive step size scheme uses ordered statistics of fixed number of previous noisy function values as a criterion for accepting good and rejecting bad steps.The scheine allows the algorithm to move in bigger steps and avoid steps proportional to 1/k when it is expected that larger steps will improve the performance.An algorithin with the new adaptive scheme is defined for a general descent direction.The ahnost sure convergence is established.The performance of new algorithm is tested on a set of standard test problems and compared with relevant algorithms.Numerical results support theoretical expectations and verify efficiency of the algorithm regardless of chosen search direction and noise level.Numerical results on probleins arising in machine learning are also presented.Linear regression problem is considered using real data set.The results suggest that the proposed algorithln shows proinise. 展开更多
关键词 UNCONSTRAINED OPTIMIZATION STOCHASTIC OPTIMIZATION STOCHASTIC APPROXIMATION NOISY function Adaptive step size DESCENT direction Linear regression model
Soft-sensing modeling and intelligent optimal control strategy for distillation yield rate of atmospheric distillation oil refining process
3
作者 Zheng Wang Cheng Shao Li Zhu 《中国化学工程学报:英文版》 SCIE EI CAS CSCD 2019年第5期1113-1124,共12页
It is a challenge to conserve energy for the large-scale petrochemical enterprises due to complex production process and energy diversification. As critical energy consumption equipment of atmospheric distillation oil... It is a challenge to conserve energy for the large-scale petrochemical enterprises due to complex production process and energy diversification. As critical energy consumption equipment of atmospheric distillation oil refining process, the atmospheric distillation column is paid more attention to save energy. In this paper, the optimal problem of energy utilization efficiency of the atmospheric distillation column is solved by defining a new energy efficiency indicator - the distillation yield rate of unit energy consumption from the perspective of material flow and energy flow, and a soft-sensing model for this new energy efficiency indicator with respect to the multiple working conditions and intelligent optimizing control strategy are suggested for both increasing distillation yield and decreasing energy consumption in oil refining process. It is found that the energy utilization efficiency level of the atmospheric distillation column depends closely on the typical working conditions of the oil refining process, which result by changing the outlet temperature, the overhead temperature, and the bottom liquid level of the atmospheric pressure tower. The fuzzy C-means algorithm is used to classify the typical operation conditions of atmospheric distillation in oil refining process. Furthermore, the LSSVM method optimized with the improved particle swarm optimization is used to model the distillation rate of unit energy consumption. Then online optimization of oil refining process is realized by optimizing the outlet temperature, the overhead temperature with IPSO again. Simulation comparative analyses are made by empirical data to verify the effectiveness of the proposed solution. 展开更多
关键词 Energy efficiency OPTIMIZATION CRUDE oil DISTILLATION Particle WARM OPTIMIZATION Fuzzy C-MEANS algorithm Working condition
Optimized cellular automaton for stand delineation 预览
4
作者 Timo Pukkala 《林业研究:英文版》 CAS CSCD 2019年第1期107-119,共13页
Forest inventories based on remote sensing often interpret stand characteristics for small raster cells instead of traditional stand compartments.This is the case for instance in the Lidar-based and multi-source fores... Forest inventories based on remote sensing often interpret stand characteristics for small raster cells instead of traditional stand compartments.This is the case for instance in the Lidar-based and multi-source forest inventories of Finland where the interpretation units are 16 m×16 m grid cells.Using these cells as simulation units in forest planning would lead to very large planning problems.This difficulty could be alleviated by aggregating the grid cells into larger homogeneous segments before planning calculations.This study developed a cellular automaton(CA)for aggregating grid cells into larger calculation units,which in this study were called stands.The criteria used in stand delineation were the shape and size of the stands,and homogeneity of stand attributes within the stand.The stand attributes were:main site type(upland or peatland forest),site fertility,mean tree diameter,mean tree height and stand basal area.In the CA,each cell was joined to one of its adjacent stands for several iterations,until the cells formed a compact layout of homogeneous stands.The CA had several parameters.Due to high number possible parameter combinations,particle swarm optimization was used to find the optimal set of parameter values.Parameter optimization aimed at minimizing within-stand variation and maximizing between-stand variation in stand attributes.When the CA was optimized without any restrictions for its parameters,the resulting stand delineation consisted of small and irregular stands.A clean layout of larger and compact stands was obtained when the CA parameters were optimized with constrained parameter values and so that the layout was penalized as a function of the number of small stands(<0.1 ha).However,there was within-stand variation in fertility class due to small-scale variation in the data.The stands delineated by the CA explained 66–87%of variation in stand basal area,mean tree height and mean diameter,and 41–92%of variation in the fertility class of the site.It was concluded that the CA develope 展开更多
关键词 Forest planning Particle SWARM OPTIMIZATION RASTER data SEGMENTATION Spatial OPTIMIZATION
在线阅读 下载PDF
AI for 5G:research directions and paradigms
5
作者 Xiaohu YOU Chuan ZHANG +2 位作者 Xiaosi TAN Shi JIN Hequan WU 《中国科学:信息科学(英文版)》 SCIE EI CSCD 2019年第2期1-13,共13页
Wireless communication technologies such as fifth generation mobile networks(5 G)will not only provide an increase of 1000 times in Internet traffic in the next decade but will also offer the underlying technologies t... Wireless communication technologies such as fifth generation mobile networks(5 G)will not only provide an increase of 1000 times in Internet traffic in the next decade but will also offer the underlying technologies to entire industries to support Internet of things(IOT)technologies.Compared to existing mobile communication techniques,5 G has more varied applications and its corresponding system design is more complicated.The resurgence of artificial intelligence(AI)techniques offers an alternative option that is possibly superior to traditional ideas and performance.Typical and potential research directions related to the promising contributions that can be achieved through AI must be identified,evaluated,and investigated.To this end,this study provides an overview that first combs through several promising research directions in AI for 5 G technologies based on an understanding of the key technologies in 5 G.In addition,the study focuses on providing design paradigms including 5 G network optimization,optimal resource allocation,5 G physical layer unified acceleration,end-to-end physical layer joint optimization,and so on. 展开更多
关键词 5G mobile communication AI techniques network OPTIMIZATION resource ALLOCATION UNIFIED ACCELERATION END-TO-END joint OPTIMIZATION
A decision support system for satellite layout integrating multi-objective optimization and multi-attribute decision making 预览
6
作者 LIANG Yan’gang QIN Zheng 《系统工程与电子技术:英文版》 SCIE EI CSCD 2019年第3期535-544,共10页
A decision support system, including a multi-objective optimization framework and a multi-attribute decision making approach is proposed for satellite equipment layout. Firstly, given three objectives (to minimize the... A decision support system, including a multi-objective optimization framework and a multi-attribute decision making approach is proposed for satellite equipment layout. Firstly, given three objectives (to minimize the C.G. offset, the cross moments of inertia and the space debris impact risk), we develop a threedimensional layout optimization model. Unlike most of the previous works just focusing on mass characteristics of the system, a space debris impact risk index is developed. Secondly, we develop an efficient optimization framework for the integration of computer-aided design (CAD) software as well as the optimization algorithm to obtain the Pareto front of the layout optimization problem. Thirdly, after obtaining the candidate solutions, we present a multi-attribute decision making approach, which integrates the smart Pareto filter and the correlation coefficient and standard deviation (CCSD) method to select the best tradeoff solutions on the optimal Pareto fronts. Finally, the framework and the decision making approach are applied to a case study of a satellite platform. 展开更多
关键词 layout OPTIMIZATION SATELLITE MULTI-OBJECTIVE OPTIMIZATION PARETO FRONT MULTI-ATTRIBUTE decision making
在线阅读 下载PDF
Adaptive optimization methodology based on Kriging modeling and a trust region method
7
作者 Chunna LI Qifeng PAN 《中国航空学报:英文版》 SCIE EI CAS CSCD 2019年第2期281-295,共15页
Surrogate-Based Optimization(SBO) is becoming increasingly popular since it can remarkably reduce the computational cost for design optimizations based on high-fidelity and expensive numerical analyses. However, for c... Surrogate-Based Optimization(SBO) is becoming increasingly popular since it can remarkably reduce the computational cost for design optimizations based on high-fidelity and expensive numerical analyses. However, for complicated optimization problems with a large design space, many design variables, and strong nonlinearity, SBO converges slowly and shows imperfection in local exploitation. This paper proposes a trust region method within the framework of an SBO process based on the Kriging model. In each refinement cycle, new samples are selected by a certain design of experiment method within a variable design space, which is sequentially updated by the trust region method. A multi-dimensional trust-region radius is proposed to improve the adaptability of the developed methodology. Further, the scale factor and the limit factor of the trust region are studied to evaluate their effects on the optimization process. Thereafter, different SBO methods using error-based exploration, prediction-based exploitation, refinement based on the expected improvement function, a hybrid refinement strategy, and the developed trust-regionbased refinement are utilized in four analytical tests. Further, the developed optimization methodology is employed in the drag minimization of an RAE2822 airfoil. Results indicate that it has better robustness and local exploitation capability in comparison with those of other SBO methods. 展开更多
关键词 AIRFOIL Design OPTIMIZATION KRIGING model Surrogate-based OPTIMIZATION TRUST-REGION method
Parameter Optimization of Interval Type-2 Fuzzy Neural Networks Based on PSO and BBBC Methods 预览
8
作者 Jiajun Wang Tufan Kumbasar 《自动化学报:英文版》 CSCD 2019年第1期247-257,共11页
Interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Althou... Interval type-2 fuzzy neural networks(IT2FNNs)can be seen as the hybridization of interval type-2 fuzzy systems(IT2FSs) and neural networks(NNs). Thus, they naturally inherit the merits of both IT2 FSs and NNs. Although IT2 FNNs have more advantages in processing uncertain, incomplete, or imprecise information compared to their type-1 counterparts, a large number of parameters need to be tuned in the IT2 FNNs,which increases the difficulties of their design. In this paper,big bang-big crunch(BBBC) optimization and particle swarm optimization(PSO) are applied in the parameter optimization for Takagi-Sugeno-Kang(TSK) type IT2 FNNs. The employment of the BBBC and PSO strategies can eliminate the need of backpropagation computation. The computing problem is converted to a simple feed-forward IT2 FNNs learning. The adoption of the BBBC or the PSO will not only simplify the design of the IT2 FNNs, but will also increase identification accuracy when compared with present methods. The proposed optimization based strategies are tested with three types of interval type-2 fuzzy membership functions(IT2FMFs) and deployed on three typical identification models. Simulation results certify the effectiveness of the proposed parameter optimization methods for the IT2 FNNs. 展开更多
关键词 BIG bang-big crunch (BBBC) INTERVAL type-2 fuzzy NEURAL networks (IT2FNNs) parameter OPTIMIZATION particle SWARM OPTIMIZATION (PSO)
在线阅读 下载PDF
Application of several optimization techniques for estimating TBM advance rate in granitic rocks 预览
9
作者 Danial Jahed Armaghani Mohammadreza Koopialipoor +1 位作者 Aminaton Marto Saffet Yagiz 《岩石力学与岩土工程学报:英文版》 CSCD 2019年第4期779-789,共11页
This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine (TBM) in different weathered zones of granite.For this purpose,extensive field and laboratory studies hav... This study aims to develop several optimization techniques for predicting advance rate of tunnel boring machine (TBM) in different weathered zones of granite.For this purpose,extensive field and laboratory studies have been conducted along the 12,649 m of the Pahang e Selangor raw water transfer tunnel in Malaysia.Rock properties consisting of uniaxial compressive strength (UCS),Brazilian tensile strength (BTS),rock mass rating (RMR),rock quality designation (RQD),quartz content (q) and weathered zone as well as machine specifications including thrust force and revolution per minute (RPM) were measured to establish comprehensive datasets for optimization.Accordingly,to estimate the advance rate of TBM,two new hybrid optimization techniques,i.e.an artificial neural network (ANN) combined with both imperialist competitive algorithm (ICA) and particle swarm optimization (PSO),were developed for mechanical tunneling in granitic rocks.Further,the new hybrid optimization techniques were compared and the best one was chosen among them to be used for practice.To evaluate the accuracy of the proposed models for both testing and training datasets,various statistical indices including coefficient of determination (R^2),root mean square error (RMSE) and variance account for (VAF) were utilized herein.The values of R^2,RMSE,and VAF ranged in 0.939-0.961,0.022-0.036,and 93.899-96.145,respectively,with the PSO-ANN hybrid technique demonstrating the best performance.It is concluded that both the optimization techniques,i.e.PSO-ANN and ICA-ANN,could be utilized for predicting the advance rate of TBMs;however,the PSO-ANN technique is superior. 展开更多
关键词 TUNNEL BORING machines (TBMs) ADVANCE rate Hybrid OPTIMIZATION techniques Particle SWARM OPTIMIZATION (PSO) Imperialist COMPETITIVE algorithm (ICA)
在线阅读 下载PDF
一种绿色能源小水线面双体无人艇优化初步研究 预览
10
作者 汤旸 《江苏科技信息》 2019年第6期28-30,53共4页
文章针对小水线面双体无人艇,选取该无人艇的快速性、操纵性、耐波性和太阳能系统的目标函数,确定设计变量和约束条件的范围,建立了综合优化数学模型,并选取合适的优化算法,自主编写了一套优化设计软件,进行综合优化计算。先比较不同代... 文章针对小水线面双体无人艇,选取该无人艇的快速性、操纵性、耐波性和太阳能系统的目标函数,确定设计变量和约束条件的范围,建立了综合优化数学模型,并选取合适的优化算法,自主编写了一套优化设计软件,进行综合优化计算。先比较不同代数的粒子群算法的适应度函数值,而后选用粒子群算法作为主算法得出最好的5个个体信息,与自身及其他优化算法结合进行二次计算。最终得到小水线面双体船最优船型参数,研究结果可为小水线面双体无人艇各项性能优化的多目标、多变量及多约束条件综合优化问题提供参考。 展开更多
关键词 海洋 绿色能源 小水线面双体无人艇 优化 粒子群算法
在线阅读 下载PDF
Performance improvement of optimization solutions by POD-based data mining
11
作者 Yanhui DUAN Wenhua WU +4 位作者 Peihong ZHANG Fulin TONG Zhaolin FAN Guiyu ZHOU Jiaqi LUO 《中国航空学报:英文版》 SCIE EI CAS CSCD 2019年第4期826-838,共13页
The performance of an optimized aerodynamic shape is further improved by a second-step optimization using the design knowledge discovered by a data mining technique based on Proper Orthogonal Decomposition(POD) in the... The performance of an optimized aerodynamic shape is further improved by a second-step optimization using the design knowledge discovered by a data mining technique based on Proper Orthogonal Decomposition(POD) in the present study. Data generated in the first-step optimization by using evolution algorithms is saved as the source data, among which the superior data with improved objectives and maintained constraints is chosen. Only the geometry components of the superior data are picked out and used for constructing the snapshots of POD. Geometry characteristics of the superior data illustrated by POD bases are the design knowledge, by which the second-step optimization can be rapidly achieved. The optimization methods are demonstrated by redesigning a transonic compressor rotor blade, NASA Rotor 37, in the study to maximize the peak adiabatic efficiency, while maintaining the total pressure ratio and mass flow rate.Firstly, the blade is redesigned by using a particle swarm optimization method, and the adiabatic efficiency is increased by 1.29%. Then, the second-step optimization is performed by using the design knowledge, and a 0.25% gain on the adiabatic efficiency is obtained. The results are presented and addressed in detail, demonstrating that geometry variations significantly change the pattern and strength of the shock wave in the blade passage. The former reduces the separation loss,while the latter reduces the shock loss, and both favor an increase of the adiabatic efficiency. 展开更多
关键词 Aerodynamic shape OPTIMIZATION Computational ?uid dynamics Data mining Particle SWARM OPTIMIZATION PROPER Orthogonal Decomposition TRANSONIC ?ow TURBOMACHINERY
Modeling river water quality parameters using modified adaptive neuro fuzzy inference system 预览
12
作者 Armin Azad Hojat Karami +2 位作者 Saeed Farzin Sayed-Farhad Mousavi Ozgur Kisi 《水科学与水工程:英文版》 EI CAS CSCD 2019年第1期45-54,共10页
Water quality is always one of the most important factors in human health. Artificial intelligence models are respected methods for modeling water quality. The evolutionary algorithm (EA) is a new technique for improv... Water quality is always one of the most important factors in human health. Artificial intelligence models are respected methods for modeling water quality. The evolutionary algorithm (EA) is a new technique for improving the performance of artificial intelligence models such as the adaptive neuro fuzzy inference system (ANFIS) and artificial neural networks (ANN). Attempts have been made to make the models more suitable and accurate with the replacement of other training methods that do not suffer from some shortcomings, including a tendency to being trapped in local optima or voluminous computations. This study investigated the applicability of ANFIS with particle swarm optimization (PSO) and ant colony optimization for continuous domains (ACOR) in estimating water quality parameters at three stations along the Zayandehrood River, in Iran. The ANFIS-PSO and ANFIS-ACOR methods were also compared with the classic ANFIS method, which uses least squares and gradient descent as training algorithms. The estimated water quality parameters in this study were electrical conductivity (EC), total dissolved solids (TDS), the sodium adsorption ratio (SAR), carbonate hardness (CH), and total hardness (TH). Correlation analysis was performed using SPSS software to determine the optimal inputs to the models. The analysis showed that ANFIS-PSO was the better model compared with ANFIS-ACOR. It is noteworthy that EA models can improve ANFIS' performance at all three stations for different water quality parameters. 展开更多
关键词 Water quality parameters ANFIS EVOLUTIONARY algorithm Particle SWARM OPTIMIZATION Ant COLONY OPTIMIZATION for continuous DOMAINS
在线阅读 下载PDF
Hyperparameter Optimization for Machine Learning Models Based on Bayesian Optimization 预览
13
作者 Jia Wu Xiu-Yun Chen +3 位作者 Hao Zhang Li-Dong Xiong Hang Lei Si-Hao Deng 《电子科技学刊:英文版》 CAS CSCD 2019年第1期26-40,共15页
Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models.Several techni... Hyperparameters are important for machine learning algorithms since they directly control the behaviors of training algorithms and have a significant effect on the performance of machine learning models.Several techniques have been developed and successfully applied for certain application domains.However,this work demands professional knowledge and expert experience.And sometimes it has to resort to the brute-force search.Therefore,if an efficient hyperparameter optimization algorithm can be developed to optimize any given machine learning method,it will greatly improve the efficiency of machine learning.In this paper,we consider building the relationship between the performance of the machine learning models and their hyperparameters by Gaussian processes.In this way,the hyperparameter tuning problem can be abstracted as an optimization problem and Bayesian optimization is used to solve the problem.Bayesian optimization is based on the Bayesian theorem.It sets a prior over the optimization function and gathers the information from the previous sample to update the posterior of the optimization function.A utility function selects the next sample point to maximize the optimization function.Several experiments were conducted on standard test datasets.Experiment results show that the proposed method can find the best hyperparameters for the widely used machine learning models,such as the random forest algorithm and the neural networks,even multi-grained cascade forest under the consideration of time cost. 展开更多
关键词 BAYESIAN OPTIMIZATION GAUSSIAN process hyperparameter OPTIMIZATION MACHINE LEARNING
在线阅读 免费下载
Heterogeneous pigeon-inspired optimization
14
作者 Hao WANG Zhuxi ZHANG +4 位作者 Zhen DAI Jun CHEN Xi ZHU Wenbo DU Xianbin CAO 《中国科学:信息科学(英文版)》 SCIE EI CSCD 2019年第7期64-72,共9页
Pigeon-inspired optimization(PIO) is a swarm intelligence optimizer inspired by the homing behavior of pigeons. PIO consists of two optimization stages which employ the map and compass operator,and the landmark operat... Pigeon-inspired optimization(PIO) is a swarm intelligence optimizer inspired by the homing behavior of pigeons. PIO consists of two optimization stages which employ the map and compass operator,and the landmark operator, respectively. In canonical PIO, these two operators treat every bird equally,which deviates from the fact that birds usually act heterogenous roles in nature. In this paper, we propose a new variant of PIO algorithm considering bird heterogeneity — HPIO. Both of the two operators are improved through dividing the birds into hub and non-hub roles. By dividing the birds into two groups, these two groups of birds are respectively assigned with different functions of 'exploitation' and 'exploration', so that they can closely interact with each other to locate the best promising solution. Extensive experimental studies illustrate that the bird heterogeneity produced by our algorithm can benefit the information exchange between birds so that the proposed PIO variant significantly outperforms the canonical PIO. 展开更多
关键词 heuristic OPTIMIZATION pigeon-inspired OPTIMIZATION particle heterogeneity network-based topology SCALE-FREE network selective-informed learning
Distributed Majorization-Minimization for Laplacian Regularized Problems 预览
15
作者 Jonathan Tuck David Hallac Stephen Boyd 《自动化学报:英文版》 CSCD 2019年第1期45-52,共8页
We consider the problem of minimizing a block separable convex function(possibly nondifferentiable, and including constraints) plus Laplacian regularization, a problem that arises in applications including model fitti... We consider the problem of minimizing a block separable convex function(possibly nondifferentiable, and including constraints) plus Laplacian regularization, a problem that arises in applications including model fitting, regularizing stratified models, and multi-period portfolio optimization. We develop a distributed majorization-minimization method for this general problem, and derive a complete, self-contained, general,and simple proof of convergence. Our method is able to scale to very large problems, and we illustrate our approach on two applications, demonstrating its scalability and accuracy. 展开更多
关键词 Convex OPTIMIZATION DISTRIBUTED OPTIMIZATION graphical networks LAPLACIAN REGULARIZATION
在线阅读 下载PDF
Surrogate-Assisted Particle Swarm Optimization Algorithm With Pareto Active Learning for Expensive Multi-Objective Optimization 预览
16
作者 Zhiming Lv Linqing Wang +2 位作者 Zhongyang Han Jun Zhao Wei Wang 《自动化学报:英文版》 CSCD 2019年第3期838-849,共12页
For multi-objective optimization problems, particle swarm optimization (PSO) algorithm generally needs a large number of fitness evaluations to obtain the Pareto optimal solutions. However, it will become substantiall... For multi-objective optimization problems, particle swarm optimization (PSO) algorithm generally needs a large number of fitness evaluations to obtain the Pareto optimal solutions. However, it will become substantially time-consuming when handling computationally expensive fitness functions. In order to save the computational cost, a surrogate-assisted PSO with Pareto active learning is proposed. In real physical space (the objective functions are computationally expensive), PSO is used as an optimizer, and its optimization results are used to construct the surrogate models. In virtual space, objective functions are replaced by the cheaper surrogate models, PSO is viewed as a sampler to produce the candidate solutions. To enhance the quality of candidate solutions, a hybrid mutation sampling method based on the simulated evolution is proposed, which combines the advantage of fast convergence of PSO and implements mutation to increase diversity. Furthermore,ε-Pareto active learning (ε-PAL) method is employed to pre-select candidate solutions to guide PSO in the real physical space. However, little work has considered the method of determining parameter ε. Therefore, a greedy search method is presented to determine the value of ε where the number of active sampling is employed as the evaluation criteria of classification cost. Experimental studies involving application on a number of benchmark test problems and parameter determination for multi-input multi-output least squares support vector machines (MLSSVM) are given, in which the results demonstrate promising performance of the proposed algorithm compared with other representative multi-objective particle swarm optimization (MOPSO) algorithms. 展开更多
关键词 MULTIOBJECTIVE OPTIMIZATION PARETO active learning PARTICLE SWARM OPTIMIZATION (PSO) surrogate
在线阅读 下载PDF
低展弦比CAES向心涡轮叶顶型线的正交设计优化 预览
17
作者 王星 李文 +3 位作者 张雪辉 朱阳历 左志涛 陈海生 《风机技术》 2019年第1期11-21,I0008共12页
为降低叶顶泄漏损失,本文首次将NACA 翼型引入向心涡轮,并采用正交试验设计和计算流体动力学(CFD)方法获得了具有最优NACA 叶顶型线的向心涡轮叶片,并揭示了该叶片对叶顶泄漏损失的控制机理。结果表明,最优NACA 叶顶型线具有较大的前缘... 为降低叶顶泄漏损失,本文首次将NACA 翼型引入向心涡轮,并采用正交试验设计和计算流体动力学(CFD)方法获得了具有最优NACA 叶顶型线的向心涡轮叶片,并揭示了该叶片对叶顶泄漏损失的控制机理。结果表明,最优NACA 叶顶型线具有较大的前缘内接圆半径、较小的尾缘厚度,以及更靠近前缘的最大厚度位置。其内接圆半径和最大厚度位置对向心涡轮等熵效率的影响度也随叶顶间隙增加而增大。当叶顶间隙为8%出口叶高时,最优NACA叶顶型线可使向心涡轮等熵效率提高1.47%,并使向心涡轮能够在非设计工况下具有较高效率。该型线能够降低尾缘附近的叶顶泄漏速度,减弱泄漏流与主流掺混强度,使流动损失降低。 展开更多
关键词 Radial EXPANDER Optimization Blade Tip Profile CAES ORTHOGONAL Experiment Design
在线阅读 下载PDF
An Inverse Power Generation Mechanism Based Fruit Fly Algorithm for Function Optimization
18
作者 LIU Ao DENG Xudong +2 位作者 REN Liang LIU Ying LIU Bo 《系统科学与复杂性学报:英文版》 SCIE EI CSCD 2019年第2期634-656,共23页
As a novel population-based optimization algorithm, fruit fly optimization(FFO) algorithm is inspired by the foraging behavior of fruit flies and possesses the advantages of simple search operations and easy implement... As a novel population-based optimization algorithm, fruit fly optimization(FFO) algorithm is inspired by the foraging behavior of fruit flies and possesses the advantages of simple search operations and easy implementation. Just like most population-based evolutionary algorithms, the basic FFO also suffers from being trapped in local optima for function optimization due to premature convergence.In this paper, an improved FFO, named IPGS-FFO, is proposed in which two novel strategies are incorporated into the conventional FFO. Specifically, a smell sensitivity parameter together with an inverse power generation mechanism(IPGS) is introduced to enhance local exploitation. Moreover,a dynamic shrinking search radius strategy is incorporated so as to enhance the global exploration over search space by adaptively adjusting the searching area in the problem domain. The statistical performance of FFO, the proposed IPGS-FFO, three state-of-the-art FFO variants, and six metaheuristics are tested on twenty-six well-known unimodal and multimodal benchmark functions with dimension 30, respectively. Experimental results and comparisons show that the proposed IPGS-FFO achieves better performance than three FFO variants and competitive performance against six other meta-heuristics in terms of the solution accuracy and convergence rate. 展开更多
关键词 EVOLUTIONARY algorithms FRUIT FLY OPTIMIZATION function OPTIMIZATION META-HEURISTICS
面向反应再生过程的量子粒子群多目标优化 预览
19
作者 白竣仁 易军 +2 位作者 李倩 吴凌 陈雪梅 《化工学报》 EI CAS CSCD 北大核心 2019年第2期750-756,共7页
针对催化裂化反应再生过程难以有效解决提升效率、降低损耗、减少排放的多目标优化问题,利用改进的多目标量子粒子群算法进行求解。建立轻油收率、焦炭产率和硫化物排量的多目标优化模型;引入拥挤熵排序更新外部档案,精确估计非支配解... 针对催化裂化反应再生过程难以有效解决提升效率、降低损耗、减少排放的多目标优化问题,利用改进的多目标量子粒子群算法进行求解。建立轻油收率、焦炭产率和硫化物排量的多目标优化模型;引入拥挤熵排序更新外部档案,精确估计非支配解集分布性;构造自适应因子以动态调整吸引子,平衡算法的收敛性和多样性;再引入高斯变异进行分段式扰动,增强算法的局部搜索精度,最后求解该优化模型。对某厂催化裂化进行实验,得到轻质油吸收率76.22%,焦炭产率5.72%和硫化物排放量626mg/m3的结果,均优于其他比较算法,表明改进后的算法可以快速、准确地获得分布均匀的Pareto最优解,能有效解决反应再生过程多目标优化问题。 展开更多
关键词 催化 反应 控制 优化 量子粒子群优化算法 拥挤熵
在线阅读 下载PDF
民营企业员工激励问题的探析 预览
20
作者 王利华 《现代农业研究》 2019年第4期127-129,共3页
中国改革开放以来,快速发展的民营经济,已成为国民经济发展,促进市场繁荣和社会稳定的一支重要力量。然而,在发展过程中出现的各种问题也影响着民营企业的健康发展。这些问题出现的原因是多方面的,其中一个重要的方面就是企业激励机制... 中国改革开放以来,快速发展的民营经济,已成为国民经济发展,促进市场繁荣和社会稳定的一支重要力量。然而,在发展过程中出现的各种问题也影响着民营企业的健康发展。这些问题出现的原因是多方面的,其中一个重要的方面就是企业激励机制的不健全。本文立足于对民营员工激励问题的探析,提出民营员工激励问题的改进措施。 展开更多
关键词 民营企业 员工激励 员工满意度 改进
在线阅读 下载PDF
上一页 1 2 250 下一页 到第
使用帮助 返回顶部 意见反馈