检索结果-内蒙古大学图书馆

hybrid algorithm for global optimization based on periodic selection scheme in engineering computation

ENGINEERING COMPUTATIONS 2024年第2期41卷 385-412页

作者： Zhou, Ting Wei, Yingjie Niu, Jian Jie, Yuxin Tsinghua Univ Beijing Peoples R China China Univ Geosci Beijing Peoples R China Gen Municipal Engn Design & Res Inst Co Ltd Beijing Peoples R China Tsinghua Univ Hydraul Engn Dept Beijing Peoples R China

PurposeMetaheuristic algorithms based on biology, evolutionary theory and physical principles, have been widely developed for complex global optimization. This paper aims to present a new hybrid optimization algorithm that combines the characteristics of biogeography-based optimization (BBO), invasive weed optimization (IWO) and genetic algorithms (GAs).Design/methodology/approachThe significant difference between the new algorithm and original optimizers is a periodic selection scheme for offspring. The selection criterion is a function of cyclic discharge and the fitness of populations. It differs from traditional optimization methods where the elite always gains advantages. With this method, fitter populations may still be rejected, while poorer ones might be likely retained. The selection scheme is applied to help escape from local optima and maintain solution *** efficiency of the proposed method is tested on 13 high-dimensional, nonlinear benchmark functions and a homogenous slope stability problem. The results of the benchmark function show that the new method performs well in terms of accuracy and solution diversity. The algorithm converges with a magnitude of 10-4, compared to 102 in BBO and 10-2 in IWO. In the slope stability problem, the safety factor acquired by the analogy of slope erosion (ASE) is closer to the recommended ***/valueThis paper introduces a periodic selection strategy and constructs a hybrid optimizer, which enhances the global exploration capacity of metaheuristic algorithms.

关键词： hybrid algorithm Global optimization Periodic selection scheme Engineering computation

来源：评论

学校读者我要写书评

暂无评论

hybrid algorithm for image denoising of mixed speckle-impulse noise in planar laser-induced fluorescence (PLIF) imaging

引用

OPTICS AND LASERS IN ENGINEERING 2025年 187卷

作者： Mirzaei, Mohsen Wang, Yejun Wang, Qiu Guo, Chenglong Li, Pan Zhao, Wei Chinese Acad Sci Inst Mech State Key Lab High Temp Gas Dynam Beijing 100190 Peoples R China Vali e Asr Univ Rafsanjan Dept Mech Engn Rafsanjan Iran Univ Chinese Acad Sci Sch Engn Sci Beijing 100049 Peoples R China China Univ Min & Technol Sch Low Carbon Energy & Power Engn Xuzhou 221116 Peoples R China Chinese Acad Sci Inst Mech Key Lab Micrograv Beijing 100190 Peoples R China

Flow visualization is essential for a comprehensive understanding of complex fluid dynamics. Planar laserinduced fluorescence (PLIF) has emerged as one of the most promising techniques for multi-dimensional imaging acquisition, offering high spatial and temporal resolutions. However, PLIF images frequently suffer from impulse and speckle noise under low-density, low-pressure conditions, thereby affecting the quality of visualization. This study proposes a novel hybrid algorithm that integrates order-statistic and non-local means filters with residue feedback. The algorithm enhances image quality and eliminates noise effectively by incorporating recursive processes guided by the structural similarity index (SSIM) and anisotropic diffusion. The efficacy of the method is confirmed through evaluation using peak signal-to-noise ratio (PSNR) and SSIM on images with varying noise levels. The algorithm demonstrates computational efficiency and shows promise for denoising PLIF images.

关键词： Planar laser-induced fluorescence (PLIF) Image denoising hybrid algorithm Residue feedback Contrast enchantment

来源：评论

学校读者我要写书评

暂无评论

hybrid algorithm for the time-dependent Hartree-Fock method using the Yang-Baxter equation on quantum computers

引用

ELECTRONIC STRUCTURE 2024年第4期6卷 045007-045007页

作者： Gulania, Sahil Gray, Stephen K. Alexeev, Yuri Peng, Bo Govind, Niranjan Argonne Natl Lab Math & Comp Sci Div Lemont IL 60439 USA Argonne Natl Lab Ctr Nanoscale Mat Lemont IL 60439 USA Argonne Natl Lab Computat Sci Div Lemont IL 60439 USA Pacific Northwest Natl Lab Phys & Computat Sci Directorate Richland WA 99352 USA

The time-dependent Hartree-Fock (TDHF) method is an approach to simulate the mean field dynamics of electrons within the assumption that the electrons move independently in their self-consistent average field and within the space of single Slater determinants. One of the major advantages of performing time dynamics within Hartree-Fock theory is the free fermionic nature of the problem, which makes TDHF classically simulatable in polynomial time. Here, we present a hybrid TDHF implementation for quantum computers. This quantum circuit grows with time;but with our recent work on circuit compression via the Yang-Baxter equation (YBE), the resulting circuit is constant depth. This study provides a new way to simulate TDHF with the aid of a quantum device as well as provides a new direction for the application of YBE symmetry in quantum chemistry simulations.

关键词： Hartree-Fock Yang-Baxter hybrid algorithm

来源：评论

学校读者我要写书评

暂无评论

hybrid algorithm to Find Minimum Expected Escape Time From a Maze

引用

ENGINEERING LETTERS 2023年第1期31卷 346页

作者： Yendri, Sheinna Soelaiman, Rully Purwananto, Yudhi Sepuluh Nopember Inst Technol Dept Informat Surabaya Indonesia

Expected value is one of the important factors used in scenario analysis and decision makings. The most commonly known method in finding expected value of a probability problem is using deterministic approach to formulate and solve a system of linear equations that represent the given problem. This method, nonetheless, requires unrealistic conditions for it to be executable which are knowing all the scenarios that might happen and the probability for each scenario to happen. In real life situations, most problems do not have a definite scenarios provided with each of their probabilities. This is a major loophole in deterministic approach for finding an expected value of certain problems. One specific probability problem that has this issue is to find the minimum expected time needed to escape from a two-dimensional maze without any information given for the escape path and the movement probabilities. In this paper, we propose a novel solution for the aforementioned problem using hybrid algorithm which is a combination of deterministic and heuristic approach. In our hybrid algorithm, the heuristic method is for optimizing the assembly of possible path scenarios, while the deterministic method is for counting and finding the minimum expected time for each path scenario. Based on the case study testing result, the solution using this hybrid algorithm requires an average time of only 2.4 seconds which is seven times faster than the required time limit, and an average memory of 5.31MB which is only using 0.3% resources from the required memory limit.

关键词： expected value hybrid algorithm linear alge-bra and path scenario

来源：评论

学校读者我要写书评

暂无评论

A bi-level hybrid algorithm for solving multi-target inspection path planning problem of mobile robot in complex radioactive indoor environment

引用

EXPERT SYSTEMS WITH APPLICATIONS 2025年 266卷

作者： Zhang, De Yin, Yebo Xiong, Guokai Zou, Shuliang Univ South China Sch Resource Environm & Safety Engn Hengyang 421001 Peoples R China Hunan Prov Key Lab Emergency Safety Technol & Equi Hengyang 421001 Peoples R China Univ South China Sch Comp Sci Hengyang 421001 Peoples R China Univ South China Sch Mech Engn Hengyang 421001 Peoples R China

Multi-target inspection path planning (MTIPP) of mobile robot (MR) represents a significant area of research in the context of environmental monitoring and routine inspection of nuclear power plants (NPPs). Given the challenges posed by complex radioactive indoor environments, characterized by the presence of numerous radioactive sources and dense obstacles, a bi-level multi-objective programming framework is proposed to model the MTIPP problem. To navigate this model effectively, a novel bi-level hybrid algorithm named ACO-GA-A* that integrates improved ant colony optimization (IACO), genetic algorithm (GA) with modified A* algorithm is developed. In the upper level, ACO with a GA-based non-uniform initial pheromone distribution, an adaptive heuristic function and an elite strategy for pheromone update is employed to determine the optimal traversal sequence of inspection targets. In the lower level, a modified A* algorithm, which considers multiple constraints including path length, risk degree and energy consumption, is utilized to plan pairwise paths between targets, thereby generating cost graphs. Comparative simulation experiments are conducted in various complexity radioactive scenarios. The results indicate that the modified A* can plan pairwise paths with lower total costs in shorter time compared to traditional A*, ACO, and GA. Furthermore, the ACO-GA-A* demonstrates better sensitivity, reliability, and convergence characteristics compared to some other bi-level hybrid algorithms. Subsequent real-world experimentation corroborates the effectiveness and feasibility of both the bi-level programming framework for MTIPP and the proposed ACO-GA-A* algorithm.

关键词： Mobile robots (MRs) Radioactive environment hybrid algorithm Ant colony optimization-genetic algorithm-A (ACO-GA-A*)

来源：评论

学校读者我要写书评

暂无评论

A hybrid algorithm for the dimensional synthesis of parallel manipulators

引用

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2025年第9期239卷 3203-3215页

作者： Yang, Chao Zhang, Hui Huang, Fengli Ye, Wei Jiaxing Nanhu Univ Coll Mech & Elect Engn Jiaxing Peoples R China Jiaxing Univ STl Valley Ind & Innovat Inst G60 Jiaxing Peoples R China Zhejiang Sci Tech Univ Natl & Local Joint Engn Res Ctr Reliabil Anal & Te Hangzhou Peoples R China

Increasing the number of non-dominated solutions among multiple objectives is beneficial for determining the optimal comprehensive performance of parallel manipulators (PMs). In engineering, we often achieve this by increasing the population size of intelligent optimization algorithms, which leads to an exponential increase in computational costs, especially for high computationally intensive objective functions. How to increase the number of non-dominated solutions without significantly increasing computational costs is a challenge faced by the dimensional synthesis of PMs. A hybrid algorithm combing particle swarm optimization (PSO) and direct search algorithms was proposed in this work to fix this issue. And, a procedure for direct search algorithm to determine the Pareto optimal front is proposed. The proposed hybrid algorithm is implemented in three steps: (1) The design parameter space is coarsely meshed to determine the amplitude of objective functions to achieve its dimensionless purpose;(2) the PSO algorithm is adopted to obtain the approximate Pareto front, as well as the Pareto front hypersurface stretched by the optimal design parameter space;(3) the neighborhood of the approximate Pareto optimal set was finely meshed, and the direct search algorithm is proposed to update the non-dominated solutions that are closer to the real Pareto front. The proposed hybrid algorithm can obtain more non-dominated solutions while avoiding the significant increase in computational costs caused by increasing population size. The proposed algorithm was implemented using the dimensional synthesis of the 2UPR-RPU PM. Compared with the PSO algorithm, which produced 80 sets of non-dominated solutions, the proposed hybrid algorithm generated 242 sets of solutions with the computational costs only increased 32%.

关键词： Parallel manipulator dimensional synthesis pareto front hybrid algorithm

来源：评论

学校读者我要写书评

暂无评论

Modeling, landscape analysis, and solving the capacitated single-allocation hub maximal covering problem using the GARVND hybrid algorithm

引用

EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartC期249卷

作者： Karimi, Amin Masehian, Ellips Univ Melbourne Sch Math & Stat Parkville Vic 3010 Australia Calif State Polytech Univ Pomona Ind & Mfg Engn Dept Pomona CA 91768 USA

In this paper, a new variation to the Hub Maximal Covering problem called the Capacitated Single-Allocation Hub Maximal Covering Problem (CSAHMCP) is addressed for the first time and a novel binary-integer mathematical model has been proposed. To analyze the features of the problem's solution space and to determine the most effective heuristic approach to solve it, a comprehensive Fitness Landscape Analysis is performed which reveals that the problem is of 'rugged plain' type and can best be solved using an algorithm with good exploration and exploitation capabilities at the same time. Accordingly, we proposed a new algorithm named GARVND, which is a hybrid of the Genetic algorithm acting as the main framework for diversification, and a variant of the Variable Neighborhood Descent algorithm called 'Reduced VND' acting as a means to improve individual solutions (intensification) by searching in the spaces generated by four types of neighborhood operators. We also introduced two original crossover and mutation operators that can be used for any hub location solution method. The parameters of GARVND are tuned toward less computational times and higher solution quality by using the Taguchi experiment design technique. We solved 114 CSAHMCP instances (several times each) from the Australian Post (AP) dataset using the proposed GARVND, as well as by the Genetic algorithm, Variable Neighborhood Search, Simulated Annealing, Tabu Search algorithms, and a hybrid algorithm consisting of Grey Wolf Optimizer and Simulated Annealing, in addition to the Gurobi exact solver. Extensive computational results and statistical analyses showed that GARVND yields favorable solutions, outperforming all the compared algorithms by producing smaller gaps with global optimal objective values. In addition, to illustrate the practical value of the CSAHMCP and the GARVND method, we performed a cost-flow sensitivity analysis for the AP Central Sydney network and came up with the best expansion sce

关键词： Hub Location problem Capacitated Hub Maximal Covering Fitness Landscape Analysis Genetic algorithm Variable Neighborhood Descent Search hybrid algorithm Parameter Tuning

来源：评论

学校读者我要写书评

暂无评论

Reliability evaluation of reinforced concrete arch bridges during construction based on LGSSA-SVR hybrid algorithm

引用

STRUCTURAL CONCRETE 2024年第6期25卷 4150-4166页

作者： Xu Binlin Tian Zhongchu Shen Xiaoping Bai Wenguang Hunan Inst Technol Coll Civil & Construct Engn Hengyang Peoples R China Fujian Univ Technol Sch Civil Engn Fuzhou Peoples R China China Railway 17th Bur Grp City Construct Co Guiyang Peoples R China

During the cantilever casting process of reinforced concrete arch bridges with cantilever cast-in-situ method, it is difficult to select representative training samples for reliability analysis due to its complex structural system. Many random variables and large computational sample size, this paper proposes to solve the reliability indexes based on the combination of the improved sparrow search algorithm (LGSSA) and the support vector regression (SVR) method. Firstly, random variables are selected according to the actual situation of the bridge structure. Then representative training samples are designed to be substituted into the finite element model through the homogeneous method. The resultant data samples are used to fit the functional function by the support vector regression. Then combined with the penalized function method to transform the nonlinear optimization into the problem of solving the extreme value of the function. Based on the improved SSA to solve the extreme value of the final function. Finally the reliability index of the structure is obtained. With the background of reinforced concrete arch bridge of 200m, the method is used to analyze the reliability of its buckling cable stress, arch stress, buckling tower deviation and structural system reliability during the cantilever casting process. The results show that the overall structural reliability of the arch ring during cantilever casting is 3.502-3.608. The indexes of buckling cable stress reliability are 3.806-6.784. The indexes of arch ring stress reliability are 4.379-7.562, and the indexes of buckling tower deflection reliability are 3.608-8.123.

关键词： hybrid algorithm LGSSA-SVR reinforced arch bridge reliability training samples

来源：评论

学校读者我要写书评

暂无评论

A novel multivariable optimization strategy for methanol to propylene fixed bed reactors using a hybrid algorithm

引用

CHEMICAL ENGINEERING SCIENCE 2025年 306卷

作者： Zhang, Chijin Hong, Xiaodong Liao, Zuwei Jiang, Binbo Wang, Jingdai Yang, Yongrong Zhejiang Univ Coll Chem & Biol Engn Hangzhou 310027 Peoples R China Zhejiang Univ Engn Res Ctr Funct Mat Intelligent Mfg Zhejiang Pr ZJU Hangzhou Global Sci & Technol Innovat Ctr Hangzhou 311215 Peoples R China

The methanol-to-propylene (MTP) process holds significant promise as a viable route for propylene production. To enhance the efficiency of this process, we conducted a comprehensive study employing a two-step approach that integrates micro- and macro-level perspectives. Firstly, we established a gas-solid two-phase reactor model tailored for an industrial six-stage fixed-bed reactor. Through a rigorous sensitivity analysis, we investigated the impact of catalyst particle size on bed pressure drops and product distribution, ultimately identifying an optimal particle size range between 2.5-3.5 mm. Subsequently, we developed a hybrid optimization algorithm that seamlessly integrates a stochastic algorithm with a deterministic algorithm. A key component of our methodology was the utilization of the orthogonal collocation method on finite elements (OCFE), which effectively converted differential equations (DEs) into algebraic equations (AEs), simplifying the optimization process. The results show that methanol throughput increases by 4.75 %, and propylene selectivity increases from 65.34 % to 73.98 %.

关键词： MTP process Particle design hybrid algorithm Reaction optimization

来源：评论

学校读者我要写书评

暂无评论

Enhancing LightGBM for Industrial Fault Warning: An Innovative hybrid algorithm

引用

PROCESSES 2024年第1期12卷 221页

作者： Li, Shuai Jin, Nan Dogani, Azadeh Yang, Yang Zhang, Ming Gu, Xiangyun 76 Chongmingdao East Rd Qingdao 266000 Peoples R China Ferdowsi Univ Mashhad Fac Agr Dept Agr Econ Mashhad *** Iran

The reliable operation of industrial equipment is imperative for ensuring both safety and enhanced production efficiency. Machine learning technology, particularly the Light Gradient Boosting Machine (LightGBM), has emerged as a valuable tool for achieving effective fault warning in industrial settings. Despite its success, the practical application of LightGBM encounters challenges in diverse scenarios, primarily stemming from the multitude of parameters that are intricate and challenging to ascertain, thus constraining computational efficiency and accuracy. In response to these challenges, we propose a novel innovative hybrid algorithm that integrates an Arithmetic Optimization algorithm (AOA), Simulated Annealing (SA), and new search strategies. This amalgamation is designed to optimize LightGBM hyperparameters more effectively. Subsequently, we seamlessly integrate this hybrid algorithm with LightGBM to formulate a sophisticated fault warning system. Validation through industrial case studies demonstrates that our proposed algorithm consistently outperforms advanced methods in both prediction accuracy and generalization ability. In a real-world water pump application, the algorithm we proposed achieved a fault warning accuracy rate of 90%. Compared to three advanced algorithms, namely, Improved Social Engineering Optimizer-Backpropagation Network (ISEO-BP), Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN), and Grey Wolf Optimizer-Light Gradient Boosting Machine (GWO-LightGBM), its Root Mean Square Error (RMSE) decreased by 7.14%, 17.84%, and 13.16%, respectively. At the same time, its R-Squared value increased by 2.15%, 7.02%, and 3.73%, respectively. Lastly, the method we proposed also holds a leading position in the success rate of a water pump fault warning. This accomplishment provides robust support for the timely detection of issues, thereby mitigating the risk of production interruptions.

关键词： fault warning LightGBM hybrid algorithm hyperparameter optimization Arithmetic Optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：