检索结果-内蒙古大学图书馆

2016联合国际信息技术、机械与电子工程国际会议

作者： Zhanjun Liu Faculty of Aerospace Engineering Shenyang Aerospace University

ISBN: (纸本)9781510830028

The chaos optimization algorithm and immune algorithm are combination of their respective characteristics, which is a chaos immune algorithm. The test results show that the algorithm not only keep s population diversity, also better and faster convergence speed and search ability. The influence cause of boron steel sheet on size precision is studied, which is contained with additional drawing stress, emergency ratio, board deep, plastic modulus, subdued point, sclerotic index, crushing force, curvature radius and so *** order to lay the further foundations of optimizing design, it is analyzed that the latent relation of influence cause of boron steel sheet on size precision by orthogonal experiment. It is pointed orderly out that the influences with rebound result of certain automobile back wheel casing are that sheet crushing force occupies the first place, sheet thick comes second, and sclerotic index comes third, emergency ratio and curvature radius in order.

关键词： size precision boron steel optimization algorithm chaos immune

来源：评论

学校读者我要写书评

暂无评论

An Evaluation of optimization algorithms for the Optimal Selection of GNSS Satellite Subsets

引用

REMOTE SENSING 2024年第10期16卷 1794-1794页

作者： Alluhaybi, Abdulaziz Psimoulis, Panos Remenyte-Prescott, Rasa Univ Nottingham Nottingham Geospatial Inst Nottingham NG7 2TU England Taibah Univ Dept Civil Engn Madinah 42353 Saudi Arabia Univ Nottingham Dept Civil Engn Nottingham NG7 2RD England

Continuous advancements in GNSS systems have led, apart from the broadly used GPS, to the development of other satellite systems (Galileo, BeiDou, GLONASS), which have significantly increased the number of available satellites for GNSS positioning applications. However, despite GNSS satellites' redundancy, a potential poor GNSS satellite signal (i.e., low signal-to-noise ratio) can negatively affect the GNSS's performance and positioning accuracy. On the other hand, selecting high-quality GNSS satellite signals by retaining a sufficient number of GNSS satellites can enhance the GNSS's positioning performance. Various methods, including optimization algorithms, which are also commonly adopted in artificial intelligence (AI) methods, have been applied for satellite selection. In this study, five optimization algorithms were investigated and assessed in terms of their ability to determine the optimal GNSS satellite constellation, such as Artificial Bee Colony optimization (ABC), Ant Colony optimization (ACO), Genetic algorithm (GA), Particle Swarm optimization (PSO), and Simulated Annealing (SA). The assessment of the optimization algorithms was based on two criteria, such as the robustness of the solution for the optimal satellite constellation and the time required to find the solution. The selection of the GNSS satellites was based on the weighted geometric dilution of precision (WGDOP) parameter, where the geometric dilution of precision (GDOP) is modified by applying weights based on the quality of the satellites' signal. The optimization algorithms were tested on the basis of 24 h of tracking data gathered from a permanent GNSS station, for GPS-only and multi-GNSS data (GPS, GLONASS, and Galileo). According to the comparison results, the ABC, ACO, and PSO algorithms were equivalent in terms of selection accuracy and speed. However, ABC was determined to be the most suitable algorithm due it requiring the fewest number of parameters to be set. To further investiga

关键词： GNSS GPS satellite selection WGDOP CNR-WGDOP optimization algorithm ABC ACO GA PSO SA

来源：评论

学校读者我要写书评

暂无评论

Multi-objective network planning optimization algorithm: human exposure, power consumption, cost, and capacity

引用

WIRELESS NETWORKS 2015年第3期21卷 841-857页

作者： Liu, Ning Plets, David Goudos, Sotirios K. Martens, Luc Joseph, Wout Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610054 Peoples R China Univ Ghent iMinds WiCa Dept Informat Technol B-9050 Ghent Belgium Aristotle Univ Thessaloniki Dept Phys Radiocommun Lab Thessaloniki 54124 Greece

Due to the huge popularity of wireless networks, future designs will not only consider the provided capacity, but also the induced exposure, the corresponding power consumption, and the economic cost. As these requirements are contradictory, it is not straightforward to design optimal wireless networks. Those contradicting demands have to satisfy certain requirements in practice. In this paper, a combination of two algorithms, a genetic algorithm and a quasi-particle swarm optimization, is developed, yielding a novel hybrid algorithm that generates further optimizations of indoor wireless network planning solutions, which is named hybrid indoor genetic optimization algorithm. The algorithm is compared with a heuristic network planner and composite differential evolution algorithm for three scenarios and two different environments. Results show that our hybrid-algorithm is effective for optimization of wireless networks which satisfy four demands: maximum coverage for a user-defined capacity, minimum power consumption, minimal cost, and minimal human exposure.

关键词： Indoor wireless network planning optimization algorithm WiFi (IEEE 802.11n) Coverage Power consumption Exposure

来源：评论

学校读者我要写书评

暂无评论

Uniaxial Compressive Strength Prediction for Rock Material in Deep Mine Using Boosting-Based Machine Learning Methods and optimization algorithms

引用

Computer Modeling in Engineering & Sciences 2024年第7期140卷 275-304页

作者： Junjie Zhao Diyuan Li Jingtai Jiang Pingkuang Luo School of Resources and Safety Engineering Central South UniversityChangsha410083China

Traditional laboratory tests for measuring rock uniaxial compressive strength(UCS)are tedious and *** is a pressing need for more effective methods to determine rock UCS,especially in deep mining environments under high in-situ ***,this study aims to develop an advanced model for predicting the UCS of rockmaterial in deepmining environments by combining three boosting-basedmachine learning methods with four optimization *** this purpose,the Lead-Zinc mine in Southwest China is considered as the case *** density,P-wave velocity,and point load strength index are used as input variables,and UCS is regarded as the ***,twelve hybrid predictive models are *** mean square error(RMSE),mean absolute error(MAE),coefficient of determination(R2),and the proportion of the mean absolute percentage error less than 20%(A-20)are selected as the evaluation *** results showed that the hybridmodel consisting of the extreme gradient boostingmethod and the artificial bee colony algorithm(XGBoost-ABC)achieved satisfactory results on the training dataset and exhibited the best generalization performance on the testing *** values of R2,A-20,RMSE,and MAE on the training dataset are 0.98,1.0,3.11 MPa,and 2.23MPa,*** highest values of R2 and A-20(0.93 and 0.96),and the smallest RMSE and MAE values of 4.78 MPa and 3.76MPa,are observed on the testing *** proposed hybrid model can be considered a reliable and effective method for predicting rock UCS in deep mines.

关键词： Uniaxial compression strength strength prediction machine learning optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Application and effect evaluation of different optimization algorithms in distributed hydrological model

引用

JOURNAL OF HYDROLOGY-REGIONAL STUDIES 2025年 58卷

作者： Wu, Ming Gao, Yuqin Liu, Yunping Xu, Longsheng Gao, Li Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210024 Peoples R China

Study Region: The upper reaches of the Shaying River Basin (the USR Basin) in the Huai River Basin, China Study Focus: The calibration of model parameters is one of the key challenges in advancing the application of hydrological models. The study proposes a novel metaheuristic optimization algorithm, the chaotic particle swarm genetic algorithm (CPSGA), and compares its performance with four well-known optimization algorithms in the field of hydrological model calibration: GA, PSO, DE, and SCE-UA. The comparison focuses on effectiveness, stability, time consumption, and convergence characteristics. New Hydrological Insights for the Region: During the parameter calibration process of runoff simulation in the USR Basin, CPSGA demonstrates strong effectiveness and convergence characteristics. It enhances the GA framework by integrating initial population chaotization, perturbation evolution, and sub-adaptation strategies, which improve individual diversity and facilitate targeted evolution, thereby increasing effectiveness and convergence. However, these enhancements compromise stability and increase time consumption compared to other algorithms. While PSO shows the best convergence characteristics, it suffers from reduced swarm diversity in later iterations, leading to local optima and poor effectiveness. The complex concept and competitive complex evolution (CCE) strategy of SCE-UA make it less effective for optimization problems with a considerable number of variables, limiting its suitability for calibrating fully distributed hydrological models. These results can provide reference for parameter calibration and uncertainty analysis in distributed hydrological models.

关键词： optimization algorithm Chaotic particle swarm genetic algorithm Distributed hydrological model Parameter calibration Performance evaluation

来源：评论

学校读者我要写书评

暂无评论

Wave spectrum fitting with multiple parameters based on optimization algorithms and its application

引用

OCEAN ENGINEERING 2024年第Part1期312卷

作者： Wu, Feng Zhu, Li Zhao, Yuelin Ai, Congfang Wang, Xiao Cai, Feng Wang, Dazhi Suo, Liujia Yan, Jun Univ Technol Sch Mech & Aerosp Engn State Key Lab Struct Anal Optimizat & CAE Software Ind Equipment Dalian 116024 Liaoning Peoples R China Dalian Univ Technol State Key Lab Coastal & Offshore Engn Dalian 116024 Liaoning Peoples R China Dalian Naval Acad Dept Nav Dalian 116018 Liaoning Peoples R China Dalian Univ Technol Sch Mech Engn Key Lab Micro Nano Technol & Syst Liaoning Prov Dalian 116024 Liaoning Peoples R China

In the realm of ocean engineering and environmental research, the accuracy of the wave spectrum is paramount. Traditional models, constrained by the number of parameters, often struggle to accurately capture the intricacies of real-world wave spectra, especially when confronting the complexities of multi-peak spectral shapes shaped by wind-sea and swell systems. This research develops a novel multi-parameter wave spectrum model that substantially amplifies the capacity to describe the diversity of wave spectra by introducing more shape parameters. The proposed model includes more shape parameters than its predecessors. These parameters can be directly optimized and obtained through the Heterogeneous Comprehensive Learning Particle Swarm Optimizer (HCLPSO). This sophisticated algorithm leverages its robust global search capabilities and swift convergence to fine-tune the model's parameters, ensuring a high degree of precision in fitting the measured wave spectra. Furthermore, the proposed model also shows great potential in wave spectrum prediction, even when there are fewer training samples, it can still yield relatively accurate predictive results. This research not only improves the applicability and accuracy of the wave spectrum model but also offers a new tool for ocean wave research and real-world applications, thereby contributing important research and practical value to the field.

关键词： Wave spectrum optimization algorithm Wave spectrum prediction HCLPSO

来源：评论

学校读者我要写书评

暂无评论

Compressor airfoil optimization method driven by data-mechanism integration based on evolutionary multi-tasking algorithm

引用

AEROSPACE SCIENCE AND TECHNOLOGY 2024年 148卷

作者： Cheng, Jinxin Zhang, Yong Chen, Jiang Ma, Hui Liu, Beiying Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China China Univ Min & Technol Sch Informat & Control Engn Xuzhou 221008 Peoples R China Beihang Univ Sch Energy & Power Engn Beijing 100191 Peoples R China Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China

To address the challenge of the "curse of dimensionality" in aerodynamic design optimization of compressors, this study introduces an innovative optimization technique suitable for compressor airfoil design. This technique, rooted in a hybrid mechanism-data-driven approach, seamlessly integrates a hierarchical parameterization method, based on elliptic topological deformation, into a multitasking evolutionary algorithm framework. This integration deviates from the conventional approach of treating parameterization methods and optimization algorithms as distinct elements. The proposed method positions airfoil parameterization as its core, constructing two tasks within the optimization algorithm. It leverages the critical influence of the parameterization method on the aerodynamic performance landscape of the airfoils and the intrinsic qualities of the hierarchical parameterization method in the design space. The multitasking evolutionary optimization framework facilitates effective information exchange between tasks, significantly boosting optimization efficiency. In comparison to standard data-driven multitasking evolutionary algorithms, the proposed method achieves superior optimized solutions with merely 11 x D aerodynamic performance evaluations, where D denotes the number of design variables.

关键词： Hybrid driven optimization Multitasking evolutionary optimization algorithm Curse of dimensionality

来源：评论

学校读者我要写书评

暂无评论

optimization of Orthogonal Waveform Using Memetic algorithm with Iterative Greedy Code Search

引用

REMOTE SENSING 2025年第5期17卷 856-856页

作者： Wang, Wanbin Qian, Lu Zhou, Yun Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

The orthogonality of transmitted waveforms is an important factor affecting the performance of MIMO radar systems. The orthogonal coded signal is a commonly adopted waveform in MIMO radar, and its orthogonality depends on the used orthogonal discrete code sequence set (ODCSs). Among existing optimization algorithms for ODCSs, the results designed by the greedy code search-based memetic algorithm (MA-GCS) have exhibited the best autocorrelation and cross-correlation properties observed so far. Based on MA-GCS, we propose a novel hybrid algorithm called the memetic algorithm with iterative greedy code search (MA-IGCS). Extensions involve replacing the greedy code search used in MA-GCS with a more efficient approach, iterative greedy code search. Furthermore, we propose an "individual uniqueness strategy" and incorporate it into our algorithm to preserve population diversity throughout iteration, thereby preventing premature stagnation and ensuring the continued pursuit of feasible solutions. Finally, the design results of our algorithm are compared with the MA-GCS. Experimental results demonstrate that the MA-IGCS exhibits superior search capability and generates more favorable design results than the MA-GCS.

关键词： radar orthogonal waveforms optimization algorithm orthogonal discrete code sequence set (ODCSs) iterative greedy code search (IGCS)

来源：评论

学校读者我要写书评

暂无评论

XAI-BO: an architecture using Grad-CAM technique to evaluate Bayesian optimization algorithms on deep learning models

引用

JOURNAL OF INFORMATION AND TELECOMMUNICATION 2025年

作者： Quach, Luyl-Da Khang, Nguyen Quoc Thai-Nghe, Nguyen Nguyen, Chi-Ngon Can Tho Univ Campus 23-2 St Can Tho Vietnam FPT Univ Can Tho Vietnam

The Bayesian optimization algorithm, proposed in the 1980s, is known for its advantages in optimizing parameters and conserving system resources. However, the specific application of this algorithm in various deep-learning models has not been thoroughly evaluated. This study proposes an experimental model to assess the dependency on reducing hyperparameters in Dense layers and dropout layers, as well as the performance and accuracy across 13 deep learning models, implemented on two datasets with different characteristics and sample sizes (Cucumber disease recognition dataset and tomato physiological states dataset). Additionally, the research utilizes Grad-CAM to clarify the positive impact of parameter reduction when using the BO algorithm. Through evaluations based on hyperparameter data, performance by each iteration, accuracy, and model explainability, the study demonstrates that the BO algorithm optimizes hyperparameters through selective processes. However, it significantly impacts the model's accuracy. This is particularly evident in the InceptionResNetV2 and NasNetMobile models. The research results contribute to a clearer understanding of the impact of optimization algorithms on deep learning models, opening up new research directions in optimizing and elucidating models through the use of explainable artificial intelligence.

关键词： optimization algorithm disease classification Bayesian optimization hyperparameter tuning Grad-CAM

来源：评论

学校读者我要写书评

暂无评论

An LQR-Lagrange algorithm Generated by Interdisciplinary Integration With Optimal Control and optimization

引用

OPTIMAL CONTROL APPLICATIONS & METHODS 2025年第2期46卷 615-633页

作者： An, Molin Han, Xueshan Shandong Univ Sch Elect Engn Jinan Peoples R China

Interdisciplinary integration is a superior method to improve the optimization algorithm. In this paper, control theory and optimization are combined, and the optimization algorithm is regarded as a control process. Based on the premise of optimal control, the state equation corresponding to Lagrange algorithm is established with the Karush-Kuhn-Tucker (KKT) conditions as the objective. As an optimal control method, linear quadratic regulator (LQR) is utilized to control the calculation process, and an innovative LQR-Lagrange algorithm is proposed. The Lyapunov stability criterion is applied to analyze the convergence, and it is proved that the proposed LQR-Lagrange algorithm is bound to converge as long as the parameter matrices Q and R are positive definite. The analysis indicates that the influence of parameters in LQR-Lagrange algorithm on the calculation speed is monotonic, and the elements in Q and R has no effect on the convergence. Therefore, the proposed algorithm has a monotonic and user-friendly parameter tuning strategy. It perfectly tackles the game between parameter tuning strategy and calculation speed, and cracks the difficulties and dilemmas of conventional algorithms in this issue, realizing a win-win situation.

关键词： interdisciplinary integration LQR-Lagrange algorithm monotonic and user-friendly optimal control 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：