检索结果-内蒙古大学图书馆

Multi-objective generalized normal distribution optimization: a novel algorithm for multi-objective problems

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第8期27卷 10589-10631页

作者： Khodadadi, Nima Khodadadi, Ehsan Abdollahzadeh, Benyamin EI-Kenawy, El-Sayed M. Mardanpour, Pezhman Zhao, Weiguo Gharehchopogh, Farhad Soleimanian Mirjalili, Seyedali Univ Miami Dept Civil & Architectural Engn Coral Gables FL 33146 USA Univ Arkansas Dept Chem & Biochem Fayetteville AR 72701 USA Islamic Azad Univ Dept Comp Engn Urmia Branch Orumiyeh Iran Delta Higher Inst Engn & Technol DHIET Dept Commun & Elect Mansoura 35111 Egypt Florida Int Univ Dept Mech & Mat Engn Miami FL USA Hebei Univ Engn Sch Water Conservancy & Hydropower Handan 056038 Hebei Peoples R China Yonsei Univ YFL Yonsei Frontier Lab Seoul South Korea Obuda Univ Res & Innovat Ctr H-1034 Budapest Hungary Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Qld Australia

This study introduces the Multi-objective generalized normal distribution optimization (MOGNDO) algorithm, an advancement of the generalized normal distribution optimization (GNDO) algorithm, now adapted for multi-objective optimization tasks. The GNDO algorithm, previously known for its effectiveness in single-objective optimization, has been enhanced with two key features for multi-objective optimization. The first is the addition of an archival mechanism to store non-dominated Pareto optimal solutions, ensuring a detailed record of the best outcomes. The second enhancement is a new leader selection mechanism, designed to strategically identify and select the best solutions from the archive to guide the optimization process. This enhancement positions MOGNDO as a cutting-edge solution in multi-objective optimization, setting a new benchmark for evaluating its performance against leading algorithms in the field. The algorithm's effectiveness is rigorously tested across 35 varied case studies, encompassing both mathematical and engineering challenges, and benchmarked against prominent algorithms like MOPSO, MOGWO, MOHHO, MSSA, MOALO, MOMVO, and MOAOS. Utilizing metrics such as Generational Distance (GD), Inverted Generational Distance (IGD), and Maximum Spread (MS), the study underscores MOGNDO's ability to produce Pareto fronts of high quality, marked by exceptional precision and diversity. The results affirm MOGNDO's superior performance and versatility, not only in theoretical tests but also in addressing complex real-world engineering problems, showcasing its high convergence and coverage capabilities. The source codes of the MOGNDO algorithm are publicly available at https://***/algorithms-%2B-codes.

关键词： generalized normal distribution optimization Multi-objective GNDO Engineering problems

来源：评论

学校读者我要写书评

暂无评论

Dynamic generalized normal distribution optimization for feature selection

引用

NEURAL COMPUTING & APPLICATIONS 2022年第20期34卷 17355-17370页

作者： Tubishat, Mohammad Rawshdeh, Zainab Jarrah, Hazim Elgamal, Zenab Mohamed Elnagar, Ashraf Alrashdan, Maen T. Zayed Univ Coll Technol Innovat Abu Dhabi U Arab Emirates Prince Sultan Univ Coll Business Adm Riyadh Saudi Arabia Skyline Univ Coll Sch Informat Technol Sharjah U Arab Emirates Univ Malaya Fac Comp Sci & Informat Technol Kuala Lumpur Malaysia Univ Sharjah Dept Comp Sci Sharjah U Arab Emirates Jadara Univ Fac Sci & Informat Technol Irbid Jordan

High dimensionality of data represents a major problem that affects the accuracy of the classification. This problem related with classification is mainly resulted from the availability of irrelevant features. Feature selection represents a solution to a problem by selecting the most informative features and discard the irrelevant features. generalized normal distribution optimization (GNDO) represents a newly developed optimization that confirmed its outperformance in comparison with well-known optimization algorithms on parameter extraction for photovoltaic models. As an optimization algorithm, however, GNDO suffers from degraded performance when dealing with a problem with a high dimensionality. The main problems of GNDO include exploitation problem by falling into local optima problem. Also, GNDO has solutions diversity problem when it deals with data with high dimensionality. To alleviate the drawbacks of this algorithm and solve feature selection problems, a local search algorithm (LSA) is used. The new algorithm is called dynamic generalized normal distribution optimization (DGNDO), which includes the following main improvements to GNDO: it can improve the best solution to solve the local optima problem, it can improve solution diversity by improving the randomly selected solution, and it can improve both exploration and exploitation combined. To confirm the outperformance and efficiency of the new DGNDO algorithm, DGNDO algorithm is applied on 20 benchmarked datasets from UCI repository of data. In addition, DGNDO algorithm results are compared with seven well-known optimization algorithms using number of evaluation metrics including classification, accuracy, fitness, the number of selected features, statistical results using Wilcoxon test and convergence curves. The obtained results reveal the superiority of DGNDO algorithm over all other competing algorithms.

关键词： generalized normal distribution optimization Feature selection optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

An improved generalized normal distribution optimization and its applications in numerical problems and engineering design problems

引用

ARTIFICIAL INTELLIGENCE REVIEW 2023年第1期56卷 685-747页

作者： Zhang, Yiying Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Jiangsu Peoples R China

generalized normal distribution optimization (GNDO) inspired by the theory of normal distribution is a recently developed metaheuristic method for global optimization problems. This work presents a novel variant of GNDO, which is called elite-driven generalized normal distribution optimization (EDGNDO). EDGNDO enhances the global search ability of GNDO by the designed search mechanism consisting of three local search operators and three global search operators that are based on two built archives used to save elite individuals. Note that, EDGNDO only needs population size and termination criteria for optimization, which can distinguish it over the most reported metaheuristic methods. The performance of EDGNDO is investigated by the well-known CEC 2017 test suite including three unimodal functions and 27 multimodal functions. Experimental results demenstrate that EDGNDO is obviously better than GNDO and the other five powerful algorithms in terms of solution quality and computational efficiency. In addition, EDGNDO is also used for solving four challenging constrained engineering design problems. Experimental results support the superiority of EDGNDO in solving the four problems. The superiority of EDGNDO in solving complex optimization problems is proven. The source code can be loaded from https://***/jsuzyy/EDGNDO.

关键词： generalized normal distribution optimization Elite-driven Global optimization Engineering design

来源：评论

学校读者我要写书评

暂无评论

optimization of route planning based on active towed array sonar for underwater search and rescue

引用

OCEAN ENGINEERING 2025年 330卷

作者： Liao, Siyuan Xiao, Wenbin Wang, Yongxian Natl Univ Def Technol Coll Meteorol & Oceanog Changsha 410073 Hunan Peoples R China

In order to meet the underwater search requirements in deep sea, an route planning method based on active towed array sonar (ATAS) for underwater search and rescue is proposed in this paper. Firstly, the 3D ocean acoustic environment is modeled based on the hydrographic data and the global bathymetry data. Secondly, the detection distances of ATAS in the mission area are accurately evaluated based on the sonar equation. In particular, we introduce hybrid parallel computing, which accelerates the transmission loss computation by a factor of 202.69. Thirdly, to overcome the sampling sparsity of environmental parameters, an improved inverse distance weight interpolation method based on the ordinary least squares theory is proposed, which makes the correlation coefficient between the predicted value and the real reaches 0.945. Finally, an USAR route planning model based on the Lawnmower search method is established, and the generalized normal distribution optimization (GNDO) algorithm is introduced. In the test of the Atlantic Ocean (29,818 km2), the uncovered rate can be reduced from 34.39% to 10.13% (single-vessel search) and from 14.68% to 0.07% (two-vessels search) by GNDO in sub-second, whose performance is better than five popular heuristic algorithms.

关键词： Active towed array sonar Route planning Hybrid parallel computing generalized normal distribution optimization

来源：评论

学校读者我要写书评

暂无评论

Design of transmission line towers by metaheuristic algorithms: A comparative study

引用

JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH 2025年 228卷

作者： Ogata, Marcos Wilson Gomes, Herbert Martins de Almeida, Felipe Schaedler Univ Fed Rio Grande do Sul Post Grad Program Civil Engn Ave Osvaldo Aranha993 Andar BR-90035190 Porto Alegre RS Brazil Univ Rio Grande do Sul Dept Mech Engn Program Mech Engn Ave Sarmento Leite4252 AndarSala 202 BR-90150170 Porto Alegre RS Brazil Univ Fed Rio Grande do Sul Dept Civil Engn Ave Osvaldo Aranha993 Andar BR-90035190 Porto Alegre RS Brazil

Latticed towers are essential components of high-voltage transmission lines. Saving resources in their construction is crucial for economic and environmental efficiency, but the safety of these structures under severe loads is of major importance for the reliability of the power distribution system. This work explores optimization techniques to improve the structural design of transmission line towers according to international code specifications and considering loads due to construction, wind, and cable rupture. A self-supporting tower with 52 m high and 1721 members is designed using the proposed methodology, showing that the computational tool obtains competitive solutions. Four metaheuristic algorithms are applied to solve the problem and compared in terms of efficiency and computational cost in extensive tests with more than 1080 independent optimizations with different parameter settings. The generalized normal distribution optimization greatly outperformed the other methods, raising as a good alternative for the optimization-aided structural design.

关键词： Structural optimization Transmission line towers generalized normal distribution optimization

来源：评论

学校读者我要写书评

暂无评论

Estimating the Maximum Dry Density of Soil via Least Square Support Vector Regression Individual and Hybrid Forms

引用

INDIAN GEOTECHNICAL JOURNAL 2025年第2期55卷 866-878页

作者： Zhao, Qiuduo Liu, Ke Xiong, Chen Deng, Xing Yang, Saifei Heilongjiang Univ Sci & Technol Coll Elect & Informat Engn Harbin 150022 Heilongjiang Peoples R China

Maximum dry density (MDD) holds pivotal importance in geotechnical engineering as it signifies the ideal soil mass per unit volume given particular circumstances. It is important in determining the stability and effectiveness of various earthworks, such as embankments and foundations. MDD is subject to variation based on factors including soil type, distribution of grain sizes, level of compaction, and moisture content. Typically, increasing compaction efforts result in higher MDD values, leading to a denser structure, while elevated moisture levels tend to decrease it. The precise estimation of MDD is indispensable for engineers to make well-founded decisions, ensuring the longevity and safety of civil engineering structures over time. This paper introduces a novel method for predicting maximum dry density (MDD) by utilizing the least square support vector regression (LSSVR) algorithm. The approach involves utilizing the LSSVR technique to develop precise models that connect the MDD of stabilized soil with several intrinsic soil characteristics such as particle size distribution, plasticity, linear shrinkage, and the composition and amount of stabilizing agents used. In this study, a comprehensive dataset comprising 187 samples of various soil types sourced from previously published stabilization test results is utilized to formulate and evaluate the predictive models. Furthermore, the accuracy of the LSSVR model in this research is augmented through the incorporation of meta-heuristic techniques, specifically Leader Harris Hawk's optimization (LHHO) and generalized normal distribution optimization (GNDO). The R-2 values for the training, validation, and testing data for the LSLH model were 99.55%, 98.51%, and 99.32%, respectively. Additionally, LSLH had the most suitable RMSE of 15.72 kN/m(3). Generally, the LSLH model demonstrated acceptable predictive and generalization capabilities compared to the LSGN model developed in this study.

关键词： Maximum dry density Least square support vector regression Leader Harris Hawk's optimization generalized normal distribution optimization

来源：评论

学校读者我要写书评

暂无评论

Developing support vector regression and radial basis function neural networks in optimized holistic frameworks to estimate hardness properties of concrete

引用

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第5期45卷 8349-8364页

作者： Wang, Chuncha Fujian Chuanzheng Commun Coll Fuzhou Peoples R China

The hardness properties of constructional materials should be investigated as important factors in assessing the performance over the operation period. Two tests are performed to determine the stiffness characteristic, including slump and compressive strength (CS). They must be considered to examine efficiency, durability, and resistance to pressure. Due to the structure's susceptibility and usage in dams, bridges, etc., high-performance concrete must have an appropriate set of these tests. There are two soft-based and laboratory methods for performing these tests. The laboratory method is not economical in terms of cost and time, and artificial intelligence (AI) is used to reduce the aforementioned factors. Models and optimizers use software-based methods to help reduce errors and increase model accuracy. So, The main purpose of this research has been introducing novel ways of coupling an ensemble model with optimizers by adjusting some internal parameters. In this article, two models, the Radial Basis Function Neural network and Support Vector Regression were combined and coupled with General normal distribution optimization (GNDO) and Archimedes optimization algorithm (AOA) into the two frameworks of SVRRBF-AOA and SVRRBF-GNDO. As a result, the hybrid model of SVRRBF-AOA could perform well by obtaining R-2 and RMSE of 0.9915 and 2.71 for the slump and 0.9845 and 3.34 for CS, respectively.

关键词： High-performance concrete slump compressive strength support vector regression Radial basis function generalized normal distribution optimization archimedes optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Development and analysis of AC optimal power flow optimization algorithms for minimization of cost and emissions with stochastic renewables

引用

ENERGY REPORTS 2024年 11卷 2059-2076页

作者： Barnawi, Abdulwasa Bakr King Khalid Univ Coll Engn Dept Elect Engn Abha Saudi Arabia

Economic growth, population rise, and industrialization have increased the daily energy demand. The rapid depletion of fossil fuels and the adverse effects of greenhouse gas (CO2, NOx) emissions from huge conventional power plants, forced the environmentalists and power system engineers to come up with an alternative solution for more reliable and economical generation and distribution of electricity. The alternating current optimal power flow (AC-OPF) problem is an effective tool for analysis, which involves the adjustment of certain controllable variables to optimize objective functions while satisfying operational, physical, and security limits on various controls and dependent variables. This paper focuses on analyzing the optimal power flow operation of a system with renewable integration, taking into account more popular wind and solar resources along with electric vehicles and tidal energy systems. In this context, proposed a model of the optimal power flow problem incorporating thermal-hydro-solar-wind-tidal-EV energy systems. Probability distribution functions (Weibull, Gumbel, and Lognormal) are used for renewable uncertainties and to determine predictable wind and solar power for proper generation scheduling. Furthermore, this paper provides solutions to the optimal power flow problem by including test cases of stochastic renewables involving minimizing the cost of power generation, minimizing emissions from fossil-fuelled plants, minimizing losses in the system, maximizing profit, and maximizing voltage stability. The generalized normal distribution optimization algorithm is applied to handle the above-mentioned AC-OPF problems in contingency conditions. The proposed method is employed on a modified IEEE 30-bus system integrated with RESs to assess the accuracy and performance. Statistical analysis and comparison of the results establish the superiority and robustness of the proposed algorithm over other available techniques in the literature. Simulation

关键词： Electric vehicles Emission generalized normal distribution optimization Hydro Power Optimal power flow Probability distribution function Solar energy Tidal energy Wind energy

来源：评论

学校读者我要写书评

暂无评论

Medical Feature Selection Approach Based on generalized normal distribution Algorithm

引用

Computers, Materials & Continua 2021年第12期69卷 2883-2901页

作者： Mohamed Abdel-Basset Reda Mohamed Ripon K.Chakrabortty Michael J.Ryan Yunyoung Nam Mohamed Abouhawwash Faculty of Computers and Informatics Zagazig UniversityZagazig44519Egypt Capability Systems Centre School of Engineering and ITUNSWCanberraAustralia Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast Lansing48824MIUSA

This paper proposes a new pre-processing technique to separate the most effective features from those that might deteriorate the performance of the machine learning classifiers in terms of computational costs and classification accuracy because of their irrelevance,redundancy,or less information;this pre-processing process is often known as feature *** technique is based on adopting a new optimization algorithm known as generalized normal distribution optimization(GNDO)supported by the conversion of the normal distribution to a binary one using the arctangent transfer function to convert the continuous values into binary ***,a novel restarting strategy(RS)is proposed to preserve the diversity among the solutions within the population by identifying the solutions that exceed a specific distance from the best-so-far and replace them with the others created using an effective updating *** strategy is integrated with GNDO to propose another binary variant having a high ability to preserve the diversity of the solutions for avoiding becoming stuck in local minima and accelerating convergence,namely improved GNDO(IGNDO).The proposed GNDO and IGNDO algorithms are extensively compared with seven state-of-the-art algorithms to verify their performance on thirteen medical instances taken from the UCI *** is shown to be superior in terms of fitness value and classification accuracy and competitive with the others in terms of the selected *** the principal goal in solving the FS problem is to find the appropriate subset of features that maximize classification accuracy,IGNDO is considered the best.

关键词： generalized normal distribution optimization feature selection transfer function novel restarting strategy UCI repository

来源：评论

学校读者我要写书评

暂无评论

An explanatory machine learning model for forecasting compressive strength of high-performance concrete

引用

MULTISCALE AND MULTIDISCIPLINARY MODELING EXPERIMENTS AND DESIGN 2024年第1期7卷 543-555页

作者： Yan, Guifeng Wu, Xu Zhang, Wei Bao, Yuping Nantong Inst Technol Dept BIM Res Nantong 226002 Jiangsu Peoples R China

High-performance concrete (HPC) is one of the concrete types with high strength, good performance, and high durability, which has been considered in the structural industry. Testing and sampling this type of concrete to determine its mechanical properties is daunting and complex. In addition, human and environmental factors were significant in the preparation of samples, which was also time-consuming and energy-consuming. Artificial intelligence (AI) can be used to eliminate and reduce these factors. This article intends to use the machine learning (ML) method to forecast one of the HPC mechanical properties: compressive strength (CS). The experimental data set used from the published article includes 168 samples, of which 70% (118) of the sample belonged to training and 30% (50) to the testing phase. Least square support vector regression (LSSVR) is one of the ML models used for forecasting in this article. In addition, meta-heuristic algorithms have been utilized to obtain the target to improve the accuracy and reduce the error. Algorithms include Honey Badger algorithm (HBA), COOT optimization algorithm (COA), and generalized normal distribution optimization (GNDO). Combining the algorithms with the introduced model forms a hybrid format evaluated by metrics in the training and testing phases. By evaluating the hybrid models, it has been determined that they can forecast with high accuracy and are reliable. In general, the LSHB hybrid model obtained the highest R2 and the lowest error compared to other models.

关键词： High-performance concrete Compressive strength Least square support vector regression Honey Badger algorithm COOT optimization algorithm generalized normal distribution optimization

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：