检索结果-南通市图书馆

Gaussian Backbone-Based Spherical Evolutionary Algorithm with Cross-search for Engineering Problems

在线全文

学校读者我要写书评

暂无评论

Journal of Bionic Engineering 2024年第2期21卷 1055-1091页

作者： Yupeng Li Dong Zhao Ali Asghar Heidari Shuihua Wang Huiling Chen Yudong Zhang College of Computer Science and Technology Changchun Normal UniversityChangchun130032JilinChina School of Surveying and Geospatial Engineering College of EngineeringUniversity of TehranTehranIran School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Biological Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou215123JiangsuChina Key Laboratory of Intelligent Informatics for Safety and Emergency of Zhejiang Province Wenzhou UniversityWenzhou325035China School of Computer Science and Engineering Southeast UniversityNanjing210096China

In recent years,with the increasing demand for social production,engineering design problems have gradually become more and more *** novel and well-performing meta-heuristic algorithms have been studied and developed to cope with this *** them,the Spherical Evolutionary Algorithm(SE)is one of the classical representative methods that proposed in recent years with admirable optimization ***,it tends to stagnate prematurely to local optima in solving some specific ***,this paper proposes an SE variant integrating the Cross-search Mutation(CSM)and Gaussian Backbone Strategy(GBS),called *** this study,the CSM can enhance its social learning ability,which strengthens the utilization rate of SE on effective information;the GBS cooperates with the original rules of SE to further improve the convergence effect of *** objectively demonstrate the core advantages of CGSE,this paper designs a series of global optimization experiments based on IEEE CEC2017,and CGSE is used to solve six engineering design problems with *** final experimental results fully showcase that,compared with the existing well-known methods,CGSE has a very significant competitive advantage in global tasks and has certain practical value in real ***,the proposed CGSE is a promising and first-rate algorithm with good potential strength in the field of engineering design.

关键词： meta-heuristic algorithms Engineering optimization Spherical evolution algorithm Global optimization

Genetic-Frog-Leaping Algorithm for Text Document Clustering

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2019年第9期61卷 1045-1074页

作者： Lubna Alhenak Manar Hosny Computer Science Department College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia

In recent years,the volume of information in digital form has increased tremendously owing to the increased popularity of the World Wide *** a result,the use of techniques for extracting useful information from large collections of data,and particularly documents,has become more necessary and *** clustering is such a technique;it consists in dividing a set of text documents into clusters(groups),so that documents within the same cluster are closely related,whereas documents in different clusters are as different as *** depends on measuring the content(i.e.,words)of a document in terms of ***,as documents usually contain a large number of words,some of them may be irrelevant to the topic under consideration or *** can confuse and complicate the clustering process and make it less ***,feature selection methods have been employed to reduce data dimensionality by selecting the most relevant *** this study,we developed a text document clustering optimization model using a novel genetic frog-leaping algorithm that efficiently clusters text documents based on selected *** proposed approach is based on two metaheuristic algorithms:a genetic algorithm(GA)and a shuffled frog-leaping algorithm(SFLA).The GA performs feature selection,and the SFLA performs *** evaluate its effectiveness,the proposed approach was tested on a well-known text document dataset:the“20Newsgroup”dataset from the University of California Irvine Machine Learning ***,after multiple experiments were compared and analyzed,it was demonstrated that using the proposed algorithm on the 20Newsgroup dataset greatly facilitated text document clustering,compared with classical K-means ***,this improvement requires longer computational time.

关键词： Text documents clustering meta-heuristic algorithms shuffled frog-leaping algorithm genetic algorithm feature selection

An Adaptive Strategy-incorporated Integer Genetic Algorithm for Wind Farm Layout Optimization

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Bionic Engineering 2024年第3期21卷 1522-1540页

作者： Tao Zheng Haotian Li Houtian He Zhenyu Lei Shangce Gao Faculty of Engineering University of ToyamaToyama9300887Japan

Energy issues have always been one of the most significant concerns for scientists *** the ongoing over exploitation and continued outbreaks of wars,traditional energy sources face the threat of *** energy is a readily available and sustainable energy *** farm layout optimization problem,through scientifically arranging wind turbines,significantly enhances the efficiency of harnessing wind ***-heuristic algorithms have been widely employed in wind farm layout *** paper introduces an Adaptive strategy-incorporated Integer Genetic Algorithm,referred to as AIGA,for optimizing wind farm layout *** adaptive strategy dynamically adjusts the placement of wind turbines,leading to a substantial improvement in energy utilization efficiency within the wind *** this study,AIGA is tested in four different wind conditions,alongside four other classical algorithms,to assess their energy conversion efficiency within the wind *** results demonstrate a notable advantage of AIGA.

关键词： Wind farm layout optimization problem meta-heuristic algorithms Adaptive Integer genetic algorithm

Prediction of blasting mean fragment size using support vector regression combined with five optimization algorithms

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Journal of Rock Mechanics and Geotechnical Engineering 2021年第6期13卷 1380-1397页

作者： Enming Li Fenghao Yang Meiheng Ren Xiliang Zhang Jian Zhou Manoj Khandelwal School of Resources and Safety Engineering Central South UniversityChangsha410083China Universidad Politecnica de Madrid-ETSI Minas y Energia Rios Rosas 21Madrid28003Spain College of Locomotive and Rolling Stock Engineering Dalian Jiaotong UniversityDalian116028China Department of Mathematics University of California Santa BarbaraSanta BarbaraCA93106USA State Key Laboratory of Safety and Health for metal Mines Maanshan243000China School of Engineering Information Technology and Physical SciencesFederation University AustraliaBallaratVIC3350Australia

The main purpose of blasting operation is to produce desired and optimum mean size rock *** or fine fragments cause the loss of ore during loading and transportation,whereas large or coarser fragments need to be further processed,which enhances production ***,accurate prediction of rock fragmentation is crucial in blasting *** fragment size(MFS) is a crucial index that measures the goodness of blasting *** the past decades,various models have been proposed to evaluate and predict blasting *** these models,artificial intelligence(AI)-based models are becoming more popular due to their outstanding prediction results for multiinfluential *** this study,support vector regression(SVR) techniques are adopted as the basic prediction tools,and five types of optimization algorithms,*** search(GS),grey wolf optimization(GWO),particle swarm optimization(PSO),genetic algorithm(GA) and salp swarm algorithm(SSA),are implemented to improve the prediction performance and optimize the *** prediction model involves 19 influential factors that constitute a comprehensive blasting MFS evaluation system based on AI *** all the models,the GWO-v-SVR-based model shows the best comprehensive performance in predicting MFS in blasting *** types of mathematical indices,*** square error(MSE),coefficient of determination(R^(2)) and variance accounted for(VAF),are utilized for evaluating the performance of different prediction *** R^(2),MSE and VAF values for the training set are 0.8355,0.00138 and 80.98,respectively,whereas 0.8353,0.00348 and 82.41,respectively for the testing ***,sensitivity analysis is performed to understand the influence of input parameters on *** shows that the most sensitive factor in blasting MFS is the uniaxial compressive strength.

关键词： Blasting mean fragment size e-support vector regression(e-SVR) V-support vector regression(v-SVR) meta-heuristic algorithms Intelligent prediction

A Double Adaptive Random Spare Reinforced Sine Cosine Algorithm

在线全文

学校读者我要写书评

暂无评论

Computer Modeling in Engineering & Sciences 2023年第9期136卷 2267-2289页

作者： Abdelazim G.Hussien Guoxi Liang Huiling Chen Haiping Lin Department of Computer and Information Science Linköing UniversityLinköing4189Sweden Faculty of Science Fayoum UniversityFayoum63514Egypt Department of Information Technology Wenzhou PolytechnicWenzhou325035China Artificial Intelligence Wenzhou UniversityWenzhou325035China College of Information Engineering Hangzhou Vocational&Technical CollegeHangzhou310018China

Many complex optimization problems in the real world can easily fall into local optimality and fail to find the optimal solution,so more new techniques and methods are needed to solve such *** algorithms have received a lot of attention in recent years because of their efficient performance and simple *** Cosine Algorithm(SCA)is a recent metaheuristic algorithm that is based on two trigonometric functions Sine&***,like all other metaheuristic algorithms,SCA has a slow convergence and may fail in sub-optimal *** this study,an enhanced version of SCA named RDSCA is suggested that depends on two techniques:random spare/replacement and double adaptive *** first technique is employed in SCA to speed the convergence whereas the second method is used to enhance exploratory searching *** evaluate RDSCA,30 functions from CEC 2017 and 4 real-world engineering problems are ***,a nonparametric test called Wilcoxon signed-rank is carried out at 5%level to evaluate the significance of the obtained results between RDSCA and the other 5 variants of *** results show that RDSCA has competitive results with other metaheuristics algorithms.

关键词： Sine cosine algorithm global optimization swarm intelligence meta-heuristic algorithms

Imperialistic Competitive Algorithm:A metaheuristic algorithm for locating the critical slip surface in 2-Dimensional soil slopes

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Geoscience Frontiers 2016年第1期7卷 83-89页

作者： Ali Reza Kashani Amir Hossein Gandomi Mehdi Mousavi Department of Civil Engineering Arak University Department of Civil Engineering The University of Akron

In this study, Imperialistic Competitive Algorithm（ICA） is utilized for locating the critical failure surface and computing the factor of safety（FOS） in a slope stability analysis based on the limit equilibrium approach. The factor of safety relating to each trial slip surface is calculated using a simplified algorithm of the Morgenstern-Price method, which satisfies both the force and the moment equilibriums. General slip surface is considered non-circular in this study that is constituted by linking random straight *** explore the performance of the proposed algorithm, four benchmark test problems are analyzed. The results demonstrate that the present techniques can provide reliable, accurate and efficient solutions for locating the critical failure surface and relating FOS. Moreover, in contrast with previous studies the present algorithm could reach the lower value of FOS and reached more exact solutions.

关键词： meta-heuristic algorithms Morgen-stern and price method Non-circular slip surface Imperialistic competitive algorithm

Optimized functional linked neural network for predicting diaphragm wall deflection induced by braced excavations in clays

在线全文

学校读者我要写书评

暂无评论

Geoscience Frontiers 2022年第2期13卷 34-51页

作者： Chengyu Xie Hoang Nguyen Yosoon Choi Danial Jahed Armaghani School of Environment and Resources Xiang tan UniversityXiangtan 411105China Department of Surface Mining Mining FacultyHanoi University of Mining and GeologyDuc ThangBac Tu LiemHanoiViet Nam Innovations for Sustainable and Responsible Mining(ISRM)Group Hanoi University of Mining and GeologyDuc ThangBac Tu LiemHanoiViet Nam Department of Energy Resources Engineering Pukyong National UniversityBusan 48513Republic of Korea Department of Urban Planning Engineering Networks and SystemsInstitute of Architecture and ConstructionSouth Ural State University76Lenin ProspectChelyabinsk 454080Russia

Deep excavation during the construction of underground systems can cause movement on the ground,especially in soft clay *** high levels,excessive ground movements can lead to severe damage to adjacent *** this study,finite element analyses(FEM)and the hardening small strain(HSS)model were performed to investigate the deflection of the diaphragm wall in the soft clay layer induced by braced *** geometric and mechanical properties of the wall were investigated to study the deflection behavior of the wall in soft ***,1090 hypothetical cases were surveyed and simulated based on the HSS model and FEM to evaluate the wall deflection *** results were then used to develop an intelligent model for predicting wall deflection using the functional linked neural network(FLNN)with different functional expansions and activation *** the FLNN is a novel approach to predict wall deflection;however,in order to improve the accuracy of the FLNN model in predicting wall deflection,three swarm-based optimization algorithms,such as artificial bee colony(ABC),Harris’s hawk’s optimization(HHO),and hunger games search(HGS),were hybridized to the FLNN model to generate three novel intelligent models,namely ABC-FLNN,HHO-FLNN,*** results of the hybrid models were then compared with the basic FLNN and MLP *** revealed that FLNN is a good solution for predicting wall deflection,and the application of different functional expansions and activation functions has a significant effect on the outcome predictions of the wall *** is remarkably interesting that the performance of the FLNN model was better than the MLP model with a mean absolute error(MAE)of 19.971,root-mean-squared error(RMSE)of 24.574,and determination coefficient(R^(2))of ***,the performance of the MLP model only obtained an MAE of 20.321,RMSE of 27.091,and R^(2)of ***,the results also indicated that the proposed

关键词： Diaphragm wall deflection Braced excavation Finite element analysis Clays meta-heuristic algorithms Functional linked neural network

An improved fruit fly optimization algorithm for solving high dimensional problems

在线全文

学校读者我要写书评

暂无评论

An improved fruit fly optimization algorithm for solving hig...