检索结果-内蒙古大学图书馆

meta-heuristic optimization algorithms for solving real-world mechanical engineering design problems: a comprehensive survey, applications, comparative analysis, and results

引用

NEURAL COMPUTING & APPLICATIONS 2022年第6期34卷 4081-4110页

作者： Abualigah, Laith Abd Elaziz, Mohamed Khasawneh, Ahmad M. Alshinwan, Mohammad Ibrahim, Rehab Ali Al-qaness, Mohammed A. A. Mirjalili, Seyedali Sumari, Putra Gandomi, Amir H. Amman Arab Univ Fac Comp Sci & Informat Amman Jordan Univ Sains Malaysia Sch Comp Sci George Town Malaysia Zagazig Univ Dept Math Fac Sci Zagazig 44519 Egypt Sanaa Univ Fac Engn Sanaa 12544 Yemen Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat 90 Bowen Terrace Fortitude Valley Qld 4006 Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia Ajman Univ Artificial Intelligence Res Ctr AIRC Ajman 346 U Arab Emirates Galala Univ Fac Comp Sci & Engn Suze 435611 Egypt Tomsk Polytech Univ Sch Comp Sci & Robot Tomsk 634050 Russia

Real-world engineering design problems are widespread in various research disciplines in both industry and industry. Many optimization algorithms have been employed to address these kinds of problems. However, the algorithm's performance substantially reduces with the increase in the scale and difficulty of problems. Various versions of the optimization methods have been proposed to address the engineering design problems in the literature efficiently. In this paper, a comprehensive review of the meta-heuristic optimization methods that have been used to solve engineering design problems is proposed. We use six main keywords in collecting the data (meta-heuristic, optimization, algorithm, engineering, design, and problems). It is worth mentioning that there is no survey or comparative analysis paper on this topic available in the literature to the best of our knowledge. The state-of-the-art methods are presented in detail over several categories, including basic, modified, and hybrid methods. Moreover, we present the results of the state-of-the-art methods in this domain to figure out which version of optimization methods performs better in solving the problems studied. Finally, we provide remarkable future research directions for the potential methods. This work covers the main important topics in the engineering and artificial intelligence domain. It presents a large number of published works in the literature related to the meta-heuristic optimization methods in solving various engineering design problems. Future researches can depend on this review to explore the literature on meta-heuristic optimization methods and engineering design problems.

关键词： meta-heuristic optimization algorithms Differential evolution Real-world engineering design problems optimization problems Algorithm Benchmark

来源：评论

学校读者我要写书评

暂无评论

Implementation of meta-heuristic optimization algorithms for interview problem in land consolidation: A case study in Konya/Turkey

引用

LAND USE POLICY 2021年 108卷 105511-105511页

作者： Ozsari, Sifa Uguz, Harun Hakli, Huseyin Ankara Univ Dept Comp Engn Ankara Turkey Konya Tech Univ Dept Comp Engn Konya Turkey Necmettin Erbakan Univ Dept Comp Engn Konya Turkey

The cultivation of soil for supply of nutritional products necessary for human life is called agriculture. Although agriculture is very important for human beings, it is getting more difficult day by day to cultivate soil efficiently for various reasons. One of the main causes, which significantly prevents sustainable agriculture, is land fragmentation. Land consolidation is one of the important measures taken in order to prevent further fragmentation of agricultural land and the decrease in yield obtained from agriculture. The land consolidation process consists of several time consuming steps. Interview, today conducted manually in Turkey, is the stage where preferences of landowners are taken. These preferences correspond to the blocks that enterprises want their parcels to be placed at the end of consolidation. The interview phase takes a long time as it is carried out manually by a technician. Various studies have been done to improve the land consolidation, but most of these studies focus on other stages of process. In this study, genetic algorithm, particle swarm optimization, non-dominated sorting genetic algorithm II and multi objective particle swarm optimization are applied on the interview problem. The interview problem is a discrete structure optimization problem, thus its solution with traditional methods is difficult and time consuming. Preference lists are generated automatically using optimization algorithms. These lists are compared with the actual interview lists created by the technician. The experimental results confirm the success of algorithms in solving the real world problem.

关键词： Land consolidation Interview meta-heuristic optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

An Optimal Elliptical Antenna Array Design Using Novel meta-heuristic optimization Approaches

引用

WIRELESS PERSONAL COMMUNICATIONS 2023年第2期129卷 1425-1449页

作者： Durmus, Ali Kayseri Univ Vocat Coll Dept Elect & Energy TR-38280 Kayseri Turkiye

In this study, elliptical antenna arrays (EAAs) with low maximum side lobe levels (MSLs) and fixed half-power beam width has been synthesized with novel meta-heuristic methods, namely, Archimedes optimization Algorithm (AOA) and Chimp optimization Algorithm (ChOA). In order to use the communication systems effectively and efficiently in recent years, application-specific antenna designs have become more important. In this specialization in wireless communication, interest in the optimum design of EAA specially designed for the application has increased. Two different cases are discussed in this study. In the first case, the angular position of the EAA elements are fixed and the amplitude values are optimally determined by AOA and ChOA methods to obtained radiation patterns with reduced MSLs. Assuming that the elements in the antenna array are fed uniformly and the current amplitude values are all one, the angular positions of the elements are found by the proposed novel optimization methods to minimize MSLs in the second case. To test the flexibility and performance of the proposed methods, three different designs of the 8, 12 and 20 elements EAA are considered. The results of the AOA and ChOA methods are compared with the current results obtained by Antlion Optimisation and Symbiotic Organisms Search in the literature. In addition, the statistical performances of the proposed methods are compared. It has been observed that generally the results obtained with the AOA method are better than other compared methods.

关键词： Antenna array synthesis Elliptical arrays meta-heuristic optimization algorithms Maximum side lobe levels

来源：评论

学校读者我要写书评

暂无评论

Shell and tube heat exchanger optimization: A critical literature assessment and fairness-based comparative performance analysis of meta-heuristic algorithms

引用

Case Studies in Thermal Engineering 2025年 72卷

作者： Samet Gürgen Department of Naval Architecture and Marine Engineering İskenderun Technical University 31200 Hatay Turkey

Determining the optimum design of shell and tube heat exchangers (STHE) is a crucial issue for the efficient use of scarce energy resources. In particular, there is a great effort on the economic-based optimization of STHE. The most well-known STHE design in the literature is the one presented by Sinnot et al. This design problem has been addressed by many researchers with different optimization algorithms. In the first part of this study, an optimization study was conducted using a limited set of algorithms for the same optimization problem. In order to make comparisons, the objective function, decision variables and their boundary values were taken as the same. Initial findings showed that the results were quite close to each other, contrary to the literature. It was determined that the variability in the results of previous studies was due to differences in the mathematical model and incorrect optimization procedure. In the second part of the study, a comprehensive algorithm performance analysis of twenty meta-heuristic optimization algorithms was performed. This study aimed to perform algorithm performance for STHE optimization by integrating a fair evaluation approach. It also offers a robust framework for effectively using and comparing optimization algorithms in thermal engineering applications.

关键词： Shell and tube heat exchangers optimization meta-heuristic optimization algorithms Algorithm performance

来源：评论

学校读者我要写书评

暂无评论

On the optimal synthesis of sparsely thinned planar array antenna with constraints using meta-heuristic optimization

引用

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2024年第9期37卷 e5760-e5760页

作者： Dutta, Kailash P. Mahanti, Gautam K. Cambridge Inst Technol Dept Elect & Commun Engn Ranchi 835103 India Natl Inst Technol Dept Elect & Commun Engn Durgapur India

The investigations related to the planar antenna array have attracted much attention due to their vast applications in the areas of advanced wireless communication and electromagnetics. This article presents an effective synthesis method of a sparsely thinned symmetric planar antenna array using three well-known meta-heuristics including symbiotic organism search (SOS) algorithm, moth fly optimization (MFO), and multi-verse optimization (MVO) algorithms. The main aim of this work is to optimize the positions of the switched-on antenna elements on the array aperture in order to reduce the value of side-lobe levels in the radiation field pattern in multiple planes for a desired first null beam width and subsequently to obtain the maximum reduced number of array-elements in the antenna array. Two different cases are performed to optimize the radiation pattern in different azimuth angles with two different examples. The proposed methods can constrain the total number of array elements, inter-element distance, and aperture area of the array. The radiation pattern characteristic and computation time linked with each example and each algorithm are recorded and compared with each other as well as with a fully populated planar symmetric rectangular array antenna of same aperture size for arriving at the conclusion. The simulation-based results demonstrate the effectiveness of the proposed design and the efficiency of the performance using the SOS algorithm. This article presents an effective synthesis of sparsely thinned symmetric planar antenna array using novel implementation of three famous meta-heuristics, namely, symbiotic organism search (SOS), moth fly optimization (MFO), and multi-verse optimization (MVO) algorithms. The aim of this work is to optimize the positions of switched-on antenna elements on the array aperture for optimizing the side-lobe levels in the radiation pattern in multiple planes. The simulation-based results establish the superiority of the propose

关键词： array synthesis first null beam width (FNBW) meta-heuristic optimization algorithms planar sparse antenna array side lobe level (SLL)

来源：评论

学校读者我要写书评

暂无评论

A Novel Hybrid Ensemble Wind Speed Forecasting Model Employing Wavelet Transform and Deep Learning

引用

COMPUTERS & ELECTRICAL ENGINEERING 2025年 121卷

作者： Namboodiri, V. Vishnu Goyal, Rahul Indian Inst Technol Delhi Dept Energy Sci & Engn New Delhi 110016 India NICMAR Univ Construct Studies Sch Energy & Clean Technol Hyderabad 500101 Telangana India

Efficient wind speed forecasting is crucial for operations, optimizations, and decision-making interventions in wind energy systems. However, capturing nonlinearity and relevant information from the wind speed data poses challenges in developing efficient wind speed forecasting models. The present study proposes a novel hybrid ensemble wind speed forecasting model based on signal decomposition, deep learning model, and hyperparameter optimization for short-term applications to improve the model performances. This study comprises a novel architecture, a novel hybrid ensemble wind speed forecasting model, a two-level optimization strategy, and a transfer learning approach. The present study consists of three stages: model development, validation, and transfer learning. The proposed model employs wavelet transform, deep learning models such as Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network (CNN), and a combined model using Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNN-BiLSTM) and meta-heuristic optimization algorithms. The novel architecture of the CNN-BiLSTM model is capable of exhibiting better results than baseline models. Artificial Bee Colony (ABC) and the Differential Evolution (DE) algorithms are explored to optimize the model hyperparameters. The ensemble weights of the proposed model are optimized through a DE algorithm. The model implementation is presented through a transfer learning technique using pre-trained models from the model development and validation phases. The model comparison results indicate that the proposed models outperform these models. The transfer learning results of Proposed Model-1 (PM-1) are Root Mean Squared Error (RMSE)- 0.1943 m/s, Mean Squared Error (MSE)- 0.0378 m/s, Mean Absolute Error (MAE) 0.1542 m/s, coefficient of determination (R2)- 0.9883, and Index of Agreement (IA)- 0.9997. The Proposed Model-2 (PM-2) is 0.1554 m/s (RMSE), 0.0241 m/s (

关键词： Short-term wind speed forecasting wavelet transform deep learning models meta-heuristic optimization algorithms transfer learning

来源：评论

学校读者我要写书评

暂无评论

Optimal Structure to Maximize Torque per Volume for the Consequent-Pole PMSM and Investigating the Temperature Effect

引用

IEEE ACCESS 2024年 12卷 108851-108862页

作者： Hosseinpour, Alireza Abbas, Ahmed Sadegh, Mahmoud Oukati Iqbal, Atif Flah, Aymen Prokop, Lukas Ali, Enas Ghaly, Ramy N. R. Univ Zabol Dept Elect Engn Zabol Iran Gdansk Univ Technol Fac Elect & Control Engn PL-80233 Gdansk Poland Univ Sistan & Baluchestan Dept Elect Engn Zahedan Iran Qatar Univ Dept Elect Engn Doha Qatar Univ Gabes Natl Engn Sch Gabes Gabes 6029 Tunisia Middle East Univ MEU Res Unit Amman 11831 Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Univ Business & Technol UBT Coll Engn Jeddah 21448 Saudi Arabia Univ Gabes Private Higher Sch Appl Sci & Technol Gabes Gabes 6029 Tunisia VSB Tech Univ Ostrava ENET Ctr Ostrava 70800 Czech Republic Chandigarh Univ Univ Ctr Res & Dev Mohali 140413 Punjab India Mataria Tech Coll Minist Higher Educ Cairo 11718 Egypt Chitkara Univ Chitkara Ctr Res & Dev Kalujhinda 174103 Himachal Prades India

Heat removal, maximizing torque, minimizing losses, volume, cost, and temperature effect play essential roles in electrical vehicle applications. An inner-rotor consequent-pole permanent magnet synchronous machine (CPPMSM) merits suitable losses, cost, and heat rejection. Hence, first, a two-dimensional model of CPPMSM is explained based on solving Maxwell's equations in all regions of the machine. Then, all the components of torque, back-EMF, inductance, and unbalanced magnetic forces in the direction of the X-axis and Y-axis and their magnitudes are calculated. Afterward, the overload capability and the torque-speed characteristic are determined based on the average torque. Therefore, to maximize the torque/volume ratio, four metaheuristic optimization algorithms, including Genetic Algorithm (GA), Particle Swarm optimization (PSO), Differential Evolution (DE), and Teaching Learn Base optimization (TLBO), have been implemented, and the mentioned index is optimized. Since the said algorithms usually can minimize, its inverse is minimized instead of the index mentioned above being maximized. At this stage, the effect of three types of magnetization patterns, i.e., radial, parallel, and bar magnet in shifting, is also considered. The flux density of the permanent magnet changes concerning temperature. Finally, the effect of these changes on cogging, reluctance, and instantaneous torque, as well as back-EMF, unbalance magnetic force (UMF), torque-speed characteristic, and overload capability diagram, will be analyzed. The simulation was performed using MATLAB software.

关键词： Torque Mathematical models Air gaps Rotors Windings Magnetization Maxwell equations Electric vehicles metaheuristics optimization methods Temperature measurement INDEX TERMS Electric vehicle magnetization pattern meta-heuristic optimization algorithms temperature impact

来源：评论

学校读者我要写书评

暂无评论

Group search optimizer: a nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications

引用

NEURAL COMPUTING & APPLICATIONS 2021年第7期33卷 2949-2972页

作者： Abualigah, Laith Amman Arab Univ Fac Comp Sci & Informat Amman 11953 Jordan

In this paper, to keep the researchers interested in nature-inspired algorithms and optimization problems, a comprehensive survey of the group search optimizer (GSO) algorithm is introduced with detailed discussions. GSO is a nature-inspired optimization algorithm introduced by He et al. (IEEE Trans Evol Comput 13:973-990, 2009) to solve several different optimization problems. It is inspired by animal searching behavior in real life. This survey focuses on the applications of the GSO algorithm and its variants and results from the year of its suggestion (2009) to now (2020). GSO algorithm is used to discover the best solution over a set of candidate solution to solve any optimization problem by determining the minimum or maximum objective function for a specific problem. meta-heuristic optimizations, nature-inspired algorithms, have become an interesting area because of their rule in solving various decision-making problems. The general procedures of the GSO algorithm are explained alongside with the algorithm variants such as basic versions, discrete versions, and modified versions. Moreover, the applications of the GSO algorithm are given in detail such as benchmark function, classification, networking, engineering, and other problems. Finally, according to the analyzed papers published in the literature by the all publishers such as IEEE, Elsevier, and Springer, the GSO algorithm is mostly used in solving various optimization problems. In addition, it got comparative and promising results compared to other similar published optimization algorithm.

关键词： Group search optimizer meta-heuristic optimization algorithms optimization problems Nature-inspired algorithms

来源：评论

学校读者我要写书评

暂无评论

Advanced Machine Learning and Gene Expression Programming Techniques for Predicting CO_(2)-Induced Alterations in Coal Strength

引用

Computer Modeling in Engineering & Sciences 2025年第4期143卷 153-183页

作者： Zijian Liu Yong Shi ChuanqiLi Xiliang Zhang Jian Zhou Manoj Khandelwal School of Resources and Safety Engineering Central South UniversityChangsha410083China Changsha Institute of Mining Research Co. Ltd.Changsha410012China State Key Laboratory of Safety and Health for Metal Mines Ma’anshan243000China Institute of Innovation Science and SustainabilityFederation University AustraliaBallaratVIC 3350Australia

Given the growing concern over global warming and the critical role of carbon dioxide(CO_(2))in this phenomenon,the study of CO_(2)-induced alterations in coal strength has garnered significant attention due to its implications for carbon sequestration.A large number of experiments have proved that CO_(2) interaction time(T),saturation pressure(P)and other parameters have significant effects on coal ***,accurate evaluation of CO_(2)-induced alterations in coal strength is still a difficult problem,so it is particularly important to establish accurate and efficient prediction *** study explored the application of advancedmachine learning(ML)algorithms and Gene Expression Programming(GEP)techniques to predict CO_(2)-induced alterations in coal *** were developed,including three metaheuristic-optimized XGBoost models(GWO-XGBoost,SSA-XGBoost,PO-XGBoost)and three GEP models(GEP-1,GEP-2,GEP-3).Comprehensive evaluations using multiple metrics revealed that all models demonstrated high predictive accuracy,with the SSA-XGBoost model achieving the best performance(R2—Coefficient of determination=0.99396,RMSE—Root Mean Square Error=0.62102,MAE—Mean Absolute Error=0.36164,MAPE—Mean Absolute Percentage Error=4.8101%,RPD—Residual Predictive Deviation=13.4741).Model interpretability analyses using SHAP(Shapley Additive exPlanations),ICE(Individual Conditional Expectation),and PDP(Partial Dependence Plot)techniques highlighted the dominant role of fixed carbon content(FC)and significant interactions between FC and CO_(2) saturation pressure(P).Theresults demonstrated that the proposedmodels effectively address the challenges of CO_(2)-induced strength prediction,providing valuable insights for geological storage safety and environmental applications.

关键词： CO_(2)-induced coal strength meta-heuristic optimization algorithms XGBoost gene expression programming model interpretability

来源：评论

学校读者我要写书评

暂无评论

Fine-Tuning LightGBM Using an Artificial Ecosystem-Based Optimizer for Forest Fire Analysis

引用

FOREST SCIENCE 2023年第1期69卷 73-82页

作者： Quoc-Huy Nguyen Huu-Duy Nguyen Dinh Tan Le Quang-Thanh Bui Vietnam Natl Univ VNU Univ Sci Fac Geog 334 Nguyen Trai Str Hanoi Vietnam Trade Union Univ Fac Tourism Hanoi Vietnam

This study's main objective is to propose a hybrid machine learning model based on a gradient boosting algorithm named LightGBM and an artificial ecosystem-based optimization to improve the accuracy of forest fire susceptibility assessment. Four hundred twenty-six historical forest fires from the NASA portal and thirteen conditional factors including elevation, aspect, slope, curvature, normalized difference vegetation index, normalized difference water index, distance to residence, distance to road, distance to river, temperature, rain, humidity, and wind were used to train the model. The model performance was evaluated and compared with other benchmark models using root mean square error, area under receiver operating characteristics (AUC), and overall accuracy. The results show that the proposed model (AUC = 0.9705) outperforms others, such as Random Forest (AUC = 0.958), AdaBoost (AUC = 0.905), Bagging (AUC = 0.945), and Random Subspace (AUC = 0.938), respectively. The final model was interpreted to better understand the most influential factors of forest fire hazards. Study Implications: This study's main objective is to propose a hybrid machine learning model based on a gradient boosting algorithm called LightGBM and an artificial ecosystem-based optimization to improve the accuracy of forest fire susceptibility assessment. The final model was interpreted using the Shapley (SHAP) values to understand how input factors are related to the fire susceptibility level. This study fills a gap in the research literature, searching for optimal algorithms to improve forest fire susceptibility mapping.

关键词： forest fire susceptibility meta-heuristic optimization algorithms gradient boosting method

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

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

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：