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Neural network combined with nature-inspired algorithms to estimate overall heat transfer coefficient of a ribbed triple-tube heat exchanger operating with a hybrid nanofluid

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MEASUREMENT 2021年 174卷 108967-108967页

作者： Bahiraei, Mehdi Foong, Loke Kok Hosseini, Siavash Mazaheri, Nima Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Ton Duc Thang Univ Dept Management Sci & Technol Dev Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Civil Engn Ho Chi Minh City Vietnam Islamic Azad Univ Kermanshah Branch Young Researchers & Elite Club Kermanshah Iran

Four nature-inspired optimizers are combined with a multilayer perceptron neural network for reaching an optimal structure aimed at predicting the overall heat transfer coefficient of a ribbed triple-tube heat exchanger in terms of the rib pitch, rib height, and nanoparticle concentration. The heat exchanger works with a hybrid nanofluid having graphene nanoplatelet/Pt nanocomposite powder. The applied algorithms incorporate Artificial Bee Colony (ABC), Ant Colony optimization (ACO), Ant Lion Optimizer (ALO), and Biogeography-Based optimization (BBO). The required data are provided via computational solutions. The BBO algorithm is determined as the best method to predict the output because of its higher accuracy. The highest performance of the BBO is obtained from population of 450. The overall heat transfer coefficient is estimated with root mean square errors of 0.030 and 0.025 for the training data and testing data, respectively. Furthermore, the ACO algorithm shows the lowest computational time among all the methods.

关键词： Artificial neural network optimization algorithms Ribbed triple-tube heat exchanger Hybrid nanofluid Graphene nanoplatelets Overall heat transfer coefficient

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R-GWO: Representative-based grey wolf optimizer for solving engineering problems

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APPLIED SOFT COMPUTING 2021年 106卷 107328-107328页

作者： Banaie-Dezfouli, Mahdis Nadimi-Shahraki, Mohammad H. Beheshti, Zahra Islamic Azad Univ Najafabad Branch Fac Comp Engn Najafabad Iran Islamic Azad Univ Najafabad Branch Big Data Res Ctr Najafabad Iran

The grey wolf optimizer (GWO) is a well-known nature-inspired algorithm, which shows a sufficient performance for solving various optimization problems. However, it suffers from low exploration and population diversity because its optimization process is only based on the best three wolves greedily, and the information of other wolves does not consider. In this paper, a representative-based grey wolf optimizer (R-GWO) is proposed to tackle with these weaknesses of the GWO. The R-GWO introduces a search strategy named representative-based hunting (RH) a combination of three effective trial vectors inspired by alpha wolves' behaviors to improve the exploration and diversity of the population. The RH search strategy utilizes a representative archive to reduces the greediness and enhance the diversity of solutions, and it can also strike balance between the exploration and exploitation using a nonlinear control parameter. The performance and applicability of the proposed R-GWO were evaluated on CEC 2018 benchmark functions and six engineering design problems. The results were compared by eight state-of-the-art metaheuristic algorithms: PSO, KH, GWO, WOA, EEGWO, BOA, HHO, and HGSO. Moreover, the results were statistically analyzed by three test Wilcoxon rank-sum, Friedman and mean absolute error (MAE). The performance results show that on all 29 functions with dimensions 30, 50, and 100, the R-GWO is superior to the competitor algorithms except on function 27 on all dimensions and function 22 on dimension 30. The proposed R-GWO is the most effective algorithm compared with competitor algorithms, with an overall effectiveness of 95.4%. The experimental and statistical results show that the R-GWO is competitive and superior to compared algorithms and can solve engineering design problems better than competitor algorithms. (C) 2021 Elsevier B.V. All rights reserved.

关键词： optimization algorithms Metaheuristic algorithms Swarm intelligence algorithms Grey wolf optimizer Engineering design problems

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ANFIS-MOA models for the assessment of groundwater contamination vulnerability in a nitrate contaminated area

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2021年 286卷 112162-112162页

作者： Elzain, Hussam Eldin Chung, Sang Yong Park, Kye-Hun Senapathi, Venkatramanan Sekar, Selvam Sabarathinam, Chidambaram Hassan, Mohamed Pukyong Natl Univ Dept Environm Earth Sci Busan South Korea Alagappa Univ Dept Disaster Management Karaikkudi Tamil Nadu India VO Chidambaram Coll Dept Geol Tuticorin India Kuwait Inst Sci Res Water Res Ctr Kuwait Kuwait King Fahd Univ Petr & Minerals Dept Syst Engn Dhahran Saudi Arabia

The enhanced assessment of groundwater contamination vulnerability is necessary for the management and conservation of groundwater resources because groundwater contamination has been much increased continuously in the world by anthropogenic origin. The purpose of this study is to determine the best model among three ANFIS-MOA models (the adaptive neuro-fuzzy inference system (ANFIS) combined with metaheuristic optimization algorithms (MOAs) such as genetic algorithm (GA), differential evolution algorithm (DE) and particle swarm optimization (PSO)) in assessing groundwater contamination vulnerability at a nitrate contaminated area. The Miryang City of South Korea was selected as the study area because the nitrate contamination was widespread in the city with two functions of urban and rural activities. Eight parameters (depth to water, net recharge, topographic slope, aquifer type, impact to vadose zone, hydraulic conductivity and landuse) were classified into the numerical ratings on basis of modified DRASTIC method (MDM) for the input variables of ANFIS-MOA models. The Original ANFIS, and 3 combined models of ANFIS-PSO, ANFIS-DE and, ANFIS-GA used 95 adjusted vulnerability indices (AVI) as the target data of training (70% data) and testing (30% data) processing. The performance of 4 models was evaluated by mean absolute errors (MAE), root mean square errors (RMSE), correlation coefficients (R), ROC/AUC curves and predicted AVI (PAVI) maps. The statistical results, spatial vulnerability maps and correlation coefficients between PAVIs and nitrate concentrations revealed that the order of model excellence was ANFIS-PSO, ANFIS-DE, ANFIS-GA, and Original ANFIS, and that ANFIS-PSO showed the highest performance in training and testing processing. The performance rates of ANFIS-MOA models were also compared with 10 recent popular worldwide models using the correlation coefficients between PVI and nitrate concentrations, and they were superior to other recent popular mode

关键词： Groundwater contamination vulnerability Nitrate ANFIS-MOA models optimization algorithms Adjusted vulnerability index ANFIS-PSO

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Towards optimal task positioning in multi-robot cells, using nested meta-heuristic swarm algorithms

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ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING 2021年 71卷 102131-102131页

作者： Mutti, S. Nicola, G. Beschi, M. Pedrocchi, N. Tosatti, L. Molinari Natl Res Council Italy Inst Intelligent Ind Technol & Syst Adv Mfg I-20133 Milan Italy Univ Padua Dept Informat Engn DEI Via Gradenigo 6-A Padua Italy Univ Brescia Dipartimento Ingn Meccan & Ind Via Branze 38 Brescia Italy

While multi-robot cells are being used more often in industry, the problem of work-piece position optimization is still solved using heuristics and the human experience and, in most industrial cases, even a feasible solution takes a considerable amount of trials to be found. Indeed, the optimization of a generic performance index along a path is complex, due to the dimension of the feasible-configuration space. This work faces this challenge by proposing an iterative layered-optimization method that integrates a Whale optimization and an Ant Colony optimization algorithm, the method allows the optimization of a user-defined objective function, along a working path, in order to achieve a quasi-optimal, collision free solution in the feasible-configuration space.

关键词： Multi-robot coordination Motion planning optimization algorithms Industrial robotics

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Parallelization of computational algorithms for optimization problems with high time consumption 22

Parallelization of computational algorithms for optimization...

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22nd Slovak-Polish Scientific Conference on Machine Modelling and Simulations (MMS)

作者： Vasko, Milan Handrik, Marian Sapietova, Alzbeta Handrikova, Jana Univ Zilina Fac Mech Engn Dept Appl Mech Univ 8215-1 Zilina Slovakia Armed Forces Acad Gen MR Stefanik Dept Informat Liptovsky Mikulas Slovakia

The paper presents an analysis of the use of optimization algorithms in parallel solutions and distributed computing systems. The primary goal is to use evolutionary algorithms and their implementation into parallel calculations. Parallelization of computational algorithms is suitable for the following cases computational models with a large number of design variables or cases where the objective function evaluation is time consuming (FE analysis). As the software platform for application of distributed optimization algorithms is using MATLAB and BOINC software package.

关键词： ADINA MATLAB BOINC optimization algorithms parallelization distributed computing systems evolutionary algorithms FE analysis

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An indirect measurement of rock tensile strength through optimized relevance vector regression models, a case study

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ENVIRONMENTAL EARTH SCIENCES 2021年第22期80卷 748-748页

作者： Fattahi, Hadi Hasanipanah, Mahdi Arak Univ Technol Fac Earth Sci Engn Arak Iran Univ Kashan Dept Min Engn Kashan Iran Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam

To design the structure of foundations and tunnels, one of the most important parameters to be well considered is the rock tensile strength (TS). Direct methods of TS measurement, i.e., laboratory experiments, are both costly and time-consuming. Therefore, to measure the rock TS, this paper proposes two novel hybrid optimization models, i.e. relevance vector regression (RVR) optimized by the harmony search (HS) and cuckoo search (CS) algorithms. A database including 76 datasets was constructed through collecting the rock samples from a tunnel site and exposing them to different tests (Brazilian tensile strength, BTS, as output) in laboratory. The proposed models performances were assessed through some statistical metrics, e.g., determination coefficient. The results demonstrated the successful application of RVR-HS and RVR-CS models in predicting BTS. However, the performance of RVR-HS was better than RVR-CS. As a conclusion, the HS algorithm played a great role in optimizing the proposed RVR model.

关键词： Rock tensile strength Brazilian tensile strength Relevance vector regression optimization algorithms

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Design of multilayer sound-absorbing composites with excellent sound absorption properties at medium and low frequency via constructing variable section cavities

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COMPOSITE STRUCTURES 2021年 266卷 113798-113798页

作者： Wang, Xiting Ma, Lifeng Wang, Yansong Guo, Hui Shanghai Univ Engn Sci Sch Mech & Automot Engn Shanghai 201620 Peoples R China

In this work, multilayer composites composed of ethylene-propylene-diene monomer (EPDM) foams and EPDM perforated plates were proposed to improve sound absorption properties at medium and low frequency. Models of composites with variable section cavities were designed and explored the influence of structural parameters on sound absorption coefficient. Numeral calculations results showed that the sound absorption curves of multilayer composites usually have two peaks in 300 -1300 Hz. Compared with the other, the composite composed of one perforated board with variable section cavities and two foams (VFF) shows best sound absorption capacities in low frequency. Two optimization algorithms: artificial fish swarm algorithm (AFSA) and improved particle swarm optimization (I-PSO) were presented to optimize structural parameters of VFF. It is found that the optimization results of I-PSO are better that of AFSA for improving the absorption properties at low medium and frequency. This work provide a simple method and a new idea to improve sound absorption properties of multilayer composites.

关键词： Multilayer composites Sound absorption Medium and low frequency optimization algorithms

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Remote Sensing Imagery Segmentation: A Hybrid Approach

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REMOTE SENSING 2021年第22期13卷 4604-4604页

作者： Pare, Shreya Mittal, Himanshu Sajid, Mohammad Bansal, Jagdish Chand Saxena, Amit Jan, Tony Pedrycz, Witold Prasad, Mukesh Univ Technol Sydney Sch Comp Sci Fac Engn & Informat Technolgy Sydney NSW 2007 Australia Jaypee Inst Informat Technol Dept Comp Sci Engn & IT Noida 201309 India Aligarh Muslim Univ Dept Comp Sci Aligarh 202001 Uttar Pradesh India South Asian Univ Dept Math New Delhi 110021 India Guru Ghashidash Univ Dept Comp Sci & IT Bilashpur 495009 India Torrens Univ Design & Technol Vert Sydney NSW 2007 Australia Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2R3 Canada

In remote sensing imagery, segmentation techniques fail to encounter multiple regions of interest due to challenges such as dense features, low illumination, uncertainties, and noise. Consequently, exploiting vast and redundant information makes segmentation a difficult task. Existing multilevel thresholding techniques achieve low segmentation accuracy with high temporal difficulty due to the absence of spatial information. To mitigate this issue, this paper presents a new Renyi's entropy and modified cuckoo search-based robust automatic multi-thresholding algorithm for remote sensing image analysis. In the proposed method, the modified cuckoo search algorithm is combined with Renyi's entropy thresholding criteria to determine optimal thresholds. In the modified cuckoo search algorithm, the Levy flight step size was modified to improve the convergence rate. An experimental analysis was conducted to validate the proposed method, both qualitatively and quantitatively against existing metaheuristic-based thresholding methods. To do this, the performance of the proposed method was intensively examined on high-dimensional remote sensing imageries. Moreover, numerical parameter analysis is presented to compare the segmented results against the gray-level co-occurrence matrix, Otsu energy curve, minimum cross entropy, and Renyi's entropy-based thresholding. Experiments demonstrated that the proposed approach is effective and successful in attaining accurate segmentation with low time complexity.

关键词： image segmentation remote sensing images multilevel Renyi's entropy cuckoo search optimization algorithms

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Assessing agricultural salt-affected land using digital soil mapping and hybridized random forests

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GEODERMA 2021年 385卷 114858-114858页

作者： Nabiollahi, Kamal Taghizadeh-Mehrjardi, Ruhollah Shahabi, Aram Heung, Brandon Amirian-Chakan, Alireza Davari, Masoud Scholten, Thomas Univ Kurdistan Fac Agr Dept Soil Sci & Engn Sanandaj Iran Ardakan Univ Fac Agr & Nat Resources Ardakan Iran Univ Tubingen Dept Geosci Soil Sci & Geomorphol Tubingen Germany Dalhousie Univ Fac Agr Dept Plant Food & Environm Sci Halifax NS Canada Lorestan Univ Dept Soil Sci Khorramabad *** Iran Univ Tubingen DFG Cluster Excellence Machine Learning Tubingen Germany Univ Tubingen CRC Resource Cultures 1070 Gartenstr 29 Tubingen Germany

Salinization and alkalization are predominant environmental problem world-wide which their accurate assessment is essential for determining appropriate ways to deal with land degradation, for better soil and crop management. In the current research, a combination of random forests and covariate data were used to assess spatial variability of soil salinity and sodicity in 436 km(2) agricultural salt-affected land in Kurdistan Province, Iran. Using the conditioned Latin hypercube sampling method, 295 soil samples were sampled across the study area, and then soil reaction (pH), electrical conductivity (EC), and sodium adsorption ratio (SAR) were measured. Covariate data including terrain attributes, remotely-sensed data, groundwater table, and categorical maps were acquired. Random forest (RF) models were used to predict the spatial distribution of pH, EC, and SAR by making a relationship between soil data and covariates. Furthermore, three optimization algorithms (particle swarm optimization-PSO, genetic algorithm-GA, and bat algorithm-BAT) were used to explore if the hybridized RF works better than the standard RF. Results of 10-fold cross-validation with 100 replications indicated that the accuracy of RF PSO was higher for predicting pH (RMSE = 0.52 and R-2 = 0.67), EC (RMSE = 2.32 dSm(-1) and R-2 = 0.57), and SAR (RMSE = 8.98 and R-2 = 0.54, respectively) in comparison to the other implemented models. Furthermore, the results disclosed that the most important covariates to predict pH, EC, and SAR were groundwater table, categorical maps, salinity index, and multi-resolution ridge top flatness. Besides, the results indicated that the mean values for pH, EC, and SAR in lowland and bare land were significantly different from the other physiographic units and land uses, respectively. Importantly, the classified map of salt-affected soils highlighted areas with a high risk of exceeding critical threshold values of pH, EC, and SAR, which is located in the center of the s

关键词： Digital soil mapping Random forest optimization algorithms Soil salinity Soil sodicity

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Methods for Solving optimization Problems with a Multimodal Objective Function Based on Hybrid algorithms

Methods for Solving Optimization Problems with a Multimodal ...

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International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： Evich, L. N. Ostroukh, E. N. Panasenko, P. A. Don State Tech Univ Dept Informat Technol Dept Media Commun & Multimedia Technol Rostov Na Donu Russia Krasnodar Higher Mil Sch Dept Sci Work Krasnodar Russia

ISBN: (纸本)9781538643075

Popular algorithms of global optimization are reviewed. Comparison of two hybrid algorithms is presented. In particular, consecutive and conveyor hybridization of Artificial bee colony (ABC) and Gravitational Search Algorithm (GSA) is introduced. The analysis of two algorithms work productivity based on spherical function, Rosenbrak, Girvonk, Rastrigin, Shevfel functions is performed. Efficiency of algorithm with conveyor hybridization compared to algorithm with consecutive hybridization is set for determined parameters.

关键词： optimization algorithms hybrid algorithms genetic algorithms bee colony gravitational algorithm

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