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Evaluation of total dissolved solids in rivers by improved neuro fuzzy approaches using metaheuristic algorithms

EARTH SCIENCE INFORMATICS 2024年第2期17卷 1501-1522页

作者： Jannatkhah, Mahdieh Davarpanah, Rouhollah Fakouri, Bahman Kisi, Ozgur Univ Tabriz Fac Civil Engn Tabriz Iran Tarbiat Modares Univ Fac Agr Dept Water Engn & Management Tehran Iran Tech Univ Lubeck Dept Civil Engn D-23562 Lubeck Germany Ilia State Univ Dept Civil Engn Tbilisi 0162 Georgia

Substantial deterioration of surface water quality, mainly caused by human activities and climate change, makes the assessment of water quality a global priority. Thus, in this study, four metaheuristic algorithms, namely the particle swarm optimization (PSO), differential evolution (DE), ant colony optimization algorithm (ACOR), and genetic algorithm (GA), were employed to improve the performance of the adaptive neuro-fuzzy inference system (ANFIS) in the evaluation of surface water total dissolved solids (TDS). Monthly and annual TDS were considered as target variables in the analysis. In order to evaluate and compare the authenticity of the models, an economic factor (convergence time) and statistical indices of the coefficient of determination (R2), Kling Gupta efficiency (KGE), root mean squared error (RMSE), mean absolute error (MAE), and Nash-Sutcliff efficiency (NSE) were utilized. The results revealed that the hybrid methods used in this study could enhance the classical ANFIS performance in the analysis of the monthly and annual TDS of both stations. For more clarification, the models were ranked using the TOPSIS approach by simultaneously applying the effects of statistical parameters, temporal and spatial change factors, and convergence time. This approach significantly facilitated decision-making in ranking models. The ANFIS-ACOR annual model considering discharge had the best performance in the Vanyar Station;Furthermore, the ANFIS-ACOR monthly model ignoring discharge was outstanding in the Gotvand Station. In total, after utilizing two defined and proposed temporal and spatial change factors, the ANFIS-ACOR and ANFIS-DE hybrid models had the best and worst performance in TDS prediction, respectively.

关键词： Surface water quality TDS metaheuristic algorithms ANFIS Hybrid models TOPSIS

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An initialization approach for metaheuristic algorithms by using Gibbs sampling

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MATHEMATICS AND COMPUTERS IN SIMULATION 2024年 225卷 586-606页

作者： Cuevas, Erik Barba-Toscano, Oscar Escobar, Hector Zaldivar, Daniel Rodriguez-Vazquez, Alma Univ Guadalajara CUCEI Dept Electrofoton Ave Revoluc 1500 Guadalajara 44430 Jal Mexico

Recently, several new initialization techniques have been proposed. Despite their good results in very low dimensions, their performance deteriorated significantly with an increase in the number of dimensions. This paper introduces a new method for initializing metaheuristic algorithms, based on the Gibbs sampling approach. The proposed method samples according to the Gibbs method, a multidimensional Gaussian that completely covers the search space defined by the objective function. In this process, each decision variable is sequentially sampled to ensure that the value of each is dependent only on the value of the variable sampled before it. This process leads to the generation of initial positions with significantly low mutual correlation, which is an advantageous feature that prevents the aggregation of initial solutions in specific areas of the search space. This issue is particularly prevalent in optimization problems with a greater number of dimensions. To test the effectiveness of this method, it was applied in conjunction with a Differential Evolution algorithm. The complete approach has been evaluated using a selection of pertinent and challenging functions in the field. The outcome of these experiments showed that the algorithm can establish a superior set of initial solutions, which allows consistent determination of the global solution, even as the complexity of the problem increases with more dimensions.

关键词： Gibbs Sampling metaheuristic algorithms High-Dimensional Optimization Differential Evolution and Global Solution Search

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Discrete variable optimization of structures subjected to dynamic loads using equivalent static loads and metaheuristic algorithms

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OPTIMIZATION AND ENGINEERING 2022年第2期23卷 643-687页

作者： Al-Bazoon, Mustafa Arora, Jasbir S. Univ Misan Dept Civil Engn Coll Engn Amarah 62001 Maysan Iraq Univ Iowa Iowa Technol Inst Iowa City IA 52242 USA

This paper presents a new computational procedure for optimization of structures subjected to dynamic loads. The optimization problem is formulated with discrete design variables that represent the members from a table of commercially available members. Also, the requirements in the American Institute of Steel Construction (AISC) manual are formulated as constraints. This results in a nondifferentiable optimization problem. In the new procedure, the dynamic load is transformed into equivalent static loads (ESLs). Then the static response optimization problem having discrete design variables is solved using a metaheuristic optimization algorithm. Three methods to calculate the ESLs are investigated. It is found that the ESL cycles cannot converge to the final design. Therefore after a few ESL cycles, the original dynamic loads need to be used in the optimization process. Four example problems are solved to analyze the procedure. Based on this analysis, it is concluded that the new procedure is more efficient compared to a procedure that does not use the ESL cycles because it reduces the total CPU effort to obtain the final design. Also, better final designs are found. The reason is that many more designs are analyzed very efficiently with the ESL procedure.

关键词： Equivalent static loads method metaheuristic algorithms Discrete variables Structural optimization Dynamic loads

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A new framework for reliability-based design optimization using metaheuristic algorithms

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STRUCTURES 2022年 38卷 1210-1225页

作者： Kaveh, Ali Zaerreza, Ataollah Iran Univ Sci & Technol Sch Civil Engn POB 16846-13114 Tehran Iran

This paper presents a new framework for reliability-based design optimization (RBDO) using metaheuristic algorithms based on decoupled methods. The sequential optimization and reliability assessment (SORA) is utilized as a decoupled method in this study. In the present framework, unlike the previous RBDO based on decoupled methods, a metaheuristic is employed on both reliability assessment and the optimization parts. Here, first a new reliability assessment method based on the metaheuristics is introduced. Then, a new termination condition for the metaheuristic inspired by the termination condition of the gradient-based method is presented. In order to investigate the efficiency of the proposed framework, an enhanced shuffled shepherd optimization algorithm (ESSOA) is used as an optimization approach. The efficiency of the proposed framework is evaluated by four well-known RBDO benchmarks investigated in the previous studies using the gradient-based method. Also, the two new RBDO problems are introduced in this study. The results show that the proposed framework can have better performance than the gradient-based method in the RBDO and can easily be utilized in a wide range of RBDO problems.

关键词： Reliability-based design optimization metaheuristic algorithms Reliability assessment method Shuffled shepherd optimization algorithm

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Multilevel image thresholding using entropy of histogram and recently developed population-based metaheuristic algorithms

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EVOLUTIONARY INTELLIGENCE 2017年第1-2期10卷 45-75页

作者： Mousavirad, Seyed Jalaleddin Ebrahimpour-Komleh, Hossein Univ Kashan Dept Comp & Elect Engn Kashan Iran

Multilevel thresholding is one of the most broadly used approaches to image segmentation. However, the traditional techniques of multilevel thresholding are time-consuming, especially when the number of the threshold values is high. Thus, population-based metaheuristic (P-metaheuristic) algorithms can be used to overcome this limitation. P-metaheuristic algorithms are a type of optimization algorithms, which improve a set of solutions using an iterative process. For this purpose, image thresholding problem should be seen as an optimization problem. This paper proposes multilevel image thresholding for image segmentation using several recently presented P-metaheuristic algorithms, including whale optimization algorithm, grey wolf optimizer, cuckoo optimization algorithm, biogeography-based optimization, teaching-learning-based optimization, gravitational search algorithm, imperialist competitive algorithm, and cuckoo search. Kapur's entropy is used as the objective function. To conduct a more comprehensive comparison, the mentioned P-metaheuristic algorithms were compared with five others. Several experiments were conducted on 12 benchmark images to compare the algorithms regarding objective function value, peak signal to noise ratio (PSNR), feature similarity index (FSIM), structural similarity index (SSIM), and stability. In addition, Friedman test and Wilcoxon signed rank test were carried out as the nonparametric statistical methods to compare P-metaheuristic algorithms. Eventually, to create a more reliable result, another objective function was evaluated based on Cross Entropy.

关键词： Kapur's entropy Cross entropy metaheuristic algorithms Image thresholding Statistical test

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Using metaheuristic algorithms to optimize a mixed model-based ground-motion prediction model and associated variance components

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JOURNAL OF SEISMOLOGY 2022年第3期26卷 483-498页

作者： Akhani, Mohsen Pezeshk, Shahram Univ Memphis Dept Civil Engn Memphis TN 38152 USA

In this study, two metaheuristic optimization algorithms are employed to estimate a mixed model-based ground-motion model (GMM) with several variance components. Two optimization algorithms, particle swarm optimization (PSO) and teaching-learning-based optimization (TLBO), are employed to compute regression coefficients and uncertainties of the GMM by considering a one-stage maximum likelihood estimation framework. These optimization models are applied to a complex predictive equation in a way that the results best fit a ground-motion dataset. Uncertainties and the regression coefficients of a functional form of a predictive equation are estimated and compared with other models to show the strengths and the limitations of the proposed approaches. The obtained results provide reliable solutions and demonstrate good accuracy compared to previous search algorithms based on a given dataset of ground-motions. We estimated error metrics for the predicted data, and the results show that using the proposed algorithms provides better results compared to the existing search algorithms.

关键词： metaheuristic algorithms Regression coefficients Uncertainties GMM

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Prediction of Blast-Induced Ground Vibration in a Mine Using Relevance Vector Regression Optimized by metaheuristic algorithms

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NATURAL RESOURCES RESEARCH 2021年第2期30卷 1849-1863页

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

Prediction of ground vibration induced by blasting operations is a crucial challenge to engineers working in surface mines. This study aims to assess the efficiency of two advanced machine learning models in predicting ground vibrations in a granite quarry located in Malaysia. To this end, two intelligent models were proposed by hybridizing the relevance vector regression (RVR) with the grey wolf optimization (GWO) (which formed the RVR-GWO model) and with the bat-inspired algorithm (BA) (which formed the RVR-BA model). To the best of our knowledge, this is the first attempt to predict ground vibration using the RVR-GWO and RVR-BA models. The afore-mentioned models were developed and tested using 95 datasets. Then, the performance of the developed models was statistically checked through four comparative experiments using, among others, mean square error (MSE) and correlation coefficient (R). The results indicated the superiority of the RVR-GWO model over the RVR-BA model in terms of prediction precision. The RVR-GWO model withRof 0.915 and MSE = 7.920 predicted the ground vibration better than the RVR-BA model withRof 0.867 and MSE = 8.551. Accordingly, it was concluded that applying the GWO algorithm to RVR can result in high accuracy in the prediction of blast-induced ground vibration.

关键词： Blasting Ground vibration Relevance vector regression Grey wolf optimization Bat-inspired algorithm metaheuristic algorithms

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Optimization of Laser Cutting Parameters for PMMA Using metaheuristic algorithms

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年第9期49卷 12333-12355页

作者： Urgun, Satilmis Yigit, Halil Fidan, Sinan Sinmazcelik, Tamer Kocaeli Univ Fac Aeronaut & Astronaut Dept Aviat Elect & Elect Izmit Kocaeli Turkiye Kocaeli Univ Fac Technol Dept Informat Syst Engn TR-41001 Izmit Kocaeli Turkiye Kocaeli Univ Fac Aeronaut & Astronaut Dept Airframe & Powerplant Maintenance Izmit Kocaeli Turkiye Kocaeli Univ Fac Engn Mech Engn Dept Izmit Kocaeli Turkiye

This study fixates on determining the optimum laser input parameters that simultaneously meet the desired kerf width and depth during CO2 laser cutting of various polymethylmethacrylate (PMMA) sheets. It has three contributions. The first is to model the cutting process of PMMA by polynomial curve fitting as a function of laser power, laser speed, and standoff distance. R-squared (R-2), adjusted R-2 and root-mean-square error (RMSE) criteria were taken into account to measure the performance of the proposed model. The effect of laser parameters on the process is investigated by analysis of variance (ANOVA) and sensitivity analysis. The second is to optimize the derived nonlinear regression models using genetic algorithm (GA), particle swarm optimization (PSO), whale optimization algorithm (WOA) and ant lion optimization (ALO) metaheuristic methods and compare the performances of the algorithms. The third is to compare the adequacy of the optimization process with the artificial neural network (ANN). The investigations exhibited that the best-fitting polynomials are obtained with the R-2 and adjusted R-2 values of over 99% and 97%, respectively. ANOVA and sensitivity test revealed that the sensitivity of the laser power, which is the most effective parameter, was 150 at low powers and decreased to 0 as the power value increased. When the nozzle distance is 4.1, the proposed metaheuristics gave effective and sufficiently accurate results. PSO stood out in terms of both best cost value (3.49 x 10(-8)) and relative error value (0.19%). The relative error of the ANN was found as 3% in terms of kerf depth.

关键词： PMMA CO2 Laser Optimization metaheuristic algorithms Neural network

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Optimal sensor placement methodology of triaxial accelerometers using combined metaheuristic algorithms for structural health monitoring applications

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STRUCTURES 2023年 51卷 1959-1971页

作者： Mghazli, Mohamed Oualid Zoubir, Zineb Nait-Taour, Abdellah Cherif, Seifeddine Lamdouar, Nouzha El Mankibi, Mohamed Green Energy Pk UM6P IRESEN km2 R206 Benguerir Morocco Univ Lyon ENTPE LTDS UMR CNRS 5513 Vaulx en Velin France Mohammed V Univ Rabat Mohammadia Sch Engineers Civil Engn Lab Rabat Morocco Mohammed VI Polytech Univ Morocco Sch Ind Management EMINES Ben Guerir Morocco Cadi Ayyad Univ Fac Sci & Technol Geoenvironm & Civil Engn Lab L3G Georessources Marrakech 40000 Morocco

Structural health monitoring applications present a valuable opportunity for cities to improve their in-frastructures environmental life cycle, reduce operational maintenance cost and enhance their resiliency through early damage detection capabilities. The dilemma with structural health monitoring resides in balancing between the number of sensors to be installed and the amount of data that needs to be transferred, stored, and managed. These technical complications directly impact the cost efficiency of such solutions. Thus, limiting the wide range deployment of such technology. Henceforth, Optimal Sensor Placement (OSP) methodologies offers the possi-bility to optimize the sensor configuration that will ensure a proper and cost-efficient monitoring system to obtain the maximum modal information related to any infrastructure *** this study, a novel "Modal Assurance Criterion (MAC)"-based methodology with the aim of the optimal sensor placement is presented using a 410 m high rise structure. The Modal Kinetic Energy (MKE) and a MAC -based objective function are combined in the suggested methodology. The optimization problem is solved using a new hybrid metaheuristic algorithm that combines Teaching-Learning-Based Optimization (TLBO), Artificial Bee Colonies (ABC), and Stochastic Paint Optimizer (SPO). With no user-defined parameters, the combination of algorithms exhibits up to 86% better fitness, avoids local optimums due to higher accuracy, and shows promising results for cost calculation optimization because it requires 50% to 70% fewer iterations compared to six evolutionary algorithms that are usually used.

关键词： Optimal sensor placement metaheuristic algorithms Combinatorial optimization Structural health monitoring High rise buildings

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An application on forecasting for stock market prices: hybrid of some metaheuristic algorithms with multivariate adaptive regression splines

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INTERNATIONAL JOURNAL OF INTELLIGENT COMPUTING AND CYBERNETICS 2023年第4期16卷 847-866页

作者： Sabanci, Dilek Kilicarslan, Serhat Adem, Kemal Tokat Gaziosmanpasa Univ Dept Math Tokat Turkiye Bandirma Onyedi Eylul Univ Dept Software Engn Balikesir Turkiye Sivas Univ Sci & Technol Dept Comp Engn Sivas Turkiye

PurposeBorsa Istanbul 100 Index, known as BIST100, is the main indicator to measure the performance of the 100 highest stocks publicly traded in Borsa Istanbul concerning market and trading volume. BIST 100 index prediction is a popular research domain for its complex data structure caused by stock price, commodity, interest rate and exchange rate effects. The study proposed hybrid models using both Genetic, Particle Swarm Optimization, Harmony Search and Greedy algorithms from metaheuristic algorithms approach for dimension reduction, and MARS for ***/methodology/approachThis paper aims to model in the simplest way through metaheuristic algorithms hybridized with the MARS model the effects of stock, commodity, interest and exchange rate variables on BIST 100 during the Covid-19 pandemic period (in the process of closing) between January 2020 and June *** most suitable hybrid model was chosen as PSO & MARS by calculating the RMSE, MSE, GCV, MAE, MAD, MAPE and R2 measurements of training, test and overall dataset to check every model's efficiency. Empirical results demonstrated that the proposed PSO & MARS hybrid modeling procedure gave results both as good as the MARS model and a simpler and non-complex model ***/valueUsing metaheuristic algorithms as a supporting tool for variable selection can help to identify important independent variables and contribute to the establishment of more non-complex ***, test and overall dataset to check every model's efficiency.

关键词： Dimension reduction metaheuristic algorithms MARS Feature selection BIST 100

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