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Experimental Comparisons of metaheuristic algorithms in Solving Combined Economic Emission Dispatch Problem Using Parametric and Non-Parametric Tests

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APPLIED ARTIFICIAL INTELLIGENCE 2018年第9-10期32卷 845-857页

作者： Jevtic, Milena Jovanovic, Nenad Radosavljevic, Jordan Univ Belgrade Tech Fac Bor Vojske Jugoslavije 12 Bor Serbia Univ Pristina Kosovska Mitrovica Fac Tech Sci Kosovska Mitrovica Serbia

In this paper, the parametric and non-parametric statistical tests are applied for comparisons of metaheuristic algorithms' (MAs) behavior in solving Combined Economic Emission Dispatch (CEED) problem. In the last years, in many published papers, a large number of MAs have been proposed to solve CEED problem of different dimensions consisting of different objective functions. In this paper, the statistical tests are applied over samples of results obtained for eight objective functions of CEED problem using the four MAs: Firefly Algorithm, Moth Swarm Algorithm, Adaptive Wind Driven Optimization and Particle Swarm Optimization-Gravitational Search Algorithm. The standard IEEE 30-bus six-generator test system is used. The statistical tests are applied over results obtained for each function and over results obtained for all eight functions simultaneously. The analysis of the results of statistical tests over a single function shows that one MA statistically behaves differently for different functions and one MA is not the best for each function of CEED problem. Therefore, more MAs are more acceptable than one MA for solving a specified CEED problem. However, the analysis of the results of statistical tests over all functions simultaneously shows that all four MAs statistically behave in the same way.

关键词： metaheuristic algorithms PARTICLE swarm optimization PROBLEM solving ELECTRIC generators SEARCH algorithms

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A Hybrid Model Using Bio-Inspired metaheuristic algorithms for Network Intrusion Detection System

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Computers, Materials & Continua 2021年第7期68卷 409-429页

作者： Omar Almomani Department of Computer Network and Information Systems The World Islamic Sciences and Education UniversityAmman11947Jordan

Network Intrusion Detection System(IDS)aims to maintain computer network security by detecting several forms of attacks and unauthorized uses of applications which often can not be detected by *** features selection approach plays an important role in constructing effective network *** bio-inspired metaheuristic algorithms used to reduce features to classify network traffic as abnormal or normal traffic within a shorter duration and showing more ***,this paper aims to propose a hybrid model for network IDS based on hybridization bio-inspired metaheuristic algorithms to detect the generic *** proposed model has two objectives;The first one is to reduce the number of selected features for Network *** objective was met through the hybridization of bioinspired metaheuristic algorithms with each other in a hybrid *** algorithms used in this paper are particle swarm optimization(PSO),multiverse optimizer(MVO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),firefly algorithm(FFA),and bat algorithm(BAT).The second objective is to detect the generic attack using machine learning *** objective was met through employing the support vector machine(SVM),C4.5(J48)decision tree,and random forest(RF)***-NB15 dataset used for assessing the effectiveness of the proposed hybrid ***-NB15 dataset has nine attacks *** generic attack is the highest among ***,the proposed model aims to identify generic *** data showed that J48 is the best classifier compared to SVM and RF for the time needed to build the *** terms of features reduction for the classification,my data show that the MFO-WOA and FFA-GWO models reduce the features to 15 features with close accuracy,sensitivity and F-measure of all features,whereas MVO-BAT model reduces features to 24 features with the same accuracy,sensitivity and F-measure of all features for all classifiers.

关键词： IDS metaheuristic algorithms PSO MVO GWO MFO WOA FFA BAT SVM J48 RF UNSW-NB15 dataset

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A Feature-Weighting Approach Using metaheuristic algorithms to Evaluate the Performance of Handball Goalkeepers

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IEEE ACCESS 2022年 10卷 30556-30572页

作者： Alberto Lopez-Gomez, Julio Romero, Francisco P. Angulo, Eusebio Univ Castilla La Mancha Dept Informat Syst & Technol Ciudad Real 13071 Spain Univ Castilla La Mancha Dept Math Ciudad Real 13071 Spain

Handball experts agree that the most crucial position in a handball match is that of the goalkeeper. Their performance can be a good predictor of a team's ranking in tournaments. Despite this, few studies have been conducted on the relevance of every elite goalkeeper's action to their performance in the match. This paper provides the features or criteria for objectively evaluating a handball goalkeeper based on their actions during a match. For this purpose, the feature-weighting problem is formulated as an optimization problem. The problem is solved using eight metaheuristic algorithms to adjust the weights of the features. Computer experiments using real data from the 2020 Women's and Men's European Handball Championships are carried out with these algorithms. The algorithms optimize the weights based on three metrics. The first metric is to identify the best goalkeeper;the second metric is to identify the top five goalkeepers, regardless of order;and the third metric is to identify and order the top five goalkeepers. A case study is carried out with real data from the 2021 Women's and Men's World Handball Championships, where the best goalkeeper found in both cases with the optimized weights coincide with the best goalkeeper chosen by the International Handball Federation (IHF). Finally, the paper shows the particularities and specific difficulties involved in evaluating handball goalkeepers.

关键词： Measurement Sports metaheuristics Feature extraction Memetics Machine learning Europe metaheuristic algorithms evaluating handball goalkeepers player performance evaluation feature weighting

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A Hybrid Artificial Neural Network with metaheuristic algorithms for Predicting Stock Price

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CYBERNETICS AND SYSTEMS 2017年第4期48卷 365-392页

作者： Ghasemiyeh, Rahim Moghdani, Reza Sana, Shib Sankar Shahid Chamran Univ Ahvaz Dept Management Ahvaz Iran Persian Gulf Univ Ind Management Bushehr Iran Bhangar Mahavidyalaya Dept Math South 24 Parganas Bhangar 743502 India

Most investors change stock prices in long-term businesses because of global turbulence in the markets. Consequently, prediction of stock price is a difficult task because of unknown effective factors in this area although previous researches have shown that neural networks are more effective and accurate in many areas than traditional statistical models. The proposed study aims to predict prices on stock exchange via the hybrid artificial neural network models and metaheuristic algorithms which consist of cuckoo search, improved cuckoo search, improved cuckoo search genetic algorithm, genetic algorithm, and particle swarm optimization. The important 28 variables of value-added knowledge related to stock indices are identified as input parameters in this network, and then real values are obtained (http://***). The results of the proposed model suggest that particle swarm optimization is a dominant metaheuristic approach to predict stock price according to statistical performances of the above approaches.

关键词： Artificial neural network metaheuristic algorithms stock price

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Failure Detection on Electronic Systems Using Thermal Images and metaheuristic algorithms

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IEEE LATIN AMERICA TRANSACTIONS 2020年第8期18卷 1371-1380页

作者： Hernandez, G. Navarro, M. Ortega-Sanchez, N. Oliva, D. Perez-Cisneros, M. Univ Guadalajara CUCEI Div Elect & Comp Guadalajara Jalisco Mexico

Segmentation is considered an important part of image processing. There are commonly used segmentation techniques to improve the thresholding process, such as Otsu and Kapur. The use of these techniques allows us to find the regions of interest in an image by accurately grouping the pixel intensity levels. On the other hand, the use of thermal images makes it possible to obtain information about the temperature of an object and to capture the infrared radiation of the electromagnetic spectrum through cameras that transform the radiated energy into heat information. The segmentation of this kind of images represents a challenging problem that requires a huge computational effort. This work proposes the use of metaheuristic algorithms, combined with segmentation techniques and applied to thermal images, to detect faults and contribute to the preventive maintenance of electronic systems.

关键词： Electronic systems Image segmentation metaheuristic algorithms Preventive maintenance Thermographic analysis Thresholding

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Pile bearing capacity prediction in cold regions using a combination of ANN with metaheuristic algorithms

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STEEL AND COMPOSITE STRUCTURES 2024年第4期51卷 417-440页

作者： Zhou, Jingting Moayedi, Hossein Fatahizadeh, Marieh Varamini, Narges Southwest Jiatong Univ Sch Civil Engn Chengdu Peoples R China Duy Tan Univ Inst Res & Dev Da Nang Vietnam Duy Tan Univ Sch Engn & Technol Da Nang Vietnam INSA Strasbourg CNRS ICUBE UMR 7357 Strasbourg France Shiraz Univ Dept Civil & Environm Engn Shiraz Iran

Artificial neural networks (ANN) have been the focus of several studies when it comes to evaluating the pile's bearing capacity. Nonetheless, the principal drawbacks of employing this method are the sluggish rate of convergence and the constraints of ANN in locating global minima. The current work aimed to build four ANN -based prediction models enhanced with methods from the black hole algorithm (BHA), league championship algorithm (LCA), shuffled complex evolution (SCE), and symbiotic organisms search (SOS) to estimate the carrying capacity of piles in cold climates. To provide the crucial dataset required to build the model, fifty-eight concrete pile experiments were conducted. The pile geometrical properties, internal friction angle phi shaft, internal friction angle phi tip, pile length, pile area, and vertical effective stress were established as the network inputs, and the BHA, LCA, SCE, and SOS -based ANN models were set up to provide the pile bearing capacity as the output. Following a sensitivity analysis to determine the optimal BHA, LCA, SCE, and SOS parameters and a train and test procedure to determine the optimal network architecture or the number of hidden nodes, the best prediction approach was selected. The outcomes show a good agreement between the measured bearing capabilities and the pile bearing capacities forecasted by SCE-MLP. The testing dataset's respective mean square error and coefficient of determination, which are 0.91846 and 391.1539, indicate that using the SCE-MLP approach as a practical, efficient, and highly reliable technique to forecast the pile's bearing capacity is advantageous.

关键词： artificial neural network bearing capacity metaheuristic algorithms pile

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Optimization modelling for a sustainable closed-loop supply chain network using IoT: multiobjective metaheuristic algorithms

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CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH 2024年 1-31页

作者： Madani, Zeinab Goodarzian, Fariba Navaei, Ali Ali, Irfan KN Toosi Univ Technol Dept Ind Engn Tehran *** Iran Heriot Watt Univ Edinburgh Business Sch EBS Currie EH14 4AS Scotland Heriot Watt Univ Sch Social Sci Currie EH14 4AS Scotland Univ Tehran Coll Engn Dept Ind Engn Tehran Iran Aligarh Muslim Univ Dept Stat & Operat Res Aligarh 202002 India

This paper investigates the optimization of a Closed-Loop Supply Chain Management to minimize total system costs and CO2 emissions. In addition, a mathematical model of mixed integer linear programming was designed to study the Closed-Loop Supply Chain Network. An Internet of Things (IoT) based platform was also developed to increase the planned network's speed, accuracy, and security. In this way, customers register their orders online and deposit the money to buy the product. After consuming the product, they can eventually sell it on the same platform when the waste is sent for recycling. In this context, the best location for the plant's construction and the amount of transportation in the network are selected for this problem. By generalizing this problem to a larger dimension, the number of decision variables of the model increases, and such a problem becomes difficult to solve. Therefore, metaheuristic algorithms such as Non-Dominated Genetic Sorting Algorithm II, Multiple Objective Particle Swarm Optimization, Multiobjective Grasshopper Optimization Algorithm, Multiobjective Salp Swarm Algorithm and Multiobjective Ant Lion Optimizer have been used to solve this Bi-Objective Optimization Problem. This paper proposes an optimization model for designing a sustainable SCLSCN that incorporates IoT technology. Moreover, several metaheuristic algorithms are used to solve the optimization problem. A real case study on plastic recycling was conducted in Mazandaran province to investigate the applicability of the model. The Taguchi method is used to adjust the parameters to obtain reliable results for these algorithms. In addition, we compared the responses of each algorithm to select the better solution and evaluate the effectiveness of each algorithm in the face of such a problem.

关键词： Closed loop supply chain Optimization Sustainability Operations research metaheuristic algorithms

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Optimization of active controlled system for structures using metaheuristic algorithms

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EARTHQUAKES AND STRUCTURES 2024年第5期27卷 401-417页

作者： Mehta, Nirmal S. Bhaiya, Vishisht SVNIT Surat Dept Civil Engn Surat India

This study presents a method for optimization of weighting matrices of the linear quadratic regulator (LQR) control algorithm in order to design an optimal active control system using metaheuristic algorithms. The LQR is a widely used control technique in engineering for designing optimal controllers for linear systems by minimizing a quadratic cost function. However, the performance of the LQR strongly depends on the appropriate selection of weighting matrices, which are usually determined by some thumb rule or exhaustive search method. In the present study, for the optimization of weighting matrices, four metaheuristic algorithms including, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO) and, Whale Optimization Algorithm (WOA) are considered. To generate optimal weighting matrices, the objective function used consists of displacement and absolute acceleration. During the optimization process, a response effectiveness factor is also checked for displacement and acceleration as a constraint for the proper selection of weighting matrices. To study the effectiveness of optimized active control system to those for the exhaustive search method, the various controlled responses of the system are compared with the corresponding uncontrolled system. The optimized weighting matrices effectively reduce the displacement, velocity, and acceleration responses of the structure. Based on the simulation study, it can be observed that GWO performs well compared to the PSO, GA, and WO algorithms. By employing metaheuristic algorithms, this study showcases a more efficient and effective approach to finding optimal weighting matrices, thereby enhancing the performance of active control systems.

关键词： active control system genetic algorithm grey wolf optimization linear quadratic regulator metaheuristic algorithms optimization particle swarm optimization whale optimization algorithm

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Application of metaheuristic algorithms to the identification of nonlinear magneto-viscoelastic constitutive parameters

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JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS 2018年 464卷 116-131页

作者： Pelteret, Jean-Paul Walter, Bastian Steinmann, Paul Univ Erlangen Nurnberg Chair Appl Mech Paul Gordan Str 3 D-91052 Erlangen Germany

metaheuristic algorithms offer a robust and convenient method to solve highly nonlinear optimisation problems in engineering applications. In this work we evaluate the effectiveness of a collection of canonical algorithms at performing parameter identification for nonlinear constitutive laws that describe coupled, magnetic-field responsive materials. To achieve this, we define an objective function that captures the influence of many physical measurements recorded during experimental analysis of a coupled material, and incorporates the influence of experimental uncertainty. A benchmark of the algorithms is conducted through the evaluation of a magneto-visco-elastic material by means of numerically-derived parallel-plate rotational rheometry. The effectiveness of each algorithm at matching the fictitious, but representative, experimental data was considered using two different metrics. In addition to the ranking based on a non-parametric statistical test, we consider an ad hoc criterion that accounts for only the top performing candidate solutions. It is determined that the continuous real and discrete bitstring genetic algorithm provide the best overall performance in terms of the accuracy of the predicted parameters, while globally-elitist simulated annealing provides the best compromise between accuracy and computational efficiency. When experimental uncertainties exist (which is always the case for data determined within a laboratory setting), it has been observed that the strong link between constitutive parameters and physical material properties, which is typically assumed, no longer holds. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Nonlinear magneto-visco-elasticity Parameter identification metaheuristic algorithms Constitutive model Experimental uncertainty

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Optimization of nearest neighbor classifiers via metaheuristic algorithms for credit risk assessment

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JOURNAL OF GLOBAL OPTIMIZATION 2008年第2期42卷 279-293页

作者： Marinakis, Yannis Marinaki, Magdalene Doumpos, Michael Matsatsinis, Nikolaos Zopounidis, Constantin Tech Univ Crete Dept Prod Engn & Management Khania 73100 Greece Tech Univ Crete Dept Prod Engn & Management Decis Support Syst Lab Khania 73100 Greece Tech Univ Crete Dept Prod Engn & Management Ind Syst Control Lab Khania 73100 Greece

The classification problem consists of using some known objects, usually described by a large vector of features, to induce a model that classifies others into known classes. The present paper deals with the optimization of Nearest Neighbor Classifiers via metaheuristic algorithms. The metaheuristic algorithms used include tabu search, genetic algorithms and ant colony optimization. The performance of the proposed algorithms is tested using data from 1411 firms derived from the loan portfolio of a leading Greek Commercial Bank in order to classify the firms in different groups representing different levels of credit risk. Also, a comparison of the algorithm with other methods such as UTADIS, SVM, CART, and other classification methods is performed using these data.

关键词： metaheuristic algorithms feature selection classification credit risk assessment

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