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Prediction of Compressive Strength of Concrete: Critical Comparison of Performance of a Hybrid Machine Learning Model with Standalone Models

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JOURNAL OF MATERIALS IN CIVIL ENGINEERING 2019年第11期31卷

作者： Cook, Rachel Lapeyre, Jonathan Ma, Hongyan Kumar, Aditya Missouri Univ Sci & Technol Dept Mat Sci & Engn B49 McNutt Hall1400 N Bishop Rolla MO 65409 USA Missouri Univ Sci & Technol Dept Civil Architectural & Environm Engn Rolla MO 65409 USA

The use of machine learning (ML) techniques to model quantitative composition-property relationships in concrete has received substantial attention in the past few years. This paper presents a novel hybrid ML model (RF-FFA) for prediction of compressive strength of concrete by combining the random forests (RF) model with the firefly algorithm (FFA). The firefly algorithm is utilized to determine optimum values of two hyper-parameters (i.e., number of trees and number of leaves per tree in the forest) of the RF model in relation to the nature and volume of the dataset. The RF-FFA model was trained to develop correlations between input variables and output of two different categories of datasets;such correlations were subsequently leveraged by the model to make predictions in previously untrained data domains. The first category included two separate datasets featuring highly nonlinear and periodic relationship between input variables and output, as given by trigonometric functions. The second category included two real-world datasets, composed of mixture design variables of concretes as inputs and their age-dependent compressive strengths as outputs. The prediction performance of the hybrid RF-FFA model was benchmarked against commonly used standalone ML models-support vector machine (SVM), multilayer perceptron artificial neural network (MLP-ANN), M5Prime model tree algorithm (M5P), and RF. The metrics used for evaluation of prediction accuracy included five different statistical parameters as well as a composite performance index (CPI). Results show that the hybrid RF-FFA model consistently outperforms the standalone ML models in terms of prediction accuracy-regardless of the nature and volume of datasets.

关键词： Machine learning Concrete Compressive strength Random forests firefly algorithm

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Data clustering based probabilistic UPFC allocation for improving power system reliability considering correlated uncertain variables

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2021年第12期31卷

作者： Rezaeian-Marjani, Saeed Nazarpour, Daryoush Galvani, Sadjad Urmia Univ Dept Power Engn Fac Elect & Comp Engn Orumiyeh Iran

Flexible alternating-current transmission system (FACTS) devices as power electronic-based technologies have been developed to improve the performance of power systems. This paper proposes a probabilistic framework for optimal allocation of unified power flow controller (UPFC) as one of the most beneficial FACTS devices to increase the reliability of the power system. For this purpose, the expected power not served (EPNS) is considered as a reliability index that is obtained based on the summation of required load shedding in all buses during single contingencies, including transmission lines and generators outage. Power demands and wind speed are considered as uncertain input variables. In addition, the correlations among these variables are included in the proposed study method. The well-known particle swarm optimization (PSO) algorithm is used to optimize the proposed objective function. Also, the firefly algorithm (FA) is implemented to determine the total amount of load shedding for the calculation of EPNS, considering each possible generated solution by the PSO. The k-means based data clustering method (DCM) is used for probabilistic evaluation of the problem, for the first time. Also, the correlations among uncertain input variables are modeled with the Cholesky decomposition method. Extraction of statistical information of UPFC parameters for improving power system reliability in a probabilistic framework is one of the most important achievements of the proposed method. This information is very important in deciding on the UPFC sizing. In order to evaluate the effectiveness of the proposed method, the IEEE 14-bus and 30-bus test systems have been used.

关键词： data clustering method firefly algorithm particle swarm optimization probabilistic evaluation reliability unified power flow controller

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Experimental study and optimisation in turning process of EN8 steel using RSM with hybrid algorithm approach

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2019年第4期13卷 242-256页

作者： Thangarasu, S. K. Shankar, S. Prasath, R. Navin Kongu Engn Coll Dept Mechatron Engn Erode 638060 India

The effects of cutting speed, cutting feed, depth of cut on the output responses in turning were investigated for different tool conditions. Three-factor and three-level fractional experiment designs completed with statistical analysis of variance (ANOVA) were performed. Mathematical models for output responses were developed using response surface methodology (RSM). EN8 steel is work piece material and TiN coated cemented carbide is cutting tool. The experiments were conducted for fresh, worn out tools and the output responses are measured. The responses (cutting force, tool wear, surface roughness) are to be minimised. A quadratic model is developed along with combined optimisation of the response using RSM. For each test the output responses was measured for both tools. Finally an optimum cutting speed of 90 mm/min for fresh tool and 270 mm/min for worn out tool was obtained. The results concluded that PSO algorithm produces better optimisation compared to firefly, cuckoo search algorithms.

关键词： firefly algorithm response surface methodology RSM coated carbide inserts design of experiments swarm intelligent technique grey relational analysis cuckoo search algorithm PSO algorithms cutting force tool wear surface roughness

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Hybrid neural network-based metaheuristics for prediction of financial markets: a case study on global gold market

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2023年第3期10卷 1110-1125页

作者： Mamoudan, Mobina Mousapour Ostadi, Ali Pourkhodabakhsh, Nima Fathollahi-Fard, Amir M. Soleimani, Faezeh Univ Tehran Coll Engn Sch Ind & Syst Engn Tehran 111554563 Iran Univ Bamberg Fac Informat Syst & Appl Comp Sci D-96047 Bamberg Germany Islamic Azad Univ Med Sci Coll Management Sch Adv Sci & technol Tehran *** Iran Univ Victoria Peter B Gustavson Sch Business Victoria BC V8P 5C2 Canada Ball State Univ Dept Math Sci Muncie IN 47306 USA

Technical analysis indicators are popular tools in financial markets. These tools help investors to identify buy and sell signals with relatively large errors. The main goal of this study is to develop new practical methods to identify fake signals obtained from technical analysis indicators in the precious metals market. In this paper, we analyze these indicators in different ways based on the recorded signals for 10 months. The main novelty of this research is to propose hybrid neural network-based metaheuristic algorithms for analyzing them accurately while increasing the performance of the signals obtained from technical analysis indicators. We combine a convolutional neural network and a bidirectional gated recurrent unit whose hyperparameters are optimized using the firefly metaheuristic algorithm. To determine and select the most influential variables on the target variable, we use another successful recently developed metaheuristic, namely, the moth-flame optimization algorithm. Finally, we compare the performance of the proposed models with other state-of-the-art single and hybrid deep learning and machine learning methods from the literature. Finally, the main finding is that the proposed neural network-based metaheuristics can be useful as a decision support tool for investors to address and control the enormous uncertainties in the financial and precious metals markets.

关键词： bidirectional gated recurrent unit convolutional neural network firefly algorithm moth-flame optimization algorithm precious metals market prediction

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Impact of demand management with load frequency control in distribution network with high penetration of renewable energy sources

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2021年第11期31卷

作者： Ranjitha, K. Ponnurangam, Sivakumar Rajapandiyan, A. Rajalakshmi Inst Technol Dept Elect & Elect Engn Chennai Tamil Nadu India Rajalakshmi Engn Coll Dept Elect & Elect Engn Chennai Tamil Nadu India Er Perumal Manimekalai Coll Engn Dept Elect & Elect Engn Hosur India

Renewable sources are deployed to meet the growing needs of the energy demand. Integration of these renewable sources in a power system leads to increased complexity due to which it is essential to adapt new control strategies to retain system stability by preserving frequency in a nominal value. Therefore, the objective of the work is to control the load frequency of an interconnected hybrid (wind, solar, thermal) power system by optimizing generator cost while implementing the demand management technique. Deviation in the frequency and tie-line power due to fluctuation of renewable energy sources and load is addressed. For cost optimization, demand management technique was applied to the distribution system comprising flexible loads such as electric vehicle and heat pump. Voltage constraints and network congestion in the system are considered. The cascaded control technique is proposed for load frequency control and firefly based controller for cost optimization in the thermal power system. In the cascaded controller, proportional integral derivative (PID) and atom search optimization algorithm-tuned PID controller are recommended. Performance of the projected approach is compared with a PID controller and fuzzy logic controller-based load frequency control. Simulation studies and the result of various test cases considered clearly show the effectiveness of the controller implemented in the proposed system.

关键词： atom search optimization cascaded controller demand side management firefly algorithm load frequency control

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A novel metaheuristic optimization and soft computing techniques for improved hydrological drought forecasting

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PHYSICS AND CHEMISTRY OF THE EARTH 2024年 135卷

作者： Katipoglu, Okan Mert Ertugay, Nese Elshaboury, Nehal Akturk, Gaye Kartal, Veysi Pande, Chaitanya Baliram Erzincan Binali Yildirim Univ Dept Civil Engn TR-24002 Erzincan Turkiye Housing & Bldg Natl Res Ctr Construct & Project Management Res Inst Giza 12311 Egypt Kirikkale Univ Dept Civil Engn TR-71450 Kirikkale Turkiye Siirt Univ Dept Civil Engn TR-56000 Siirt Turkiye Al Ayen Univ Sci Res Ctr New Era & Dev Civil Engn Res Grp Thi Qar 64001 Iraq

Drought is one of the costliest natural disasters worldwide and weakens countries economically by causing negative impacts on hydropower and agricultural production. Therefore, it is necessary to create drought risk management plans by monitoring and predicting droughts. Various drought indicators have been developed to monitor droughts. This study aims to forecast Streamflow Drought Index (SDI) values with various novel metaheuristic optimization-based Artificial Neural Network (ANN) and deep learning models to predict 1-month lead-time hydrological droughts on 1, 3, and 12-month time scales in the Konya closed basin, one of the driest basins in Turkey. To achieve this goal, the ANN model was integrated with the firefly algorithm (FFA), Genetic algorithm (GA), and Particle Swarm Optimization (PSO) techniques and compared against long shortterm memory (LSTM) networks. While establishing the SDI prediction model, lag values exceeding the 95% confidence intervals in the partial autocorrelation function graphs were used. Model performance was evaluated according to scatter matrix, radar, time series, bee swarm graphs, and statistical performance metrics. As a result of the analysis, the PSO-ANN hybrid model with (R2:0.468-0.931) at station 1611 and the FFA-ANN hybrid model with (R2:0.443-0.916) at station 1612 generally have the highest accuracy.

关键词： Hydrologic drought firefly algorithm Genetic algorithm Nature-inspired optimization Artificial intelligence Deep learning

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Optimal Kernel ELM and Variational Mode Decomposition for Probabilistic PV Power Prediction

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ENERGIES 2020年第14期13卷 3592-3592页

作者： Wu, Xiaomei Lai, Chun Sing Bai, Chenchen Lai, Loi Lei Zhang, Qi Liu, Bo Guangdong Univ Technol Sch Automat Dept Elect Engn Guangzhou 510006 Peoples R China Brunel Univ London Brunel Inst Power Syst Dept Elect & Comp Engn London UB8 3PH England Univ Leeds Fac Engn & Phys Sci Sch Civil Engn Leeds LS2 9JT W Yorkshire England Southern Univ Sci & Technol Dept Math Shenzhen 518055 Peoples R China Guangdong Power Grid Co Ltd Zhaoqing Power Supply Bur Zhaoqing 526060 Peoples R China

A probabilistic prediction interval (PI) model based on variational mode decomposition (VMD) and a kernel extreme learning machine using the firefly algorithm (FA-KELM) is presented to tackle the problem of photovoltaic (PV) power for intra-day-ahead prediction. Firstly, considering the non-stationary and nonlinear characteristics of a PV power output sequence, the decomposition of the original PV power output series is carried out using VMD. Secondly, to further improve the prediction accuracy, KELM is established for each decomposed component and the firefly algorithm is introduced to optimize the penalty factor and kernel parameter. Finally, the point predicted value is obtained through the summation of predicted results of each component and then using the nonlinear kernel density estimation to fit it. The cubic spline interpolation algorithm is applied to obtain the shortest confidence interval. Results from practical cases show that this probabilistic prediction interval could achieve higher accuracy as compared with other prediction models.

关键词： photovoltaic power output prediction variational mode decomposition firefly algorithm kernel extreme learning machine probabilistic prediction interval

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OPEN HYBRID MODEL: A NEW ENSEMBLE MODEL FOR SOFTWARE DEVELOPMENT COST ESTIMATION

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COMPUTING AND INFORMATICS 2020年第6期39卷 1148-1171页

作者： Moradbeiky, Amin Bardsiri, Vahid Khatibi Jafari, Mehdi Islamic Azad Univ Kerman Branch Fac Sci Dept Comp Engn Kerman Iran Islamic Azad Univ Kerman Branch Fac Engn Dept Elect Engn Kerman Iran

Given various features of a software project, it may face different administrative challenges requiring right decisions by software project managers. A major challenge is to estimate software development cost for which different methods have been proposed by many researchers. According to the literature, the capability of a proposed model or method is demonstrated in a specific set of software projects. Hence, the aim of this study is to present a model to take advantage of the capabilities of various software development cost estimation models and methods simultaneously. For this purpose, a new model called "open hybrid model" was proposed based on the firefly algorithm. The proposed model includes an extensible bank of estimation methods. The model also includes an extensible bank of rules to describe the relation between existing methods. Considering project conditions, the proposed model tries to find the best rule for combining estimation methods in the methods bank. Three datasets of real projects were used to evaluate the precision of the proposed model, and the results were compared with those of other 11 methods. The results were compared based on performance parmeters widely used to show the accuracy and stability of estimation models. According to the results, the open hybrid model was able to select the most appropriate methods present in the methods bank.

关键词： Development cost estimation firefly algorithm software project

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Evaluation of bioinspired algorithms for image optimization

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JOURNAL OF ELECTRONIC IMAGING 2022年第4期31卷 041206-041206页

作者： Sharma, Neha Chakraborty, Chinmay Chandigarh Univ Mohali India Birla Inst Technol Jharkhand India

Steganography is a technique for concealing sensitive information behind a specific media source, such as an image, audio, or video file, in such a way that the concealed data are invisible to everyone. Many algorithms have been developed to optimize this process for better output. We aim to identify the different optimization algorithms used in image steganography after embedding the data to improve the resilience, visibility, and payload carrying capacity. Additionally, we highlight several bioinspired algorithms, including particle swarm optimization, ant colony optimization, firefly optimization, and artificial bee colony optimization, and evaluate through performance measures such as peak signal-to-noise ratio (PSNR) and mean square error (MSE). The performance metrics generated from the collected data indicate that the firefly method produced a higher PSNR and a lower MSE, namely 72.42 dB and 0.13, respectively. The methods are evaluated in terms of their ability for data embedding, robustness, and imperceptibility.

关键词： peak signal-to-noise ratio mean square error particle swarm optimization ant colony optimization firefly algorithm artificial bee colony image optimization image steganography bit error rate bioinspired algorithms computationally intelligent algorithms

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Charging UAV deployment for improving charging performance of wireless rechargeable sensor networks via joint optimization approach

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COMPUTER NETWORKS 2021年 201卷 108573-108573页

作者： Liang, Shuang Fang, Zhiyi Sun, Geng Lin, Chi Li, Jiahui Li, Songyang Wang, Aimin Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Peoples R China Jilin Univ Sch Math Changchun 130012 Peoples R China Dalian Univ Technol Sch Software Dalian 116621 Peoples R China

Wireless power transfer based on charging unmanned aerial vehicles (CUAVs) is a promising method for enhancing the lifetime of wireless rechargeable sensor networks (WRSNs). However, how to deploy the CUAVs so that enhancing the charging efficiency is still a challenge. In this work, we formulate a CUAV deployment optimization problem (CUAVDOP) to jointly increase the number of the sensor nodes that within the charging scopes of CUAVs, improve the minimum charging efficiency in the network and reduce the motion energy consumptions of CUAVs. Moreover, the formulated CUAVDOP is analyzed and proven as NP-hard. Then, we propose an improved firefly algorithm (IFA) to solve the formulated CUAVDOP. IFA introduces three improved items that are the opposition-based learning model, attraction model and adaptive step size factor to enhance the performance of conventional firefly algorithm, so that making it more suitable for solving the formulated CUAVDOP. Simulation results demonstrate that the proposed algorithm is effective for dealing with the formulated joint optimization problem. Moreover, the superiority of IFA is verified by tests.

关键词： Wireless rechargeable sensor networks Charging unmanned aerial vehicles Charging efficiency Joint optimization problem firefly algorithm

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