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Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm

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APPLIED ENERGY 2017年 190卷 390-407页

作者： Wang, Deyun Luo, Hongyuan Grunder, Olivier Lin, Yanbing Guo, Haixiang China Univ Geosci Sch Econ & Management Wuhan 430074 Peoples R China China Univ Geosci Mineral Resource Strategy & Policy Res Ctr Wuhan 430074 Peoples R China Univ Bourgogne Franche Comte UTBM IRTES Rue Thierry Mieg F-90010 Belfort France

In the deregulated competitive electricity market, the price which reflects the relationship between electricity supply and demand is one of the most important elements, making it crticial for all market participants to precisely forecast the electricity price. However, electricity price series usually has complex features such as non-linearity, non-stationarity and volatility, which makes the price forecasting turn out to be very difficult. In order to improve the accuracy of electricity price forecasting, this paper first proposes a two-layer decomposition technique and then develops a hybrid model based on fast ensemble empirical mode decomposition (FEEMD), variational mode decomposition (VMD) and back propagation (BP) neural network optimized by firefly algorithm (FA). The proposed model is unique in the sense that VMD is specifically applied to further decompose the high frequency intrinsic mode functions (IMFs) generated by FEEMD into a number of modes in order to improve the forecast accuracy. To validate the effectiveness and accuracy of the proposed model, three electricity price series respectively collected from the real-world electricity markets of Australia and France are adopted to conduct the empirical study. The results indicate that the proposed model outperforms the other considered models over horizons of one-step, two-step, four-step and six-step ahead forecasting, which shows that the proposed model has superior performances for both one-step and multi-step ahead forecasting of electricity price. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Electricity price forecasting Multi-step ahead Fast ensemble empirical mode decomposition Variational mode decomposition firefly algorithm Back propagation neural network

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Multi-level Segmentation of Fruits Using Modified firefly algorithm (Apr, 10.1007/s12161-022-02290-7, 2022)

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FOOD ANALYTICAL METHODS 2022年第11期15卷 2901-2901页

作者： Chaturvedi, Rekha Sharma, Abhay Bhargava, Anuja Rajpurohit, Jitendra Gothwal, Pushpa Manipal Univ Jaipur Jaipur Rajasthan India GLA Univ Mathura India Univ Petr & Energy Studies Dehra Dun Uttarakhand India

The data explosion caused by the Internet and its applications has given researchers immense scope for data analysis. A large amount of data is available in form of images. Image processing is required for better understandability of an image. Various image processing steps are available for improving the image in different application areas. Various applications like medical imaging, face recognition, biometric security, fruit quality evaluation, and traffic surveillance depend only on image and its analysis. This analysis in several applications is highly dependent on the outcome of image segmentation. This paper focuses on the good segmentation of different kinds of fruits through multi-level thresholding. In this paper, multi-level segmentation based on modified firefly algorithm (FA) with Kapur’s, Tsallis, and fuzzy entropy is proposed. FA is used to optimize fuzzy parameters for obtaining optimal thresholds. The levy flight and local search are implemented with FA. The various experiments have been performed on apple, banana, mango, and orange images with the distinct threshold (i.e., 2, 3, 4) values. The proposed algorithm has been estimated quantitatively and qualitatively by using parameters like peak signal-to-noise ratio (PSNR) and structured similarity index metric (SSIM).

关键词： Entropy Variance Segmentation Thresholding firefly algorithm Optimization

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Tuning parameter estimation in SCAD-support vector machine using firefly algorithm with application in gene selection and cancer classification

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COMPUTERS IN BIOLOGY AND MEDICINE 2018年 103卷 262-268页

作者： Al-Thanoon, Niam Abdulmunim Qasim, Omar Saber Algamal, Zakariya Yahya Univ Mosul Dept Operat Res & Artificial Intelligence Mosul Iraq Univ Mosul Dept Math Mosul Iraq Univ Mosul Dept Stat & Informat Mosul Iraq

In cancer classification, gene selection is one of the most important bioinformatics related topics. The selection of genes can be considered to be a variable selection problem, which aims to find a small subset of genes that has the most discriminative information for the classification target. The penalized support vector machine (PSVM) has proved its effectiveness at creating a strong classifier that combines the advantages of the support vector machine and penalization. PSVM with a smoothly clipped absolute deviation (SCAD) penalty is the most widely used method. However, the efficiency of PSVM with SCAD depends on choosing the appropriate tuning parameter involved in the SCAD penalty. In this paper, a firefly algorithm, which is a metaheuristic continuous algorithm, is proposed to determine the tuning parameter in PSVM with SCAD penalty. Our proposed algorithm can efficiently help to find the most relevant genes with high classification performance. The experimental results from four benchmark gene expression datasets show the superior performance of the proposed algorithm in terms of classification accuracy and the number of selected genes compared with competing methods.

关键词： SCAD Gene selection Cancer classification Penalized support vector machine firefly algorithm

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IFACNN: efficient DDoS attack detection based on improved firefly algorithm to optimize convolutional neural networks

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第2期19卷 1280-1303页

作者： Wang, Jiushuang Liu, Ying Feng, Huifen Beijing Jiaotong Univ Natl Engn Lab Interconnect Technol Next Generat I Beijing Peoples R China

Network security has become considerably essential because of the expansion of internet of things (IoT) devices. One of the greatest hazards of today's networks is distributed denial of service (DDoS) attacks, which could destroy critical network services. Recent numerous IoT devices are unsuspectingly attacked by DDoS. To securely manage IoT equipment, researchers have introduced software-defined networks (SDN). Therefore, we propose a DDoS attack detection scheme to secure the real-time in the software-defined the internet of things (SD-IoT) environment. In this article, we utilize improved firefly algorithm to optimize the convolutional neural network (CNN), to provide detection for DDoS attacks in our proposed SD-IoT framework. Our results demonstrate that our scheme can achieve higher than 99% DDoS behavior and benign traffic detection accuracy.

关键词： software-defined internet of things distributed denial of service firefly algorithm convolutional neural network detect attacks

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Shape and Size Optimization of Truss Structures under Frequency Constraints Based on Hybrid Sine Cosine firefly algorithm

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Computer Modeling in Engineering & Sciences 2023年第1期134卷 405-428页

作者： Ran Tao Xiaomeng Yang Huanlin Zhou Zeng Meng School of Civil Engineering Hefei University of TechnologyHefei230009China

Shape and size optimization with frequency constraints is a highly nonlinear problem withmixed design variables,non-convex search space,and multiple local ***,a hybrid sine cosine firefly algorithm(HSCFA)is proposed to acquire more accurate solutions with less finite element *** full attraction model of firefly algorithm(FA)is analyzed,and the factors that affect its computational efficiency and accuracy are revealed.A modified FA with simplified attraction model and adaptive parameter of sine cosine algorithm(SCA)is proposed to reduce the computational complexity and enhance the convergence ***,the population is classified,and different populations are updated by modified FA and SCA ***,the random search strategy based on Lévy flight is adopted to update the stagnant or infeasible solutions to enhance the population *** selection technique is applied to save the promising solutions and further improve the convergence ***,the adaptive penalty function is employed to deal with the ***,the performance of HSCFA is demonstrated through the numerical examples with nonstructural masses and frequency *** results show that HSCFA is an efficient and competitive tool for shape and size optimization problems with frequency constraints.

关键词： firefly algorithm sine cosine algorithm frequency constraints structural optimization

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A novel efficient substitution-box design based on firefly algorithm and discrete chaotic map

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NEURAL COMPUTING & APPLICATIONS 2019年第11期31卷 7201-7210页

作者： Ahmed, Hussam A. Zolkipli, Mohamad Fadli Ahmad, Musheer Univ Malaysia Pahang Fac Comp Syst & Software Engn Lebuhraya Tun Razak Kuantan 26300 Pahang Darul Ma Malaysia Jamia Millia Islamia Dept Comp Engn New Delhi 110025 India

Substitution boxes are essential nonlinear components responsible to impart strong confusion and security in most of modern symmetric ciphers. Constructing efficient S-boxes has been a prominent topic of interest for security experts. With an aim to construct cryptographically efficient S-box, a novel scheme based on firefly (FA) optimization and chaotic map is proposed in this paper. The anticipated approach generates initial S-box using chaotic map. The meta-heuristic FA is applied to find notable configuration of S-box that satisfies the criterions by guided search for near-optimal features by minimizing fitness function. The performance of proposed approach is assessed through well-established criterions such as bijectivity, nonlinearity, strict avalanche criteria, bit independence criteria, differential uniformity, and linear approximation probability. The obtained experimental results are compared with some recently investigated S-boxes to demonstrate that the proposed scheme has better proficiency of constructing efficient S-boxes.

关键词： Substitution box firefly algorithm Discrete chaotic map Symmetric ciphers

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Support vector regression with modified firefly algorithm for stock price forecasting

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APPLIED INTELLIGENCE 2019年第5期49卷 1658-1674页

作者： Zhang, Jun Teng, Yu-Fan Chen, Wei Capital Univ Econ & Business Sch Informat Beijing Peoples R China

The support vector regression (SVR) has been employed to deal with stock price forecasting problems. However, the selection of appropriate kernel parameters is crucial to obtaining satisfactory forecasting performance. This paper proposes a novel approach for forecasting stock prices by combining the SVR with the firefly algorithm (FA). The proposed forecasting model has two stages. In the first stage, to enhance the global convergence speed, a modified version of the FA, which is termed the MFA, is developed in which the dynamic adjustment strategy and the opposition-based chaotic strategy are introduced. In the second stage, a hybrid SVR model is proposed and combined with the MFA for stock price forecasting, in which the MFA is used to optimize the SVR parameters. Finally, comparative experiments are conducted to show the applicability and superiority of the proposed methods. Experimental results show the following: (1) Compared with other algorithms, the proposed MFA algorithm possesses superior performance, and (2) The proposed MFA-SVR prediction procedure can be considered as a feasible and effective tool for forecasting stock prices.

关键词： Stock price forecasting Support vector regression firefly algorithm Opposition-based learning Chaotic

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An optimistic firefly algorithm-based deep learning approach for sentiment analysis of COVID-19 tweets

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第2期20卷 2382-2407页

作者： Swapnarekha, H. Nayak, Janmenjoy Behera, H. S. Dash, Pandit Byomakesha Pelusi, Danilo Aditya Inst Technol & Management AITAM Dept Informat Technol Tekkali 532201 Andhra Pradesh India Maharaja Sriram Chandra Bhanja Deo Univ Dept Comp Sci Baripada 757003 Odisha India Veer Surendra Sai Univ Technol Dept Informat Technol Burla 768018 India Univ Teramo Commun Sci Coste St Agostino Campus I-64100 Teramo Italy

The unprecedented rise in the number of COVID-19 cases has drawn global attention, as it has caused an adverse impact on the lives of people all over the world. As of December 31, 2021, more than 2,86,901,222 people have been infected with COVID-19. The rise in the number of COVID-19 cases and deaths across the world has caused fear, anxiety and depression among individuals. Social media is the most dominant tool that disturbed human life during this pandemic. Among the social media platforms, Twitter is one of the most prominent and trusted social media platforms. To control and monitor the COVID-19 infection, it is necessary to analyze the sentiments of people expressed on their social media platforms. In this study, we proposed a deep learning approach known as a long short -term memory (LSTM) model for the analysis of tweets related to COVID-19 as positive or negative sentiments. In addition, the proposed approach makes use of the firefly algorithm to enhance the overall performance of the model. Further, the performance of the proposed model, along with other state-of-the-art ensemble and machine learning models, has been evaluated by using performance metrics such as accuracy, precision, recall, the AUC-ROC and the F1-score. The experimental results reveal that the proposed LSTM + firefly approach obtained a better accuracy of 99.59% when compared with the other state-of-the-art models.

关键词： long short-term memory COVID-19 sentiment analysis tweets firefly algorithm

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A new fuzzy membership assignment and model selection approach based on dynamic class centers for fuzzy SVM family using the firefly algorithm

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2016年第3期24卷 1797-U5073页

作者： Almasi, Omid Naghash Rouhani, Modjtaba Islamic Azad Univ Mashhad Branch Young Researchers & Elite Club Mashhad Iran Ferdowsi Univ Mashhad Fac Engn Dept Comp Engn Mashhad Iran

The support vector machine (SVM) is a powerful tool for classification problems. Unfortunately, the training phase of the SVM is highly sensitive to noises in the training set. Noises are inevitable in real-world applications. To overcome this problem, the SVM was extended to a fuzzy SVM by assigning an appropriate fuzzy membership to each data point. However, suitable choice of fuzzy memberships and an accurate model selection raise fundamental issues. In this paper, we propose a new method based on optimization methods to simultaneously generate appropriate fuzzy membership and solve the model selection problem for the SVM family in linear/nonlinear and separable/nonseparable classification problems. Both the SVM and least square SVM are included in the study. The fuzzy memberships are built based on dynamic class centers. The firefly algorithm (FA), a recently developed nature-inspired optimization algorithm, provides variation in the position of class centers by changing their attributes' values. Hence, adjusting the place of the class center can properly generate accurate fuzzy memberships to cope with both attribute and class noises. Furthermore, through the process of generating fuzzy memberships, the FA can choose the best parameters for the SVM family. A set of experiments is conducted on nine benchmarking data sets of the UCI data base. The experimental results show the effectiveness of the proposed method in comparison to the seven well-known methods of the SVM literature.

关键词： Support vector machines fuzzy support vector machine fuzzy membership function model selection problem firefly algorithm classification noise

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Discrete Optimum Design of Truss Structures by an Improved firefly algorithm

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ADVANCES IN STRUCTURAL ENGINEERING 2014年第10期17卷 1517-1530页

作者： Baghlani, A. Makiabadi, M. H. Sarcheshmehpour, M. Shiraz Univ Technol Fac Civil & Environm Engn Shiraz Iran Sharif Univ Technol Fac Civil Engn Tehran Iran

This paper presents an improved firefly algorithm (FA) for fast optimization of truss structures with discrete variables. The enhanced accelerated firefly algorithm (AFA) is a simple, but very effective modification of FA. In order to investigate the performance and robustness of the proposed algorithm, some benchmark (structural optimization) problems are solved and the results are compared with FA and other algorithms. The results show that in some test cases, AFA not only finds lighter structures compared to other algorithms, but also converges faster. In the rest test cases, the optimal solutions are found with very less computational effort. The study also shows that the proposed AFA remarkably improves stability of the firefly algorithm in discrete design of truss structures.

关键词： truss structures firefly algorithm discrete variables size optimization

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