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firefly algorithm (FA) based particle filter method for visual tracking

OPTIK 2015年第18期126卷 1705-1711页

作者： Gao, Ming-Liang Li, Li-Li Sun, Xian-Ming Yin, Li-Ju Li, Hai-Tao Luo, Dai-Sheng Shandong Univ Technol Sch Elect & Elect Engn Zibo 255049 Peoples R China Sichuan Univ Coll Elect & Informat Engn Chengdu 610065 Peoples R China

Particle filter algorithm has been proven to be a powerful tool in solving visual tracking problems. However, the problem of sample impoverishment which is brought by the procedure of re-sampling is a main handicap of the particle filter. In this work, an improved particle filter based on firefly algorithm is proposed to solve this problem. The particles in the particle filter are optimized using firefly algorithm before re-sampling. Thus, the number of meaningful particles can be increased, and the particles can approximate the true state of the target more accurately. Experimental results on visual tracking show that the proposed algorithm outperforms the standard particle filter and it can track targets robustly in various challenging conditions. (C) 2015 Elsevier GmbH. All rights reserved.

关键词： Visual tracking Particle filter Particle impoverishment firefly algorithm

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firefly algorithm for Discrete Optimization Problems: A Survey

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KSCE JOURNAL OF CIVIL ENGINEERING 2017年第2期21卷 535-545页

作者： Tilahun, Surafel Luleseged Ngnotchouye, Jean Medard T. Univ KwaZulu Natal Sch Math Stat & Comp Sci ZA-3209 Pietermaritzburg South Africa

firefly algorithm is a nature-inspired metaheuristic algorithm inspired by the flashing behavior of fireflies. It is originally proposed for continuous problems. However, due to its effectiveness and success in solving continuous problems, different studies are conducted in modifying the algorithm to suit discrete problems. Many engineering as well as optimization problems from other disciplines involve discrete variables. Recent reviews on the application and modifications of firefly algorithm mainly focus on continuous problems. This paper is devoted to the detailed review of the modifications done on firefly algorithm in order to solve optimization problems with discrete variables. Hence, advances on the application of firefly algorithm for optimization problems with binary, integer as well as mixed variables will be discussed. Possible future works will also be highlighted.

关键词： firefly algorithm optimization nature-inspired algorithm discrete optimization discrete firefly algorithm

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firefly algorithm-Based Semi-Supervised Learning With Transformer Method for Shore Power Load Forecasting

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IEEE ACCESS 2023年 11卷 77359-77370页

作者： Li, Weihao Zuo, Youhong Su, Tao Zhao, Weiyou Ma, Xiaoxue Cui, Guangkai Wu, Jiabin Song, Yu Tianjin Survey & Design Inst Water Transport Engn Tianjin 300450 Peoples R China Hehai Univ Dayu Coll Nanjing 210098 Jiangsu Peoples R China Tianjin Univ Technol Sch Elect Engn & Automat Tianjin 300384 Peoples R China

Load forecasting of shore power (SP) plays an important role in the power decision-making of the electrical grid due to docked ships are necessary to plug into the electrical grid. However, obtaining a large amount of labeled data on docked ships is time-consuming, presenting a challenge for Shore Power Load Forecasting. Additionally, multiple raw information entries for docked ships could lead to feature redundancy. To address these issues, we proposed a novel three-stage load forecasting method which includes attributive feature selection, semi-supervised learning (SSL) method for the mean of load distribution prediction, and a transformer-based model for variance prediction. Firstly, firefly algorithm (FA) is adopted to extract representative attribute features of docked ships to deal with the feature redundancy. Next, the selected feature set and label set are divided into two parts: a few labeled data and a large amount of labeled data. And we propose a p -model-based SSL method to predict the load distribution. Finally, we propose a transformer-based model to predict the variance of load distribution. Our model takes into account all historical load data of each docked ship for context learning. Further, we consider that the attribute features would also affect the variance prediction, so the latent features of the p -model are served as the initial condition which concatenates historical load data. We evaluated our model using 328 power load data from various ships that berth at Zhenjiang Port with shore power, totaling approximately 21,521 hours. The experiments prove the accuracy and efficiency of our proposed model, producing promising forecasting results.

关键词： Shore power load forecasting firefly algorithm semi-supervised learning transformer

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firefly algorithm in optimization of queueing systems

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BULLETIN OF THE POLISH ACADEMY OF SCIENCES-TECHNICAL SCIENCES 2012年第2期60卷 363-368页

作者： Kwiecien, J. Filipowicz, B. AGH Univ Sci & Technol Dept Automat PL-30059 Krakow Poland

Queueing theory provides methods for analysis of complex service systems in computer systems, communications, transportation networks and manufacturing. It incorporates Markovian systems with exponential service times and a Poisson arrival process. Two queueing systems with losses are also briefly characterized. The article describes firefly algorithm, which is successfully used for optimization of these queueing systems. The results of experiments performed for selected queueing systems have been also presented.

关键词： queueing systems firefly algorithm

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firefly algorithm, stochastic test functions and design optimisation

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2010年第2期2卷 78-84页

作者： Yang, Xin-She Univ Cambridge Dept Engn Cambridge CB2 1PZ England

Modern optimisation algorithms are often metaheuristic, and they are very promising in solving NP-hard optimisation problems. In this paper, we show how to use the recently developed firefly algorithm to solve non-linear design problems. For the standard pressure vessel design optimisation, the optimal solution found by FA is far better than the best solution obtained previously in the literature. In addition, we also propose a few new test functions with either singularity or stochastic components but with known global optimality and thus they can be used to validate new optimisation algorithms. Possible topics for further research are also discussed.

关键词： firefly algorithm FA design optimisation metaheuristic stochastic test function particle swarm optimisation PSO

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firefly algorithm versus genetic algorithm as powerful variable selection tools and their effect on different multivariate calibration models in spectroscopy: A comparative study

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SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2017年第0期170卷 117-123页

作者： Attia, Khalid A. M. Nassar, Mohammed W. I. El-Zeiny, Mohamed B. Serag, Ahmed Al Azhar Univ Fac Pharm Dept Pharmaceut Analyt Chem Cairo 11751 Egypt Modern Univ Technol & Informat MTI Fac Pharm Dept Analyt Chem Al Hudak Al Wosta Cairo 12582 Egypt

For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration. (C) 2016 Elsevier B.V. All rights reserved.

关键词： firefly algorithm Genetic algorithm Concentration residual augmented classical least squares Artificial neural network Support vector regression

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firefly algorithm HYBRIDIZED WITH GENETIC algorithm FOR MULTI-OBJECTIVE INTEGRATED PROCESS PLANNING AND SCHEDULING

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2024年第7期20卷 2310-2328页

作者： Ri, Kwang-won Mun, Kyong-ho Kim Il Sung Univ Inst Nat Sci Pyongyang South Korea Kim Il Sung Univ Fac Elect Automat Pyongyang South Korea

The multi-objective integrated process planning and scheduling (MOIPPS) problem has a huge search space and complex technical constraints. Therefore, there is considerable difficulty in obtaining efficient solutions, and hence, metaheuristic-based solution algorithms have been actively introduced. In our paper, we propose a method to obtain a set of Pareto solutions using a firefly algorithm hybridized with a genetic algorithm for the MOIPPS problem. We considered a MOIPPS problem model that simultaneously optimizes the makespan, total flow time and total tardiness, maximum machine workload and total machine workload. Several different scale instances have been employed to evaluate the performance of the proposed algorithm. The results show that the proposed algorithm has excellent performance in solving the MOIPPS problem.

关键词： Integrated process planning and scheduling multi-objective optimization genetic algorithm firefly algorithm

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A support vector machine firefly algorithm-based model for global solar radiation prediction

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SOLAR ENERGY 2015年 115卷 632-644页

作者： Olatomiwa, Lanre Mekhilef, Saad Shamshirband, Shahaboddin Mohammadi, Kasra Petkovic, Dalibor Sudheer, Ch Univ Malaya Dept Elect Engn Fac Engn Power Elect & Renewable Energy Res Lab PEARL Kuala Lumpur 50603 Malaysia Univ Malaya Fac Comp Sci & Informat Technol Dept Comp Syst & Technol Kuala Lumpur 50603 Malaysia Fed Univ Technol Dept Elect & Elect Engn Minna Nigeria Univ Kashan Fac Mech Engn Kashan Iran Univ Nis Fac Mech Engn Dept Mechatron & Control Nish 18000 Serbia ITM Univ Dept Civil & Environm Engn Gurgaon 122017 Haryana India

In this paper, the accuracy of a hybrid machine learning technique for solar radiation prediction based on some meteorological data is examined. For this aim, a novel method named as SVM-FFA is developed by hybridizing the Support Vector Machines (SVMs) with firefly algorithm (FFA) to predict the monthly mean horizontal global solar radiation using three meteorological parameters of sunshine duration ((n) over bar), maximum temperature (T-max)) and minimum temperature (T-min) as inputs. The predictions accuracy of the proposed SVM-FFA model is validated compared to those of Artificial Neural Networks (ANN) and Genetic Programming (GP) models. The root mean square (RMSE), coefficient of determination (R-2), correlation coefficient (r) and mean absolute percentage error (MAPE) are used as reliable indicators to assess the models' performance. The attained results show that the developed SVM FFA model provides more precise predictions compared to ANN and GP models, with RMSE of 0.6988, R-2 of 0.8024, r of 0.8956 and MAPE of 6.1768 in training phase while, RMSE value of 1.8661, R-2 value of 0.7280, r value of 0.8532 and MAPE value of 11.5192 are obtained in the testing phase. The results specify that the developed SVM FFA model can be adjudged as an efficient machine learning technique for accurate prediction of horizontal global solar radiation. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Support vector machine firefly algorithm Hybrid model Global solar radiation prediction Meteorological parameters

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firefly algorithm with non-homogeneous population: A case study in economic load dispatch problem

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2021年第3期72卷 519-534页

作者： Kapelinski, Kleber Juchem Neto, Joao Plinio dos Santos, Eduardo Machado Univ Fed Pampa UNIPAMPA Alegrete Brazil Univ Fed Rio Grande do Sul Porto Alegre RS Brazil

The economic load dispatch problem is a very important practical problem in electric power systems operation, where an efficient solution can lead to cost and emissions reductions. The use of metaheuristics, as the firefly algorithm, for the solution of economic load dispatch problems has been shown to be a more appropriate alternative than classical optimisation methods. In this work, we have proposed a new variant for the firefly algorithm, considering a non-homogeneous population. This new variant was validated using ten benchmark functions, and then applied to solve a 15-units economic load dispatch problem, considering transmission line losses, power limits, ramp limits and prohibited zones for each generator, and a 13-units non-convex system with valve-point loading effect in the cost function and power generation limits. The results show that the proposed non-homogeneous variant is able to reach better solutions than the original firefly algorithm, using both penalty and non-penalty power balance constraint handling methods. In general, the non-penalty approach reached better results than the penalty method. Moreover, the new variant presented relatively competitive results for the economic load dispatch problems considered, when compared to the best results found in the literature.

关键词： firefly algorithm non-homogeneous population metaheuristics optimisation economic load dispatch problem

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firefly algorithm based LCL filtered grid-tied STATCOM design for reactive power compensation in SCIG based Micro-grid

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ENERGY REPORTS 2022年 8卷 723-740页

作者： Saxena, Nitin Kumar Gupta, Atma Ram Mekhilef, Saad Gao, Wenzhong D. Kumar, Ashwani Gupta, Varun Netto, Roberto Silva Kanungo, Abhas KIET Grp Inst Elect & Elect Engn Dept Delhi Ncr Ghaziabad India Natl Inst Technol Elect Engn Dept Kurukshetra Haryana India Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic Australia Univ Tenaga Nas Coll Engn Dept Elect Engn Kajang Selangor Malaysia Univ Malaya Dept Elect Engn Power Elect & Renewable Energy Res Lab Kuala Lumpur Malaysia Denver Univ Denver CO USA Ctr Univ Facens Sorocaba Brazil KIET Grp Inst Elect & Commun Engn Dept Delhi Ncr Ghaziabad India

Microgrids are facing several operational and control issues while integrating with the grid. To deal with it, STATCOM, as one of the emerging power converter circuits, is connected with such microgrids. STATCOM with microgrid introduces current harmonic, inherent resonance, and active power losses related to high switching frequency. Filter design can help attenuate these effects and maintain the predefined standards as in IEEE 519-1992 and IEEE P1547.2-2003. There are also some more points of concern about filter design. Inappropriate filter types and parameters may cause worse filtering, reactive power surplus production, and low power factor. Therefore, this paper suggests that the reactive power compensation capability, as a local area problem, must be attended through the proper designing of LCL filtered grid-tied STATCOM. The parameters are estimated through transient analysis, power quality, and power balance studies for proposed Micro-grid and the results obtained are compared using nature-inspired algorithms such as GA, PSO, and FA over conventional mathematical formulation. The main contributions of this work are;(i) study of system with the inclusion of voltage and frequency-dependent load, (ii) modified STATCOM model clubbing reactive power control feedback and LCL filter with damping resistance, (iii) real and reactive power tracking system using dynamic compensator capabilities, and (iv) suppression of total harmonic distortions along with real and reactive power tracking using advanced tuning for filter parameters with GA, PSO, and FA based algorithms. (C) 2022 The Author(s). Published by Elsevier Ltd.

关键词： STATCOM Microgrid SCIG LCL filter Genetic algorithm Particle Swarm Optimization firefly algorithm

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