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Network reconfiguration of unbalanced distribution networks using fuzzy-firefly algorithm

APPLIED SOFT COMPUTING 2016年 49卷 868-886页

作者： Kaur, Manvir Ghosh, Smarajit Thapar Univ Dept Elect & Instrumentat Engn Patiala 147004 Punjab India

This paper addresses a method to optimize the unbalanced distribution networks (UDNs) for keeping up the voltage profile with respect to the consequence of solving the multi-objective reconfiguration using the firefly algorithm in a fuzzy domain with a load flow method proposed in this paper. The objectives to be minimized are the total network power losses, the deviation of bus voltage and load equalizing in the feeders with network reconfiguration. Every goal is moved into the fuzzy domain utilizing its membership function and fuzzified independently. The proposed method for network reconfiguration has been implemented in 25-node and 19-node UDNs. The outcomes obtained by the suggested method of these two unbalanced networks have been compared with that of obtained by Genetic algorithm (GA), ABC algorithm, PSO algorithm and GA-PSO algorithm using the same objective function. The juxtaposition of the proposed method with the available methods is also presented. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Network reconfiguration Unbalanced distribution systems Fuzzy logic firefly algorithm Power loss reduction

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Improving performance of extreme learning machine for classification challenges by modified firefly algorithm and validation on medical benchmark datasets

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第31期83卷 76035-76075页

作者： Bacanin, Nebojsa Stoean, Catalin Markovic, Dusan Zivkovic, Miodrag Rashid, Tarik A. Chhabra, Amit Sarac, Marko Singidunum Univ Fac Informat & Comp Danijelova 32 Belgrade 11010 Serbia Middle East Univ MEU Res Unit Amman Jordan Romanian Inst Sci & Technol Saturn 24-26 Cluj Napoca 400504 Romania Univ Craiova Fac Sci 13 AI Cuza St Craiova 200585 Romania Univ Kurdistan Hewler Comp Sci & Engn Dept 30 Metre Ave Arbil 44017 Iraq Guru Nanak Dev Univ Dept Comp Engn & Technol Amritsar 143005 Punjab India

The extreme learning machine (ELM) stands out as a contemporary neural network learning model designed for neural networks, specifically emphasizing those with a single hidden layer. This model has gained significant importance in recent years and is frequently employed in research projects due to its reputation as one of the swiftest and most robust methods. ELM is distinguished by its ability to obtain accurate results without the need for prolonged training, setting it apart from other classifiers. Additionally, its reduced reliance on human intervention significantly diminishes the likelihood of errors. Despite their considerable potential, ELMs are not extensively employed. One contributing factor could be the ongoing challenges that ELM is yet to overcome, requiring successful resolution. A prevalent issue is the model's performance being notably dependent on the weights, biases within the hidden layer, and the quantity of neurons in that layer. Optimizing the number of neurons, referred to as hyperparameter optimization, falls under the category of NP-hard optimization problems. The second challenge lies in training the ELM, which involves establishing the weights and biases tailored for a specific task, presenting another NP-hard challenge. The research presented in this manuscript concentrates on addressing both aspects: optimizing hyperparameters, specifically the number of neurons in the hidden layer, and training the network to fine-tune the weights and biases. The main goal of this research is to effectively resolve both optimization and training by utilizing an improved swarm intelligence algorithm. As a result, both issues were addressed using an adapted version of the firefly algorithm. The proposed approach was tested and validated on twelve authentic datasets and four synthetic datasets designed for classification purposes. One of the forefront tasks among them involves the fetal nonstress test, commonly known as the cardiotocography problem, requi

关键词： Extreme learning machine Metaheuristics optimization Swarm intelligence firefly algorithm Classification

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Optimum design of tower structures using firefly algorithm

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STRUCTURAL DESIGN OF TALL AND SPECIAL BUILDINGS 2014年第5期23卷 350-361页

作者： Talatahari, Siamak Gandomi, Amir Hossein Yun, Gun Jin Univ Tabriz Marand Fac Engn Tabriz Iran Univ Akron Dept Civil Engn Akron OH 44325 USA

The firefly algorithm (FA) as a recent new meta-heuristic optimization algorithm is developed for determining optimum design of tower shaped structures. The FA mimics the social behavior of fireflies, which communicate, search for pray and find mates using bioluminescence with varied flashing patterns. In this paper, an adaptive FA is presented that utilizes the feasible-based method to handle constraints. This method is effective in improving the convergence and also suitable for expensive optimization tasks such as large-scale structures. Three tower structures are selected to evaluate the performance of the algorithm. The results are better than the other results proposed in the literature and confirm the validity of the proposed algorithm. Copyright (c) 2012 John Wiley & Sons, Ltd.

关键词： meta-heuristic methods firefly algorithm truss structures design optimization adaptive firefly algorithm constrained optimization structural design

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Optimal feature selection using distance-based discrete firefly algorithm with mutual information criterion

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NEURAL COMPUTING & APPLICATIONS 2017年第9期28卷 2795-2808页

作者： Zhang, Long Shan, Linlin Wang, Jianhua Harbin Normal Univ Coll Comp Sci & Informat Engn Harbin 150025 Heilongjiang Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Heilongjiang Peoples R China Heilongjiang Univ Sch Art Harbin 150080 Heilongjiang Peoples R China

In this paper, we investigate feature subset selection problem by a new self-adaptive firefly algorithm (FA), which is denoted as DbFAFS. In classical FA, it uses constant control parameters to solve different problems, which results in the premature of FA and the fireflies to be trapped in local regions without potential ability to explore new search space. To conquer the drawbacks of FA, we introduce two novel parameter selection strategies involving the dynamical regulation of the light absorption coefficient and the randomization control parameter. Additionally, as an important issue of feature subset selection problem, the objective function has a great effect on the selection of features. In this paper, we propose a criterion based on mutual information, and the criterion can not only measure the correlation between two features selected by a firefly but also determine the emendation of features among the achieved feature subset. The proposed approach is compared with differential evolution, genetic algorithm, and two versions of particle swarm optimization algorithm on several benchmark datasets. The results demonstrate that the proposed DbFAFS is efficient and competitive in both classification accuracy and computational performance.

关键词： Feature selection firefly algorithm Mutual information Adaptive parameter

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Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting

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KNOWLEDGE-BASED SYSTEMS 2014年第Jan.期55卷 87-100页

作者： Xiong, Tao Bao, Yukun Hu, Zhongyi Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China

Highly accurate interval forecasting of a stock price index is fundamental to successfully making a profit when making investment decisions, by providing a range of values rather than a point estimate. In this study, we investigate the possibility of forecasting an interval-valued stock price index series over short and long horizons using multi-output support vector regression (MSVR). Furthermore, this study proposes a firefly algorithm (FA)-based approach, built on the established MSVR, for determining the parameters of MSVR (abbreviated as FA-MSVR). Three globally traded broad market indices are used to compare the performance of the proposed FA-MSVR method with selected counterparts. The quantitative and comprehensive assessments are performed on the basis of statistical criteria, economic criteria, and computational cost. In terms of statistical criteria, we compare the out-of-sample forecasting using goodness-of-forecast measures and testing approaches. In terms of economic criteria, we assess the relative forecast performance with a simple trading strategy. The results obtained in this study indicate that the proposed FA-MSVR method is a promising alternative for forecasting interval-valued financial time series. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Stock price forecasting Interval-valued data Multiple-output support vector regression firefly algorithm Trading strategy

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Support vector regression with chaos-based firefly algorithm for stock market price forecasting

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APPLIED SOFT COMPUTING 2013年第2期13卷 947-958页

作者： Kazem, Ahmad Sharifi, Ebrahim Hussain, Farookh Khadeer Saberi, Morteza Hussain, Omar Khadeer Univ Technol Sydney Fac Engn & Informat Technol Sch Software Decis Support & E Serv Intelligence Lab Ultimo NSW Australia Univ Tafresh Dept Ind Engn Tafresh Iran Islamic Azad Univ Tafresh Branch Young Researchers Club Tafresh Iran Curtin Univ Technol Sch Informat Syst Perth WA Australia

Due to the inherent non-linearity and non-stationary characteristics of financial stock market price time series, conventional modeling techniques such as the Box-Jenkins autoregressive integrated moving average (ARIMA) are not adequate for stock market price forecasting. In this paper, a forecasting model based on chaotic mapping, firefly algorithm, and support vector regression (SVR) is proposed to predict stock market price. The forecasting model has three stages. In the first stage, a delay coordinate embedding method is used to reconstruct unseen phase space dynamics. In the second stage, a chaotic firefly algorithm is employed to optimize SVR hyperparameters. Finally in the third stage, the optimized SVR is used to forecast stock market price. The significance of the proposed algorithm is 3-fold. First, it integrates both chaos theory and the firefly algorithm to optimize SVR hyperparameters, whereas previous studies employ a genetic algorithm (GA) to optimize these parameters. Second, it uses a delay coordinate embedding method to reconstruct phase space dynamics. Third, it has high prediction accuracy due to its implementation of structural risk minimization (SRM). To show the applicability and superiority of the proposed algorithm, we selected the three most challenging stock market time series data from NASDAQ historical quotes, namely Intel, National Bank shares and Microsoft daily closed (last) stock price, and applied the proposed algorithm to these data. Compared with genetic algorithm-based SVR (SVR-GA), chaotic genetic algorithm-based SVR (SVR-CGA), firefly-based SVR (SVR-FA), artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFIS), the proposed model performs best based on two error measures, namely mean squared error (MSE) and mean absolute percent error (MAPE). Crown Copyright (C) 2012 Published by Elsevier B.V. All rights reserved.

关键词： Support vector regression firefly algorithm Chaotic mapping Stock market price forecasting

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Multimodal size, shape, and topology optimisation of truss structures using the firefly algorithm

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ADVANCES IN ENGINEERING SOFTWARE 2013年 56卷 23-37页

作者： Fadel Miguel, Leandro Fleck Lopez, Rafael Holdorf Fadel Miguel, Leticia Fleck Univ Fed Santa Catarina Dept Civil Engn BR-88040970 Florianopolis SC Brazil Univ Fed Rio Grande do Sul Dept Mech Engn BR-90050170 Porto Alegre RS Brazil

This paper presents an efficient single-stage firefly-based algorithm (FA) to simultaneously optimise the size, shape and topology of truss structures. The optimisation problem uses the minimisation of structural weight as its objective function and imposes displacement, stress and kinematic stability constraints. Unstable and singular topologies are disregarded as possible solutions by checking the positive definiteness of the stiffness matrix. Because cross-sectional areas are usually defined by discrete values in practice due to manufacturing limitations, the optimisation algorithm must assess a mixed-variable optimisation problem that includes both discrete and continuous variables at the same time. The effectiveness of the FA at solving this type of optimisation problem is demonstrated with benchmark problems, the results for which are better than those reported in the literature and obtained with lower computational costs, emphasising the capabilities of the proposed methodology. In addition, the procedure is capable of providing multiple optima and near-optimal solutions in each run, providing a set of possible designs at the end of the optimisation process. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： Truss size Shape and topology optimisation firefly algorithm Multimodal optimisation Mixed continuous-discrete variable problems Structural optimisation Single-stage procedure

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FA-CRAN: A firefly algorithm for Dynamic BBU-RRH Mapping in Cloud/Centralized Radio Access Networks

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IEEE ACCESS 2024年 12卷 22821-22831页

作者： Imbiriba, Mariane de Paula da Silva Goncalves da Paixao, Erminio Augusto Ramos dos Santos, Albert Einstein Coutinho de Mattos Teixeira, Carlos Andre Vieira, Rafael Fogarolli Souza, Daniel da Silva Falcao, Igor Wenner Silva Cardoso, Diego Lisboa Fed Univ Postgrad Program Elect Engn BR-66075110 Belem PA Brazil Fed Univ Para Fac Comp & Technol BR-66075110 Belem PA Brazil

C-RAN (Cloud Radio Access Network) is an architecture designed to the new generation of mobile networks. It has handled many problems arising from the 4-generation network, and made several improvements, such as centralized processing and energy efficiency, among others. However, time-varying traffic, known as the tidal effect, impairs the network by making resource allocation less efficient, and this affects network performance in terms of problems related to blocked users and power consumption. This study seeks to evaluate an optimized mapping model between RRH (Remote Radio Head) and BBU (Base Band Unit) by providing a fairer and more efficient load balancing. In addition, this solution is compared with some key algorithms used in the literature for addressing optimization problems. The results demonstrated that, owing to its effective search feature, the firefly algorithm, was the most promising system, since it obtained better performance measures than the others.

关键词： C-RAN balancing firefly algorithm

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Optimization of flyrock and rock fragmentation in the Tajareh limestone mine using metaheuristics method of firefly algorithm

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ENGINEERING WITH COMPUTERS 2018年第2期34卷 241-251页

作者： Asl, Parvin Faraji Monjezi, Masoud Hamidi, Jafar Khademi Armaghani, Danial Jahed Tarbiat Modares Univ Fac Engn Tehran 14115143 Iran Amirkabir Univ Technol Dept Civil & Environm Engn Tehran 15914 Iran

A successful and favorable explosion not only causes a proper rock fragmentation, but also decreases the unfavorable and unwanted environmental issues caused by explosion such as ground vibration, flyrock, air-overpressure and back-break. Therefore, anticipation and optimization of these issues produced by blasting operations is significant. In this study, an attempt has been made to design the effective factors on Tajareh limestone mine explosion [i.e., burden, blast-hole, spacing, hole length, sub-drilling, stemming, powder factor, charge in each delay and Geological Strength Index (GSI)] in order to reduce improper fragmentation and flyrock. Since the experimental methods are not suitable in terms of accuracy, using artificial neural network (ANN) and firefly algorithms, flyrock and rock fragmentation were predicted and optimized, respectively. After collecting data and selecting the most effective parameters on flyrock and rock fragmentation, an ANN model was developed, and then its results were called by firefly algorithm for optimizing process. ANN results according to coefficient of determination (R (2)) and root mean square error (RMSE) were obtained as 0.94 and 0.1, 0.93 and 0.09, respectively, for fragmentation and flyrock. The outcome results of modeling and optimization showed a decrease of 42.9 and 32.9% in results of flyrock and rock fragmentation, respectively. In addition, results of optimization process were obtained as: 2 m of burden, 2.9 m of spacing, 7.5 m of hole length, 0.7 m of sub-drilling, 1.9 m of obstruction length, 0.69 kg/m(3) of powder factor, 1443 kg of charge in each delay and 55.5 of GSI. Based on the obtained results of sensitivity analysis, it was found that GSI and burden receive the highest influence values on both flyrock and rock fragmentation.

关键词： Rock fragmentation Flyrock Optimization process Artificial neural network firefly algorithm

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An improved chaotic firefly algorithm for global numerical optimization

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2019年第1期12卷 131-148页

作者： Brajevic, Ivona Stanimirovic, Predrag Univ Nis Dept Math & Comp Sci Fac Sci & Math Visegradska 33 Nish 18000 Serbia Business Acad Univ Fac Appl Management Econ & Finance Jevrejska 24 Belgrade 11000 Serbia

firefly algorithm (FA) is a prominent metaheuristc technique. It has been widely studied and hence there are a lot of modified FA variants proposed to solve hard optimization problems from various areas. In this paper an improved chaotic firefly algorithm (ICFA) is proposed for solving global optimization problems. The ICFA uses firefly algorithm with chaos (CFA) as the parent algorithm since it replaces the attractiveness coefficient by the outputs of the chaotic map. The enhancement of the proposed approach involves introducing a novel search strategy which is able to obtain a good ratio between exploration and exploitation abilities of the algorithm. The impact of the introduced search operator on the performance of the ICFA is evaluated. Experiments are conducted on nineteen well-known benchmark functions. Results reveal that the ICFA is able to significantly improve the performance of the standard FA, CFA and four other recently proposed FA variants.

关键词： firefly algorithm chaos global optimization nature-inspired algorithms exploitation exploration

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