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A Multi-objective Optimum Distributed Generation Placement Using firefly algorithm

JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2022年第2期17卷 945-953页

作者： Anbuchandran, S. Rengaraj, R. Bhuvanesh, A. Karuppasamypandiyan, M. JCT Coll Engn & Technol Dept Elect & Elect Engn Coimbatore Tamil Nadu India SSN Coll Engn Dept Elect & Elect Engn Chennai Tamil Nadu India PSN Coll Engn & Technol Dept Elect & Elect Engn Tirunelveli Tamil Nadu India Kalasalingam Acad Res & Educ Dept Elect & Elect Engn Krishnankoil India

Nowadays, the power systems face several environmental and economic challenges and Distributed Generations (DGs) will be an effectual solution for them. The integration of DGs may result in power system volatility and losses. The optimal allocation of DGs will resolve the aforesaid issues. This study aims to implement multi-objective firefly algorithm for siting and sizing of DGs by optimizing six dissimilar objective functions such as minimization of power losses, improvement of voltage profile, enhancement of Voltage Stability Index, reduction of pollutant emission and elimination of average voltage Total Harmonic Distortion. Besides, fuzzy decision-making methodology has been deployed to choose one of the Pareto-optimal solutions as the Best Compromise Solution. The studies have been conducted on standard IEEE 33-bus system and a practical 62 bus Indian Utility System namely Tamil Nadu Generation and Distribution Corporation Limited as a real-world distribution network. The outcomes of the proposed work have been compared with related past studies and prominent improvement has been experienced.

关键词： Distributed generations Optimal placement firefly algorithm Power losses minimization Voltage profile improvement Pollutant minimization Total harmonic distortion minimization IEEE33-bus system TANGEDCO

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第6期41卷 7047-7061页

作者： Hamad, Aws Hamed Mahmood, Ali Abdulkareem Abed, Saad Adnan Ying, Xu Minist Higher Educ & Sci Res Baghdad Iraq Univ Informat Technol & Commun Baghdad Iraq Univ Teknol PETRONAS High Performance Cloud Comp Ctr Seri Iskandar 32610 Perak Malaysia Hunan Univ Coll Comp Sci & Elect Engn Changsha Hunan Peoples R China

Word sense disambiguation (WSD) refers to determining the right meaning of a vague word using its context. The WSD intermediately consolidates the performance of final tasks to achieve high accuracy. Mainly, a WSD solution improves the accuracy of text summarisation, information retrieval, and machine translation. This study addresses the WSD by assigning a set of senses to a given text, where the maximum semantic relatedness is obtained. This is achieved by proposing a swarm intelligence method, called firefly algorithm (FA) to find the best possible set of senses. Because of the FA is based on a population of solutions, it explores the problem space more than exploiting it. Hence, we hybridise the FA with a one-point search algorithm to improve its exploitation capacity. Practically, this hybridisation aims to maximise the semantic relatedness of an eligible set of senses. In this study, the semantic relatedness is measured by proposing a glosses-overlapping method enriched by the notion of information content. To evaluate the proposed method, we have conducted intensive experiments with comparisons to the related works based on benchmark datasets. The obtained results showed that our method is comparable if not superior to the related works. Thus, the proposed method can be considered as an efficient solver for the WSD task.

关键词： firefly algorithm local search meta-heuristic semantic relatedness word sense disambiguation

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A Switch-Mode firefly algorithm for Global Optimization

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IEEE ACCESS 2018年 6卷 54177-54184页

作者： Huang, Jian Chen, Xiaochao Wu, Dongrui Huazhong Univ Sci & Technol Sch Automat Key Lab Minist Educ Image Proc & Intelligent Cont Wuhan 430074 Hubei Peoples R China

The firefly algorithm has been successfully used in many optimization problems. However, the standard firefly algorithm uses a fixed randomization parameter in the optimization, which emphasizes more on exploration than exploitation, and hence impacts its convergence. This paper proposes a switch-mode firefly algorithm, which first focuses on exploration and then switches to exploitation. A fixed randomization parameter is used in exploration, and a gradually decreasing random randomization parameter is used in exploitation. The condition for the switching from exploration to exploitation is identified automatically. Extensive experiments on 15 benchmark functions were performed to verify the effectiveness of the proposed approach.

关键词： firefly algorithm global optimization switch-mode

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An integrated approach to region based image retrieval using firefly algorithm and support vector machine

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NEUROCOMPUTING 2015年第Part3期151卷 1099-1111页

作者： Kanimozhi, T. Latha, K. Anna Univ Dept Comp Sci & Engn Tiruchirappalli 620024 Tamil Nadu India

Intelligent image retrieval is a challenging technology in multimedia applications where bridging the gap between user's expectation and low level features is typically hard for computing systems. In the proposed approach, a unique method is projected which integrates support vector machine based learning with an evolutionary stochastic algorithm, called firefly algorithm as a relevance feedback approach into a region based image retrieval system. This system overcomes the semantic gap through optimized iterative learning and also provides a better exploration of solution space. Support vector machine learning automatically updates the weights of preferences for relevant images based on the both relevant and irrelevant feedback images. The firefly optimizer guides the swarm agents to move towards the cluster of relevant images in the exploration of the search space based on user's feedback. This research study has a focused approach to increase the performance by optimizing region feature with the firefly algorithm. The efficiency of the proposed approach is experimented on the standard subset of Corel, Caltech and Pascal database images. The performance of the proposed approach is compared with other existing retrieval methods like particle swarm optimization, genetic algorithm, support vector machine and query point movement to identify the excellence with regard to the model in terms of precision and recall. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Region-based image retrieval Support vector machine firefly algorithm Gaussian distribution Particle swarm optimization Genetic algorithm

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Discrete Preference-Based Stepping Ahead firefly algorithm for Solving Multidimensional Knapsack Problems

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IEEE ACCESS 2024年 12卷 139154-139164页

作者： Nand, Ravneil Chaudhary, Kaylash Sharma, Bibhya Univ South Pacific Sch Informat Technol Engn Math & Phys Suva Fiji

Complex optimization problems, especially those encountered in real-life scenarios, pose significant challenges due to their multifaceted nature and the involvement of numerous variables. In such contexts, the application of intelligent optimization algorithms emerges as a valuable tool for effectively tackling these intricate problems. firefly algorithm (FA) is a popular meta-heuristic algorithm for continuous domain and lacks application in discrete domain. While there are a few applications but hardly on combinatorial optimization problems. Combinatorial optimization problem, which consist of selecting an optimal object from a finite number of objects is a challenging domain. In this study, a novel discrete version of stepping ahead FA together with its hybridization with another algorithm are proposed to solve the Multidimensional Knapsack Problem (MKP). The proposed algorithms are called discrete stepping ahead firefly algorithm (FA-Step) and hybridization of discrete stepping ahead firefly algorithm with Covariance Matrix Adaptation Evolution Strategy (FA-CMAES). The proposed algorithms make full use of the problem-solving expertise while also incorporating diversity to improve exploitation with stepping ahead mechanism and preference operator. The proposed algorithms are tested on 38 well-known knapsack instances and compared with some novel works from the literature. The proposed methods allow researchers to utilize discretization techniques in other state-of-the-art techniques to solve discrete domain problems with ease.

关键词： Optimization Search problems Covariance matrices Standards Mathematical models Transforms Time complexity firefly algorithm covariance matrix adaptation evolution strategy multidimensional knapsack problem optimization stepping ahead mechanism

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Hybrid firefly algorithm-neural network for battery remaining useful life estimation

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Clean Energy 2024年第5期8卷 157-166页

作者： Zuriani Mustaffa Mohd Herwan Sulaiman Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah(UMPSA)26600 Pekan PahangMalaysia Faculty of Electrical&Electronics Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah(UMPSA)26600 Pekan PahangMalaysia

Accurately estimating the remaining useful life(RUL)of batteries is crucial for optimizing maintenance,preventing failures,and enhancing reliability,thereby saving costs and *** study introduces a hybrid approach for estimating the RUL of a battery based on the firefly algorithm–neural network(FA–NN)model,in which the FA is employed as an optimizer to fine-tune the network weights and hidden layer biases in the *** performance of the FA–NN is comprehensively compared against two hybrid models,namely the harmony search algorithm(HSA)–NN and cultural algorithm(CA)–NN,as well as a single model,namely the autoregressive integrated moving average(ARIMA).The comparative analysis is based mean absolute error(MAE)and root mean squared error(RMSE).Findings reveal that the FA–NN outperforms the HSA–NN,CA–NN,and ARIMA in both employed metrics,demonstrating su-perior predictive capabilities for estimating the RUL of a ***,the FA–NN achieved a MAE of 2.5371 and a RMSE of 2.9488 compared with the HSA–NN with a MAE of 22.0583 and RMSE of 34.5154,the CA–NN with a MAE of 9.1189 and RMSE of 22.4646,and the ARIMA with a MAE of 494.6275 and RMSE of ***,the FA–NN exhibits significantly smaller maximum errors at 34.3737 compared with the HSA–NN at 490.3125,the CA–NN at 827.0163,and the ARIMA at 1.16e+03,further emphasizing its robust performance in minimizing prediction *** study offers important insights into battery health management,showing that the proposed method is a promising solution for precise RUL predictions.

关键词： battery remaining useful life firefly algorithm neural networks optimization

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APPLICATION OF A MODIFIED CES PRODUCTION FUNCTION MODEL BASED ON IMPROVED firefly algorithm

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2020年第4期16卷 1571-1584页

作者： Cheng, Maolin Xiang, Mingyin Suzhou Univ Sci & Technol Sch Math & Phys Suzhou 215009 Peoples R China Huangshan Univ Sch Math & Stat Huangshan 245041 Peoples R China

The conventional CES production function model fails to consider the influences of policy factors on economic growth in different stages. This paper proposes a modified model of the CES production function. Regarding model parameter estimation, the paper proposes a modern intelligent algorithm, the firefly algorithm (FA). The paper improves conventional FA to enhance the convergence rate and precision. To overcome the shortcomings of the conventional method in model application, the paper presents a new method of calculating the contribution rates of factors influencing economic growth and provides examples.

关键词： CES production function economic growth contribution rate firefly algorithm

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firefly algorithm for Power Economic Emission Dispatch 3rd

Firefly Algorithm for Power Economic Emission Dispatch

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3rd International Conference on Intelligent Technologies and Engineering Systems (ICITES)

作者： Chiang, Chao-Lung Nan Kai Univ Technol Dept Elect Engn Caotun 542 Nantou Taiwan

ISBN: (纸本)9783319173146;9783319173139

This work proposes a firefly algorithm for the optimal economic emission dispatch (EED) of the hydrothermal power system (HPS), considering non-smooth fuel cost and emission level functions. The firefly algorithm (FA) can efficiently search and actively explore solutions. The multiplier updating (MU) is introduced to handle the equality and inequality constraints of the HPS, and the e-constraint technique is employed to manage the multi-objective problem. To show the advantages of the proposed algorithm, one example addressing the best compromise is applied to test the EED problem of the HPS. The proposed approach integrates the FA, the MU, and the e-constraint technique, revealing that the proposed approach has the following merits-ease of implementation;applicability to non-smooth fuel cost and emission level functions;better effectiveness than the previous method, and the requirement for only a small population in applying the optimal EED problem of the HPS.

关键词： firefly algorithm Multiplier updating Economic emission dispatch

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firefly algorithm for Supply Chain Optimization

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LOBACHEVSKII JOURNAL OF MATHEMATICS 2018年第3期39卷 355-367页

作者： Elkhechafi, Mariam Benmamoun, Zoubida Hachimi, Hanaa Amine, Aouatif Elkettani, Youssfi Ibn Tofail Univ Dept Math BP 241 Kenitra Morocco Ibn Tofail Univ Natl Sch Appl Ssci BP 242 Kenitra Morocco

The firefly algorithm is one of the best latest bio-inspired algorithms, which proved its performance in solving continuous and discrete optimization problems. This paper presents a more detailed comparison study using a set of test functions. The main goal is the application of firefly algorithm (FA) to solve Lot size optimization in supply chain management which is the most complex part of stock management process. A complexity that comes from the conflict between the minimization of the costs and the maximization of the level of service. For these reason, the traditional methods of lot size control have to deal with the explosion of new needs related to supply chain evolution. The optimal solutions obtained by FA are far better than the best solutions obtained by deterministic methods analyzed in the literature.

关键词： Optimization Metaheuristics Lot Size Supply Chain firefly algorithm

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A Combined Method to Estimate Wind Speed Distribution Based on Integrating the Support Vector Machine with firefly algorithm

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ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY 2016年第3期35卷 867-875页

作者： Gani, Abdullah Mohammadi, Kasra Shamshirband, Shahaboddin Altameem, Torki A. Petkovic, Dalibor Ch, Sudheer Univ Malaya Fac Comp Sci & Informat Technol Dept Comp Syst & Technol Kuala Lumpur 50603 Malaysia Univ Massachusetts Dept Mech & Ind Engn Amherst MA 01003 USA King Saud Univ Dept Comp Sci POB 28095-11437 Riyadh Saudi Arabia Univ Nis Dept Mechatron & Control Fac Mech Engn Nish 18000 Serbia ITM Univ Dept Civil & Environm Engn Gurgaon 122017 Haryana India

A new hybrid approach by integrating the support vector machine (SVM) with firefly algorithm (FFA) is proposed to estimate shape (k) and scale (c) parameters of the Weibull distribution function according to previously established analytical methods. The extracted data of two widely successful methods utilized to compute parameters k and c were used as learning and testing information for the SVM-FFA method. The simulations were performed on both daily and monthly scales to draw further conclusions. The performance of SVM-FFA method was compared against other existing techniques to demonstrate its efficiency and viability. The results conclusively indicate that SVM-FFA method provides further precision in the predictions. Nevertheless, for daily estimations, the applicability of proposed method could not be feasible owing to high day-by-day fluctuations of parameters k, whereas the results of monthly estimation are completely appealing and precise. In summary, the SVM-FFA is a highly viable and efficient technique to estimate wind speed distribution on monthly scale. It is expected that the proposed method would be profitable for wind researchers and experts to be used in many practical applications, such as evaluating the wind energy potential and making a proper decision to nominate the optimal wind turbines. (C) 2015 American Institute of Chemical Engineers Environ Prog, 35: 867-875, 2016

关键词： wind speed distribution support vector machine firefly algorithm Weibull function hybrid approach

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