Firefly algorithm: recent advances and applications

Yang, Xin‐She; He, X. Q.

doi:10.1504/ijsi.2013.055801

Cited by 796 publications

(390 citation statements)

References 36 publications

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“…where L is the average scale of the problem (Xin-She 2014;Yang and He 2013). According to the scale of our problem variables, we varied c from 0.1 to 1.0 with a step increase of 0.1.…”

Section: The Value Of the Hcdfa's Parametersmentioning

confidence: 99%

A firefly algorithm for solving competitive location-design problem: a case study

et al. 2016

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This paper aims at determining the optimal number of new facilities besides specifying both the optimal location and design level of them under the budget constraint in a competitive environment by a novel hybrid continuous and discrete firefly algorithm. A real-world application of locating new chain stores in the city of Tehran, Iran, is used and the results are analyzed. In addition, several examples have been solved to evaluate the efficiency of the proposed model and algorithm. The results demonstrate that the performed method provides good-quality results for the test problems.

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“…where L is the average scale of the problem (Xin-She 2014;Yang and He 2013). According to the scale of our problem variables, we varied c from 0.1 to 1.0 with a step increase of 0.1.…”

Section: The Value Of the Hcdfa's Parametersmentioning

confidence: 99%

A firefly algorithm for solving competitive location-design problem: a case study

et al. 2016

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“…The attractiveness β is relative. Its change depends on the distance between two fireflies (Yang, 2010b;2010a;Yang and He, 2013).…”

Section: Related Workmentioning

confidence: 99%

“…The attractiveness, β, is related with light intensity relatively, meaning that when two fireflies attracted each other, the highest intensity will attract the lower intensity and the value of β changes based on the distance between the two fireflies. The attractiveness, β, formula is shown in Equation 1 (Yang, 2010a;Yang and He, 2013) …”

Section: Standard Firefly Algorithmmentioning

confidence: 99%

“…Y is the absorption coefficient value between (0-1). The movement of one firefly i to another firefly j is determined based on Equation 2 (Yang, 2010b;Yang and He, 2013):…”

Section: Standard Firefly Algorithmmentioning

confidence: 99%

“…In this study, the Firefly Algorithm (FA) (Yang, 2010a;2010b;Yang and He, 2013), which was introduced by Xin-She Yang 2008, is utilized for text clustering. Two contributions made include the Weight-based Firefly Algorithm (WFA) (Mohammed et al, 2014) and Weight-based Firefly Algorithm II (WFA II ).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Document Clustering Based on Firefly Algorithm

Mohammed¹,

Yusof²,

Husni³

2015

Journal of Computer Science

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Document clustering is widely used in Information Retrieval however, existing clustering techniques suffer from local optima problem in determining the k number of clusters. Various efforts have been put to address such drawback and this includes the utilization of swarm-based algorithms such as particle swarm optimization and Ant Colony Optimization. This study explores the adaptation of another swarm algorithm which is the Firefly Algorithm (FA) in text clustering. We present two variants of FA; Weight-based Firefly Algorithm (WFA) and Weight-based Firefly Algorithm II (WFA II ). The difference between the two algorithms is that the WFA II, includes a more restricted condition in determining members of a cluster. The proposed FA methods are later evaluated using the 20Newsgroups dataset. Experimental results on the quality of clustering between the two FA variants are presented and are later compared against the one produced by particle swarm optimization, K-means and the hybrid of FA and -K-means. The obtained results demonstrated that the WFA II outperformed the WFA, PSO, K-means and FA-Kmeans. This result indicates that a better clustering can be obtained once the exploitation of a search solution is improved.

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Techno‐economic management of daily Volt/Var planning considering reactive power contribution of high penetrated‐PV solar farms utilized as STATCOM : A case study in Istanbul, Turkey

Halacli

Demirören

2021

Int Trans Electr Energ Syst

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Summary The rising demand for electrical energy and decreasing capital cost of photovoltaic (PV) panels led to rapid growth in large‐scale solar PV system penetration in many countries, including Turkey. This article focused on a cost‐effective and efficient decentralized voltage and reactive power (Volt/Var) control approach in distribution networks at the presence of high penetrated solar PV farms utilized as static synchronous compensator (PV‐STATCOM) which has been recently proposed. A proper coordinated control of large number of PV‐STATCOMs suggested to achieve optimal reactive power scheduling for the next day ahead. This article introduced implementation of a modified Lévy flight‐based firefly algorithm (MLFA) to multi‐objective Volt/Var optimization problem, which was employed for minimizing active power loss and cost of energy. The validity and applicability of the proposed method were confirmed by implementing it on a real megavolt (MV) Turkish distribution network for various loading conditions. Simulation results show that the results of MLFA to this problem outperform those of other methods. Furthermore, application of the proposed method offers opportunity to distribution system operators for a large amount of cost savings and loss reduction without compromising voltage regulatory limits.

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Firefly algorithm: recent advances and applications

Cited by 796 publications

References 36 publications

A firefly algorithm for solving competitive location-design problem: a case study

A firefly algorithm for solving competitive location-design problem: a case study

Document Clustering Based on Firefly Algorithm

Techno‐economic management of daily Volt/Var planning considering reactive power contribution of high penetrated‐PV solar farms utilized as STATCOM : A case study in Istanbul, Turkey

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