Firefly algorithm with adaptive control parameters

Wang, Hui; Zhou, Xinyu; Sun, Haoyu; Wu, Lifeng; Zhao, Jia; Zhang, Hai; Cui, Laizhong

doi:10.1007/s00500-016-2104-3

Cited by 115 publications

(62 citation statements)

References 56 publications

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“…where β min denotes the minimum attraction; β max is the maximum attraction. Both of them are constants, usually β min = 0.2, β max = 1 [17], and r i,best refers to the distance between firefly i and the optimal firefly in the t-th generation.…”

Section: The Improve Firefly Algorithm: Hfa a A New Position Updmentioning

confidence: 99%

“…The calculation results of 16 benchmark functions show that the solution quality of the improved firefly algorithm is significantly improved. In 2016, Wang et al [17] proposed an improved FA based on adaptive adjustment of parameters. The improved FA implements the adaptive adjustment of step factor α and introduces the concept of minimum attraction to ensure that the attraction between fireflies is never 0.…”

Section: Introductionmentioning

confidence: 99%

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Improvement and Application of Hybrid Firefly Algorithm

et al. 2019

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Aiming at the problem of poor global search ability and slow convergence speed when solving optimization problems, this paper proposes improved hybrid firefly algorithm (HFA). HFA improves the position updating method, mutation strategy, chaotic search method and evolution strategy of the population. Specifically, the improved position update formula considers both the effect of high-brightness fireflies on position-updated fireflies, and the effects of the optimal firefly on position-updated fireflies. At the same time, a method of adaptive adjustment parameters in the position update formula is presented, which makes the position update method exhibit strong global search ability and local search ability in the initial stage and the later stage of iteration, respectively. In addition, a combined mutation operator is introduced into HFA, which effectively takes the local search and global search ability of the algorithm into account. Since chaotic search exhibits good ergodicity, an operation of randomly moving all fireflies in the population according to chaotic search is given, which enhances the ability of the algorithm to traverse the whole search space, and further improves the global search ability of the algorithm. To verify the effectiveness of HFA, 28 CEC2017 test problems are selected. The calculation results of 28 CEC2017 test problems show that compared with other algorithms, the accuracy of HFA is obviously better than that of other algorithms. Finally, HFA and other intelligent optimization methods in the literatures are used to optimize the structural parameters of cantilever beams. The optimization results show that the weight of the cantilever beam obtained by HFA is obviously smaller than other algorithms. The calculation results of CEC2017 test problems and practical problem show that the solving quality of HFA is obviously better than other algorithms.

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Section: The Improve Firefly Algorithm: Hfa a A New Position Updmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improvement and Application of Hybrid Firefly Algorithm

et al. 2019

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“…Sıfır sayısına bölünmede Eş. 1'in tanımsız durumunda olmaması [13] için mesafe yaklaşık gauss dağılımı şeklinde yazılabilir [31].…”

Section: Ateşböceği̇ Opti̇mi̇zasyon Algori̇tmasi (Firefly Optimization Alunclassified

Değiştirilmiş ateşböceği optimizasyon algoritması ile kural tabanlı çoklu sınıflama yapılması

Aydilek¹

2017

Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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“…Several variants of the FA [8][9][10][11], including hybrid approaches [12,13], and applications have been recently reported in the literature [14][15][16]. The effect of the control parameters on the performance of the FA has been studied in [17][18][19]. The main motivation for using the FA, besides being one of the most recent meta-heuristics, is related to its success when solving practical and complex problems [2,[20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%