Firefly Algorithms for Multimodal Optimization

By Xin-She Yang. Symposium on Stochastic Algorithms: Foundations and Applications, 2009

4,091 citations recorded by the source corpus.

Nature-inspired algorithms are among the most powerful algorithms for optimization. This paper intends to provide a detailed description of a new Firefly Algorithm (FA) for multimodal optimization applications. We will compare the proposed firefly algorithm with other metaheuristic algorithms such as particle swarm optimization (PSO). Simulations and results indicate that the proposed firefly algorithm is superior to existing metaheuristic algorithms. Finally we will discuss its applications and implications for further research.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/1ef9af44d1448bb9d6723889dc77f8afccfd9ddd

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Citations in the corpus

Discrete cuckoo search algorithm for the travelling salesman problem (2014, background): “Bee colonies optimisation (BCO) [30], monkey search algorithm (MS) [22], harmony search algorithm (HS) [13], firefly algorithm (FA) [32], intelligent water drops (IWD) [26], bat-inspired algorithm (BA) [33], cuckoo search (CS) [34], and krill herd (KH) [9] are among the recently proposed metaheurist”
A survey of swarm intelligence for dynamic optimization: Algorithms and applications (2017, methodology): “Less popular SI algorithms include artiﬁcial bee colony (ABC) [7], bacterial foraging optimization (BFO) [8], ﬁreﬂy algorithm (FA) [9, 10], artiﬁcial ﬁsh swarm optimization (AFSO) [11] and many others.”
Bio-inspired computation: Where we stand and what's next (2019, background): “, problems where most of its multiple optimal solutions must be found), several studies have evinced that bio-inspired computation approaches can perform extremely well in practice [11, 12, 13], as opposed to classical optimization methods.”
A Survey on Evolutionary Neural Architecture Search (2020, methodology): “The other categories of the EC methods are also important parts for realizing ENAS algorithms, such as GP, evolutionary strategy (ES), PSO, ant colony optimization (ACO) [29], differential evolution (DE) [30], and firefly algorithm (FA) [31].”
Coyote Optimization Algorithm: A New Metaheuristic for Global Optimization Problems (2018, methodology): “[10], the Grey Wolf Optimizer (GWO) [12], the Particle”
Slime mould algorithm: A new method for stochastic optimization (2020, neutral): “𝑚𝑖𝑛 The MAs used for comparison include well-regarded and recent ones: WOA [50], GWO [21], MFO [23], BA [20], SCA [7], FA[51], PSO[18], SSA [52], MVO [3], ALO [53], PBIL [54], DE [55] and advanced MAs: AGA[56], BLPSO [57], CLPSO [58], CBA [59], RCBA [60], CDLOBA [61], m_SCA [62], IWOA [63], LWOA…”
MPPT methods for solar PV systems: a critical review based on tracking nature (2019, methodology): “1 FA-based method: At first, the FA was presented in [94, 95].”
Machine learning for email spam filtering: review, approaches and open research problems (2019, methodology): “The firefly algorithm (FA) is a population based metaheuristic algorithm proposed by [68].”