Mixed variable structural optimization using Firefly Algorithm

By Amir H. Gandomi, Xin-She Yang, Amir Hossein Alavi. 2011

817 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Civil and Structural Engineering, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/cce3c66e65c6e7143fe2c89d17c6027a38c60fae

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In Artificial Intelligence

In Civil and Structural Engineering

In Computational Theory and Mathematics

Citations in the corpus

Feature selection via a novel chaotic crow search algorithm (2017, methodology): “Some of these algorithms are gradient descent algorithm [10], discrete particle swarm optimization (DPSO) [46], tabu search [56], elephant herding optimization (EHO) [11], firefly algorithm (FA) [12], harmony search (HS) [14], charged system search (CSS) [25], bird swarm algorithm (BSA) [32], animal”
The Arithmetic Optimization Algorithm (2021, background): “…(BBO) [34] • Flower Pollination Algorithm (FPA) [35] • Grey Wolf Optimizer (GWO) [36] • Bat Algorithm (BAT) [37] • Firefly Algorithm (FA) [38] • Cuckoo Search Algorithm (CS) [39] • Moth-Flame Optimization (MFO) [40] • Gravitational Search Algorithm (GSA) [25] • Differential Evolution…”
An efficient modified grey wolf optimizer with Lévy flight for optimization tasks (2017, background): “The FA [17] inspires the behaviour of small lighting germs detected in summer nights.”
Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting (2014, background): “the other parameters mentioned above, please refer to [29, 31, 34].”
Multiobjective firefly algorithm for continuous optimization (2012, methodology): “Fireﬂy Algorithm was developed by Yang for continuous optimization [9, 10, 27], which was subsequently applied into structural optimization [26] and image processing [24].”
Chaotic Krill Herd algorithm (2014, neutral): “…swarm optimization (PSO) [27] [28] [40] [56], ant colony optimization (ACO) [4], differential evolution (DE) [10] [19] [38], firefly algorithm (FA) [7], biogeography-based optimization (BBO) [29] [37], cuckoo search (CS) [9] [49], probability-based incremental learning (PBIL) [36], big bang–big…”
Harris hawks optimization: Algorithm and applications (2019, methodology): “…proposed HHO is compared with other well-established optimization techniques such as the GA [22], BBO [22], DE [22], PSO [22], CS [34], TLBO [29], BA/BAT [52], FPA [53], FA [54], GWO [55], and MFO [56] algorithms based on the best, worst, standard deviation (STD) and average of the results (AVG).”
A comprehensive review of firefly algorithms (2013, methodology): “In order to complete the comprehensive picture of this area, the paper of Gandomi et al. in [82] was reviewed, where FA was used for solving mixed continuous/discrete structural optimization problems taken from literature regarding welded beam design, pressure vessel design, helical compression…”
Firefly algorithm with chaos (2013, background): “Preliminary studies suggest that the FA canperform superiorly, comparedwith genetic algorithm and particle swarm optimization [4], and it is applicable for mixed variable and engineering optimization [5].”
Discrete cuckoo search algorithm for the travelling salesman problem (2014, background): “Metaheuristics are usually simple to implement, but they often can solve complex problems and can thus be adapted to solve many real-world optimization problems, from the fields of operations research, engineering to artificial intelligence [10, 11, 12, 36].”
Firefly Algorithm for solving non-convex economic dispatch problems with valve loading effect (2012, background): “…in the machines themselves) or the associated auxiliaries (such as boiler, feed pumps), units can have prohibited operating regions and generators that operate in these zones may experience amplification of vibrations in their shaft bearing, which should be prevented in practical applications [6].”
Bio inspired computing - A review of algorithms and scope of applications (2016, methodology): “Firefly algorithms (Yang, 2009; Lukasik & Zak, 2009; Gandomi et al., 2011; Yang et al., 2012) were introduced to address NP hard problems with non-convex objective functions which have equality and inequality based constraints.”