A new optimization method: Big Bang-Big Crunch

By O. K. Erol, I. Eksin. Advances in Engineering Software, 2006

1,587 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/c776796207fcbb3b40e06bc2b3000fa4cb74f30e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computational Theory and Mathematics

Citations in the corpus

Metaheuristic research: a comprehensive survey (2018, methodology): “2010) SFLA Shuffled Frog Leaping Algorithm (Eusuff et al. 2006) SFS Stochastic Fractal Search (Salimi 2015) SGA Search Group Algorithm (Gonçalves et al.”
EXPO2013: a kit of tools for phasing crystal structures from powder data (2013, background): “On 69 tested compounds (for details see Altomare, Corriero et al., 2013), HBB–BC saves over 40% of the execution time with respect to SA.”
A New Technique For Reduced-Order Modelling of Linear Time-Invariant System (2017, methodology): “Desai and Prasad [2] also proposed a new method for linear time invariant systems which is based on Routh approximation (RA) [3] and Big Bang-Big Crunch (BB-BC) algorithm [4] to obtain the reduced-order model.”
A survey on new generation metaheuristic algorithms (2019, methodology): “A new optimization technique, Water Cycle Algorithm (WCA), is proposed by Eskandar et al. (2012). The concepts of the algorithm are based on the water cycle process and how rivers flow to the sea in real life. Kashan (2015) proposes optics inspired optimization (OIO) algorithm. OIO assumes the surfa”
A new approach to order reduction using stability equation and big bang big crunch optimization (2013, methodology): “In the next cycle of the big bang phase, new solutions are created by using the previous knowledge (center of mass), the fitness function fk (Erol & Eksin, 2006) is fk = M−1∑ i=0 [y(i t) − yr(i t)]2, (2) where M = T t , where y(i t) and yr(i t) are the unit step responses of the higher-order and…”
Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications (2017, methodology): “The name of few algorithms are Simulated Annealing (SA) [33] , ravitational Search Algorithm (GSA) [52] , Big-Bang Big-Crunch BBBC) [14] , Charged System Search (CSS) [31] , Black Hole (BH) 23] algorithm, Central Force Optimization (CFO) [16] , Small-World ptimization Algorithm (SWOA) [13] , Artific”
Sand Cat swarm optimization: a nature-inspired algorithm to solve global optimization problems (2022, methodology): “There are famous optimization algorithms in this category such as the gravitational local search (GLSA) [17], the gravitational search algorithm (GSA) [18], and the Big Bang–Big crunch (BBBC) [19].”
Fire Hawk Optimizer: a novel metaheuristic algorithm (2022, positive): “Table 25 Optimum design sections of the 15-story benchmark structure HPSACO Hybrid algorithm based on particle swarm, ant colony and harmony search algorithms, HBB– BC Hybrid big bang-big crunch and particle swarm optimization algorithms, ICA Imperialist Competitive Algorithm, DE Differential Evolut”