BGSA: binary gravitational search algorithm

By Esmat Rashedi, Hossein Nezamabadi-pour, Saeid Saryazdi. Natural Computing, 2010

786 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering

View Paper: https://www.semanticscholar.org/paper/528f71776057d448205f1e5e6db8b24f46cafef4

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

1. Seyedali Mirjalili
2. Ali Asghar Heidari
3. Tansel Dökeroğlu
4. Hossam Faris
5. Ibrahim Aljarah
6. Sankalap Arora
7. Andrew Lewis
8. Majdi M. Mafarja

In Biomedical Engineering

1. Seyedali Mirjalili
2. Andrew Lewis
3. Sankalap Arora
4. Xin-She Yang
5. Priyanka Anand
6. Ali Asghar Heidari
7. Aboul Ella Otifey Hassanien
8. Hossam M. Zawbaa

Citations in the corpus

• Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm (2012, background): “In GSA, by choosing proper values for the random parameters (G0 and a), the exploration can be guaranteed and slow movement of heavier agents can guarantee the exploitation ability [25,28].”
• Binary ant lion approaches for feature selection (2016, methodology): “Furthermore, A binary version of the gravitational search algorithm (BGSA) is used for feature selection problem [20].”
• S-shaped versus V-shaped transfer functions for binary Particle Swarm Optimization (2013, methodology): “One of these transfer functions by Rashedi et al. [15] is presented in Fig.”
• Chaotic grasshopper optimization algorithm for global optimization (2019, background): “Until now, substantial research has been done by various researchers on these algorithms, and several nature-inspired meta-heuristic algorithms have been introduced such as Particle Swarm Optimization (PSO) [13], Genetic algorithm (GA) [14], Differential Evolution (DE) [15], Firefly Algorithm (FA) [”
• Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems (2015, methodology): “For verification of the re-sults, the binary PSO (BPSO) [58] and binary gravitational search algorithm (BGSA) [59] are chosen from the literature.”
• An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems (2018, methodology): “[70] to covert the original GSA to a binary version.”
• Optimal power flow using gravitational search algorithm (2012, methodology): “Furthermore, GSA is memory-less, it works efficiently like the algorithms with memory [31,32].”
• Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining (2014, background): “The Binary Gravitational Search Algorithm (BGSA) is a new variant for discrete optimization problems, and experimental results confirm its efficiency in solving various nonlinear benchmark problems [98].”
• BBA: A Binary Bat Algorithm for Feature Selection (2012, methodology): “Further, Rashedi et al. [5] proposed a binary version of the Gravitational Search Algorithm (GSA) [6] called BGSA for feature selection, and Ramos et al. [7] presented their version of the Harmony Search (HS) [8] for the same purpose in the context of theft detection in power distribution systems.”
• Binary bat algorithm (2013, methodology): “Most of the wellknown meta-heuristic algorithms such as harmony search (HS) [15], differential evolution algorithm (DE) [16], magnetic optimization algorithm (MOA) [17], and gravitational search algorithm (GSA) [5] have binary versions which make them capable of optimizing binary problems.”
• A review of population-based meta-heuristic algorithm (2013, methodology): “Based on the mentioned rules, the binary versions of PSO and GSA have been proposed by Kennedy and Eberhart [79] and Rashedi et al. [80] respectively.”
• A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms (2011, neutral): “…Karaboga 2009; Fei et al. 2009), Cuckoo Search Algorithm (Yang and Deb 2009; Deb et al. 2002), Gravitational Search Algorithm (Rashedi et al. 2009; Esmat et al. 2010, 2011; Duman et al. 2010; Chaoshun and Jianzhong 2011) Harmony Search Algorithm (Geem et al. 2001; Lee and Geem 2004, 2005; Mahdavi…”
• Binary grey wolf optimization approaches for feature selection (2016, methodology): “Furthermore, a binary version of the gravitational search algorithm (BGSA) is used for feature selection issue [17].”