BGSA: Binary gravitational search algorithm The gravitational search algorithm is a new optimization algorithm that is based on the law of gravity and mass interactions. In this algorithm, the search agents are a collection of masses, and their interactions are based on the Newtonian laws of gravity and motion. In this article, a binary version of the algorithm is introduced. To evaluate the performances of the proposed algorithm, several experiments are performed. The experimental results confirm the efficiency of the BGSA in solving various nonlinear benchmark functions

References in zbMATH (referenced in 20 articles , 1 standard article )

Showing results 1 to 20 of 20.
Sorted by year (citations)

  1. Douek-Pinkovich, Yifat; Ben-Gal, Irad; Raviv, Tal: The stochastic test collection problem: models, exact and heuristic solution approaches (2022)
  2. Vinod, Chandra S. S.; Anand, H. S.: Nature inspired meta heuristic algorithms for optimization problems (2022)
  3. Jiang, Yugui; Luo, Qifang; Wei, Yuanfei; Abualigah, Laith; Zhou, Yongquan: An efficient binary gradient-based optimizer for feature selection (2021)
  4. Slezkin, A. O.; Hodashinsky, I. A.; Shelupanov, A. A.: Binarization of the swallow swarm optimization for feature selection (2021)
  5. Ibrahim, Abdelmonem M.; Tawhid, M. A.; Ward, Rabab K.: A binary water wave optimization for feature selection (2020)
  6. Golzari, Shahram; Zardehsavar, Mohammad Nourmohammadi; Mousavi, Amin; Saybani, Mahmoud Reza; Khalili, Abdullah; Shamshirband, Shahaboddin: KGSA: a gravitational search algorithm for multimodal optimization based on K-means niching technique and a novel elitism strategy (2018)
  7. Aziz, Nor Azlina Ab.; Ibrahim, Zuwairie; Mubin, Marizan; Sudin, Shahdan: Adaptive switching gravitational search algorithm: an attempt to improve diversity of gravitational search algorithm through its iteration strategy (2017)
  8. Crawford, Broderick; Soto, Ricardo; Astorga, Gino; García, José; Castro, Carlos; Paredes, Fernando: Putting continuous metaheuristics to work in binary search spaces (2017)
  9. Pelusi, Danilo; Mascella, Raffaele; Tallini, Luca: Revised gravitational search algorithms based on evolutionary-fuzzy systems (2017)
  10. Obagbuwa, Ibidun Christiana; Abidoye, Ademola Philips: Binary cockroach swarm optimization for combinatorial optimization problem (2016)
  11. Razavi, Seyedeh Fatemeh; Sajedi, Hedieh: Cognitive discrete gravitational search algorithm for solving 0-1 knapsack problem (2015)
  12. Shams, Masumeh; Rashedi, Esmat; Hakimi, Ahmad: Clustered-gravitational search algorithm and its application in parameter optimization of a low noise amplifier (2015)
  13. Beheshti, Zahra; Hj. Shamsuddin, Siti Mariyam: CAPSO: centripetal accelerated particle swarm optimization (2014) ioport
  14. Deregeh, Fatemeh; Nezamabadi-Pour, Hossein: A new gravitational image edge detection method using edge explorer agents (2014) ioport
  15. Gao, Shangce; Vairappan, Catherine; Wang, Yan; Cao, Qiping; Tang, Zheng: Gravitational search algorithm combined with chaos for unconstrained numerical optimization (2014)
  16. David, Radu-Codruţ; Precup, Radu-Emil; Petriu, Emil M.; Rădac, Mircea-Bogdan; Preitl, Stefan: Gravitational search algorithm-based design of fuzzy control systems with a reduced parametric sensitivity (2013)
  17. Spichakova, Margarita: An approach to the inference of finite state machines based on a gravitationally-inspired search algorithm (2013)
  18. Gauci, Melvin; Dodd, Tony J.; Groß, Roderich: Why `GSA: a gravitational search algorithm’ is not genuinely based on the law of gravity (2012)
  19. Mirjalili, SeyedAli; Hashim, Siti Zaiton Mohd; Sardroudi, Hossein Moradian: Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm (2012)
  20. Rashedi, Esmat; Nezamabadi-pour, Hossein; Saryazdi, Saeid: BGSA: Binary gravitational search algorithm (2010)