GSA: A gravitational search algorithm. In recent years, various heuristic optimization methods have been developed. Many of these methods are inspired by swarm behaviors in nature. In this paper, a new optimization algorithm based on the law of gravity and mass interactions is introduced. In the proposed algorithm, the searcher agents are a collection of masses which interact with each other based on the Newtonian gravity and the laws of motion. The proposed method has been compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method in solving various nonlinear functions.

References in zbMATH (referenced in 129 articles , 1 standard article )

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  1. Douek-Pinkovich, Yifat; Ben-Gal, Irad; Raviv, Tal: The stochastic test collection problem: models, exact and heuristic solution approaches (2022)
  2. Kutlu Onay, Funda; Aydemir, Salih Berkan: Chaotic hunger games search optimization algorithm for global optimization and engineering problems (2022)
  3. Abualigah, Laith; Diabat, Ali; Mirjalili, Seyedali; Abd Elaziz, Mohamed; Gandomi, Amir H.: The arithmetic optimization algorithm (2021)
  4. Avalos, Omar: GSA for machine learning problems: a comprehensive overview (2021)
  5. Chou, Jui-Sheng; Truong, Dinh-Nhat: A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean (2021)
  6. Ren, Hao; Li, Jun; Chen, Huiling; Li, ChenYang: Adaptive Lévy-assisted salp swarm algorithm: analysis and optimization case studies (2021)
  7. Rodríguez, Alma; Camarena, Octavio; Cuevas, Erik; Aranguren, Itzel; Valdivia-G, Arturo; Morales-Castañeda, Bernardo; Zaldívar, Daniel; Pérez-Cisneros, Marco: Group-based synchronous-asynchronous grey wolf optimizer (2021)
  8. Tawhid, M. A.; Ibrahim, A. M.: Solving nonlinear systems and unconstrained optimization problems by hybridizing whale optimization algorithm and flower pollination algorithm (2021)
  9. Yan, Zheping; Zhang, Jinzhong; Zeng, Jia; Tang, Jialing: Nature-inspired approach: an enhanced whale optimization algorithm for global optimization (2021)
  10. Yu, Hang; Zhang, Yu; Cai, Pengxing; Yi, Junyan; Li, Sheng; Wang, Shi: Stochastic multiple chaotic local search-incorporated gradient-based optimizer (2021)
  11. Zhang, Sen; Zhou, Guo; Zhou, Yongquan; Luo, Qifang: Quantum-inspired satin bowerbird algorithm with Bloch spherical search for constrained structural optimization (2021)
  12. Ahmadianfar, Iman; Bozorg-Haddad, Omid; Chu, Xuefeng: Gradient-based optimizer: a new metaheuristic optimization algorithm (2020)
  13. Chen, Huiling; Wang, Mingjing; Zhao, Xuehua: A multi-strategy enhanced sine cosine algorithm for global optimization and constrained practical engineering problems (2020)
  14. Do, Quang Hung; Tuan, Tran Trong; Ha, Luu Thi Thu; Doan, Thi Thanh Hang; Nguyen, Thi van Anh; Tan, Le Thanh: Development of artificial neural networks trained by heuristic algorithms for prediction of exhaust emissions and performance of a diesel engine fuelled with biodiesel blends (2020)
  15. Ghasemian, Hadi; Ghasemian, Fahimeh; Vahdat-Nejad, Hamed: Human urbanization algorithm: a novel metaheuristic approach (2020)
  16. Giladi, Chen; Sintov, Avishai: Manifold learning for efficient gravitational search algorithm (2020)
  17. Jiang, Ruiye; Yang, Ming; Wang, Songyan; Chao, Tao: An improved whale optimization algorithm with armed force program and strategic adjustment (2020)
  18. Liu, Jingsen; Xing, Yuhao; Ma, Yixiang; Li, Yu: Gravitational search algorithm based on multiple adaptive constraint strategy (2020)
  19. Qu, Chiwen; He, Wei; Peng, Xiangni; Peng, Xiaoning: Harris Hawks optimization with information exchange (2020)
  20. Telikani, Akbar; Gandomi, Amir H.; Shahbahrami, Asadollah: A survey of evolutionary computation for association rule mining (2020)

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