Year: 2025
Type: article
Abstract: Global optimization helps determine a problem's optimal value. Exploration and exploitation are utilized in the optimization process to explore and refine the solution space. Intense exploitation accelerates the rate at which agents converge to the global optimum, whereas a strong exploration capability is necessary to navigate the multi-modal search spaces. Based on the idea of mass attraction, the Gravitational Search Algorithm (GSA) is a very promising heuristic algorithm that draws inspiration from physics. For real-world situations, it has been routinely employed to determine the global optima. Due to its inherent strong exploration capabilities and greater diversification power, GSA is less likely to experience local minima dropping. Additionally, GSA stores the best candidate solutions using an elitist approach. Thus, it shields optimal agents from interference from local minima. However, when tackling complex problems, GSA encounters obstacles such as premature convergence and entrapment in local minima.
DOI: 10.14419/298ffy09
Authors: Damanjeet Aulakh, Sudhanshu Dev, Anmol Yadav, Dr. Swarna Swetha Kolaventi, Dr. Narayan Patra, Dr. R. M. Joany, Vinay Kumar Sadolalu Boregowda
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4415735151
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