A heuristic to determine the initial gravitational constant of the GSA
This work addresses the lack of generality in GSA for optimization tasks, though it appears incremental as it modifies an existing algorithm's parameter setting.
The paper tackles the problem of parameter sensitivity in the Gravitational Search Algorithm (GSA) by proposing a new heuristic, GSA-NGC, to determine the initial gravitational constant, which improves generality, performance, and efficiency across various applications.
The Gravitational Search Algorithm (GSA) is an optimization algorithm based on Newton's laws of gravity and dynamics. Introduced in 2009, the GSA already has several versions and applications. However, its performance depends on the values of its parameters, which are determined empirically. Hence, its generality is compromised, because the parameters that are suitable for a particular application are not necessarily suitable for another. This paper proposes the Gravitational Search Algorithm with Normalized Gravitational Constant (GSA-NGC), which defines a new heuristic to determine the initial gravitational constant of the GSA. The new heuristic is grounded in the Brans-Dicke theory of gravitation and takes into consideration the multiple dimensions of the search space of the application. It aims to improve the final solution and reduce the number of iterations and premature convergences of the GSA. The GSA-NGC is validated experimentally, proving to be suitable for various applications and improving significantly the generality, performance, and efficiency of the GSA.